Land survey data quality evaluation method, device and equipment and storage medium

Abstract

The invention discloses a territorial survey data quality evaluation method, device and equipment and a storage medium, and relates to the technical field of geographic information.The territorial survey data of districts and counties are obtained, and statistics is conducted according to the territorial survey data to obtain statistics; dividing error categories in the land investigation data based on an expert knowledge base, and determining evaluation reference quantities of the error categories; obtaining a first class deduction score and a second class deduction score of the error class according to the evaluation reference quantity and the statistical quantity; and obtaining an evaluation score of each county according to the first-class deduction score and the second-class deduction score of the error class, and carrying out territorial survey data quality evaluation according to the evaluation score, thereby solving the problem of how to carry out territorial survey data quality evaluation under the condition of considering the error severity and the deduction limit. And objectivity and accuracy of data quality evaluation are improved.

Description

Technical Field

[0001] The present application relates to the technical field of geographic information, and particularly relates to a method, device, equipment and storage medium for evaluating the quality of land survey data. Background Technique

[0002] Land survey refers to the systematic and comprehensive investigation and evaluation of land resources, aiming to obtain information on aspects such as land cover, land use, land ownership, and land quality. These information together constitute land survey data, which is the basis for land resource management, urban and rural planning, environmental protection and other work. However, due to the complex formation process of land survey data and involving many factors, its quality is often difficult to guarantee. Therefore, it is particularly important to evaluate the quality of land survey data.

[0003] The core of the quality evaluation of land survey data lies in establishing a scientific, reasonable, and operable evaluation system. Traditional evaluation methods, such as the defect deduction method, although taking into account the severity of errors to some extent and having different deduction intensities for different defects, use a percentage-based deduction system and cannot fully meet the actual needs of land survey data quality evaluation. Specifically, the process of forming land survey data has the following characteristics, which pose higher requirements for evaluation methods: First, the costs of data improvement measures for different error types vary. This means that in the evaluation process, it is necessary to fully consider the severity of various errors, and the deduction intensity for each type of error should be different to reflect the differences in error types. However, although the traditional percentage-based deduction method has different deduction intensities for different defects, it fails to fully reflect this difference, resulting in inaccurate evaluation results. Second, the data is submitted and summarized by county or district. This means that in the evaluation process, it is necessary to consider the differences in data volume between different counties or districts to achieve unified evaluation between different counties or districts. The traditional percentage-based deduction method has obvious deficiencies in this regard. For example, when the data volumes of two counties or districts are significantly different, even if their error rates are the same, due to the same deduction base (i.e., the total score of the percentage system), the scores may vary greatly, thus unable to fairly reflect the data quality levels of the two counties or districts. Moreover, there are differences in the data volumes submitted between counties or districts. This characteristic further exacerbates the inapplicability of traditional evaluation methods. Due to different data volumes, even if the number of errors in two counties or districts is the same, their error rates may be very different. Therefore, in the evaluation process, it is necessary to fully consider the differences in data volume and adopt more reasonable evaluation indicators and calculation methods. Finally, after the data is submitted, it will go through multiple rounds of "inspection - improvement" cycles until it meets the standards. This means that in the evaluation process, it is necessary to fully consider the processuality and dynamics of data quality improvement. The traditional percentage-based deduction method often only focuses on the final score and ignores the process and effect of data quality improvement. Therefore, when establishing a new evaluation system, it is necessary to introduce indicators and methods that can reflect the process and effect of data quality improvement.

[0004] Therefore, how to evaluate the quality of land survey data while taking into account the severity of errors and the deduction limit is an urgent problem to be solved currently. Summary of the Invention

[0005] The main purpose of this application is to provide a method, device, equipment, and storage medium for evaluating the quality of land survey data, aiming to solve the technical problem of how to evaluate the quality of land survey data while taking into account the severity of errors and the deduction limit.

[0006] To achieve the above purpose, this application proposes a method for evaluating the quality of land survey data, and the method includes:

[0007] Obtain the land survey data of districts and counties, and perform statistics based on the land survey data to obtain statistics, where the statistics include the total number of objects, the number of objects involved in errors, the number of rule executions, and the cumulative number of error reports;

[0008] Divide the error categories in the land survey data based on the expert knowledge base, and determine the evaluation reference quantities for the error categories, where the evaluation reference quantities include the upper limit of the score range, the lower limit of the score range, the relative weight, the limit coefficient of the number of objects, and the fault tolerance ratio;

[0009] Obtain the first-class deductions and second-class deductions for the error categories according to the evaluation reference quantities and the statistics;

[0010] Obtain the evaluation scores of each district and county according to the first-class deductions and the second-class deductions of the error categories, and perform quality evaluation on the land survey data according to the evaluation scores.

[0011] In one embodiment, the step of dividing the error categories in the land survey data based on the expert knowledge base and determining the evaluation reference quantities for the error categories includes:

[0012] Based on the expert knowledge base, determine the influence degree of the error categories in the land survey data on the data quality, the lower limit and the upper limit of the score range of the error categories, the limit coefficient of the number of objects of the error categories, and the fault tolerance ratio;

[0013] Perform division based on the influence degree to obtain the error severity level and error categories, and assign relative weights to the error categories according to the error severity level.

[0014] In one embodiment, the step of performing division based on the influence degree to obtain the error categories includes:

[0015] Obtain the preset complexity and workload for repairing the error categories;

[0016] Perform division on the error categories according to the influence degree to obtain the main categories, where the main categories include those affecting the integrity of the results, those affecting the summary statistics, and those affecting the data application;

[0017] Perform division on the main categories according to the complexity and the workload to obtain the sub-categories, where the sub-categories include those requiring return for modification, those requiring individual modification, and those requiring batch modification.

[0018] In one embodiment, the step of obtaining the first-class deductions and second-class deductions for the error categories according to the evaluation reference quantities and the statistics includes:

[0019] A process quantity is obtained based on the evaluation reference quantity and the statistic quantity. The process quantity includes an upper limit of deductions, a lower limit of deductions, a difference between the upper and lower deduction ranges, a first type of limit value, a first type of deduction limit value, a second type of deduction limit value, a first type of deduction proportion coefficient, and a second type of deduction proportion coefficient;

[0020] A first type of deduction and a second type of deduction for the error category are obtained based on the process quantity and the statistic quantity.

[0021] In one embodiment, the step of obtaining the process quantity based on the evaluation reference quantity and the statistic quantity includes:

[0022] The upper limit of deductions and the lower limit of deductions are obtained based on the lower limit of the score range and the upper limit of the score range;

[0023] The difference between the upper and lower deduction ranges is obtained based on the upper limit of deductions and the lower limit of deductions;

[0024] The first type of limit value is obtained based on the limit value coefficient of the number of objects and the total number of objects;

[0025] The second type of deduction limit value is obtained based on the relative weight;

[0026] The first type of deduction limit value is obtained based on the upper limit of deductions and the second type of deduction limit value;

[0027] The first type of deduction proportion coefficient and the second type of deduction proportion coefficient are obtained based on the first type of deduction limit value and the second type of deduction limit value.

[0028] In one embodiment, the step of obtaining the first type of deduction and the second type of deduction for the error category based on the process quantity and the statistic quantity includes:

[0029] Obtain a predefined first deduction function and a second deduction function;

[0030] A first type of conversion quantity is obtained based on the first type of limit value and the number of objects involved in the error;

[0031] An error ratio is obtained based on the cumulative number of error reports and the number of rule executions;

[0032] The first type of deduction and the second type of deduction for the error category are obtained based on the process quantity, the statistic quantity, the first deduction function, the second deduction function, the first type of conversion quantity, and the error ratio.

[0033] In one embodiment, the step of obtaining the first type of deduction and the second type of deduction for the error category based on the process quantity, the statistic quantity, the first deduction function, the second deduction function, the first type of conversion quantity, and the error ratio includes:

[0034] Obtain the first - type deduction points based on the first deduction function, the second deduction function, the lower limit of deductions, the first - type deduction proportion coefficient, the first - type conversion quantity, and the difference between the upper and lower deduction ranges;

[0035] Obtain the second - type deduction points based on the first deduction function, the lower limit of deductions, the second - type deduction proportion coefficient, the error ratio, and the error - tolerance ratio.

[0036] In addition, to achieve the above - mentioned purpose, the present application also proposes a device for evaluating the quality of land survey data. The device includes:

[0037] A data extraction module, configured to obtain the land survey data of a district or county, and perform statistics based on the land survey data to obtain statistics, where the statistics include the total number of objects, the number of objects involved in errors, the number of rule executions, and the cumulative number of error reports;

[0038] A data division module, configured to divide the error categories in the land survey data based on an expert knowledge base, and determine the evaluation reference quantities for the error categories. The evaluation reference quantities include the upper limit of the score range, the lower limit of the score range, the relative weight, the limit coefficient of the number of objects, and the error - tolerance ratio;

[0039] A data processing module, configured to obtain the first - type deduction points and the second - type deduction points for the error categories based on the evaluation reference quantities and the statistics;

[0040] A quality evaluation module, configured to obtain the evaluation scores of each district or county based on the first - type deduction points and the second - type deduction points of the error categories, and perform quality evaluation of the land survey data based on the evaluation scores.

[0041] In addition, to achieve the above - mentioned purpose, the present application also proposes a device for evaluating the quality of land survey data. The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor. The computer program is configured to implement the steps of the method for evaluating the quality of land survey data as described above.

[0042] In addition, to achieve the above - mentioned purpose, the present application also proposes a storage medium. The storage medium is a computer - readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for evaluating the quality of land survey data as described above.

[0043] In addition, to achieve the above - mentioned purpose, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the method for evaluating the quality of land survey data as described above.

[0044] The present application provides a method for evaluating the quality of land survey data. The method of the present application includes: obtaining the land survey data of a district or county, and obtaining a statistic according to the land survey data through statistics; dividing the error categories in the land survey data based on an expert knowledge base, and determining the evaluation reference quantity of the error categories; obtaining the first-class deductions and the second-class deductions of the error categories according to the evaluation reference quantity and the statistic; obtaining the evaluation scores of each district or county according to the first-class deductions and the second-class deductions of the error categories, and performing a quality evaluation of the land survey data according to the evaluation scores. In summary, through the quality evaluation method that takes into account both the severity of errors and the deduction limit, the present application realizes the horizontal and vertical comparison of different data, solves the problem of how to evaluate the quality of land survey data under the condition of taking into account both the severity of errors and the deduction limit, and improves the objectivity and accuracy of data quality evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments that conform to the present application, and are used together with the specification to explain the principles of the present application.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0047] Figure 1 It is a schematic flowchart provided for the first embodiment of the method for evaluating the quality of land survey data of the present application;

[0048] Figure 2 It is a schematic diagram of an evaluation reference quantity table in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0049] Figure 3 It is a schematic diagram of an error classification table in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0050] Figure 4 It is a schematic diagram of an evaluation score table for each district or county in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0051] Figure 5 It is a schematic diagram of a quality inspection form for the data submitted three times by County A in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0052] Figure 6 It is a schematic flowchart provided for the second embodiment of the method for evaluating the quality of land survey data of the present application;

[0053] Figure 7Schematic diagram of the statistical scale in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0054] Figure 8 Schematic diagram of the county-level public process scale in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0055] Figure 9 Schematic diagram of the county-level different process scale in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0056] Figure 10 Schematic diagram of the deduction function table in an embodiment of the method for evaluating the quality of land survey data of the present application;

[0057] Figure 11 Schematic diagram of the module structure of the land survey data quality evaluation device in an embodiment of the present application;

[0058] Figure 12 Schematic diagram of the device structure of the hardware operating environment involved in the method for evaluating the quality of land survey data in an embodiment of the present application.

[0059] The realization of the purpose, functional features, and advantages of the present application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0060] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0061] To better understand the technical solutions of the present application, the following will be described in detail in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0062] The main solution of the embodiment of the present application is: obtaining the land survey data of the county, and obtaining the statistic according to the land survey data; dividing the error categories in the land survey data based on the expert knowledge base, and determining the evaluation reference amount of the error categories; obtaining the first-class deduction and the second-class deduction of the error categories according to the evaluation reference amount and the statistic; obtaining the evaluation scores of each county according to the first-class deduction and the second-class deduction of the error categories, and performing the quality evaluation of the land survey data according to the evaluation scores.

[0063] Land survey refers to the systematic and comprehensive investigation and evaluation of land resources, aiming to obtain information on land cover, land use, land ownership, and land quality. These information together constitute the land survey data, which is the basis for land resource management, urban and rural planning, environmental protection and other work. However, due to the complex formation process of land survey data and involving many factors, its quality is often difficult to guarantee. Therefore, it is particularly important to evaluate the quality of land survey data.

[0064] The core of the quality evaluation of land survey data lies in establishing a set of scientific, reasonable and operable evaluation systems. Traditional evaluation methods, such as the defect deduction method, although to a certain extent take into account the severity of errors and have different deduction intensities for different defects, this method uses a percentage-based deduction system and cannot fully meet the actual needs of the quality evaluation of land survey data. Specifically, the formation process of land survey data has the following characteristics, which put forward higher requirements for the evaluation method: First, the costs of data improvement measures for different error types are different. This means that in the evaluation process, it is necessary to fully consider the severity of various errors, and the deduction intensity for each type of error should be different to reflect the differences in error types. However, although the traditional percentage-based deduction method has different deduction intensities for different defects, it fails to fully reflect this difference, resulting in inaccurate evaluation results. Second, the data is submitted and summarized by county. This means that in the evaluation process, it is necessary to consider the differences in data volumes between different counties to achieve unified evaluation between different counties. The traditional percentage-based deduction method has obvious deficiencies in this regard. For example, when the data volumes of two counties are very different, even if their error rates are the same, due to the same deduction base (i.e., the total score of the percentage system), the scores may vary greatly, thus unable to fairly reflect the data quality levels of the two counties. Moreover, there are differences in the data volumes submitted between counties. This characteristic further exacerbates the inapplicability of traditional evaluation methods. Due to different data volumes, even if the number of errors in two counties is the same, their error rates may be very different. Therefore, in the evaluation process, it is necessary to fully consider the differences in data volumes and adopt more reasonable evaluation indicators and calculation methods. Finally, after the data is submitted, it will go through multiple rounds of "inspection - improvement" cycles until it meets the standards. This means that in the evaluation process, it is necessary to fully consider the processuality and dynamics of data quality improvement. The traditional percentage-based deduction method often only focuses on the final score and ignores the process and effect of data quality improvement. Therefore, when establishing a new evaluation system, it is necessary to introduce indicators and methods that can reflect the process and effect of data quality improvement. Therefore, how to conduct the quality evaluation of land survey data while taking into account the severity of errors and the deduction limit is an urgent problem to be solved at present.

[0065] This application realizes the horizontal and vertical comparison of different data through a quality evaluation method that takes into account the severity of errors and the deduction limit, solves the problem of how to conduct the quality evaluation of land survey data while taking into account the severity of errors and the deduction limit, and improves the objectivity and accuracy of data quality evaluation.

[0066] It should be noted that the execution subject of this embodiment can be a national land survey data quality evaluation system, or a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of implementing the above-mentioned national land survey data quality evaluation function. This embodiment does not specifically limit this. Hereinafter, taking the national land survey data quality evaluation system as an example, this embodiment and the following embodiments will be described.

[0067] Based on this, the embodiment of the present application provides a method for evaluating the quality of national land survey data. Refer to Figure 1 , Figure 1 which is a schematic flowchart of the first embodiment of the method for evaluating the quality of national land survey data of the present application.

[0068] In this embodiment, the method for evaluating the quality of national land survey data includes steps S10 to S40:

[0069] Step S10: Obtain the national land survey data of the district or county, and perform statistics based on the national land survey data to obtain statistical quantities, where the statistical quantities include the total number of objects, the number of objects involved in errors, the number of rule executions, and the cumulative number of error reports.

[0070] It should be noted that in this step, the system will batch import the national land survey data of the district or county through the data import module. These data cover multiple aspects such as land use, land cover, land ownership, and land quality. Subsequently, using the verification and statistics module, statistical analysis is performed on the data of each district or county based on preset rules to obtain a series of data statistical quantities. These statistical quantities specifically include: the total number of objects X ij : refers to the total number of objects for a certain type of error in each county, that is, the total number of objects included in the data to be inspected. The number of objects involved in errors x ij : refers to the number of objects where a certain type of error actually occurs. For different rules, the granularity of the objects is different. Layers, elements, reports, and records are all objects, and in different error categories, the objects may be repeated. The number of rule executions: refers to the number of times the detection rule is applied M ij , which is used to evaluate the comprehensiveness and efficiency of the detection. The cumulative number of error reports m ij : refers to the cumulative number of times an error is identified in a certain type of error, which is used to represent the frequency of the error. It can be understood that among them, i is the error code of different error categories, and j is the district or county number of different districts or counties.

[0071] Step S20: Divide the error categories in the national land survey data based on the expert knowledge base, and determine the evaluation reference quantities for the error categories. The evaluation reference quantities include the upper limit of the score range, the lower limit of the score range, the relative weight, the object number limit coefficient, and the fault tolerance ratio.

[0072] It should be noted that in this step, the system will use the industry experience and professional knowledge in the expert knowledge base to carefully classify the errors in the land survey data. These error categories are classified based on the influencing ability, processing complexity, required workload, etc. of different error types on the data. At the same time, the system will sort and code the errors according to the severity of the errors. It can be understood that the errors refer to the computational contradictions and conflicts generated based on the specified verification rules, and can be presented through the system provided in this application. The verification rules refer to the calculation methods that can make reasonable judgments according to the industry knowledge of the land survey data. The rules can be formulas or logical calculations, and can refer to the industry specifications for land survey data quality inspection. The objects involved in the errors refer to the input elements that are required to be inserted into the rules for calculation. When the data quality of the input elements meets the requirements, there will be an expected output; when the data quality of the input elements does not meet the requirements, resulting in the inability to achieve the expected output, the relevant input elements will be marked as the objects involved in the errors by the computational software tool. For different rules, the granularity of the objects is different, and the objects include but are not limited to layers, elements, reports, records, etc.

[0073] In addition, it should be noted that as Figure 2 shown, for each error category, the system will pre-determine a series of evaluation reference quantities for subsequent deduction calculation. The evaluation reference quantities include: the lower limit a of the score range i , the upper limit b of the score range i , the relative weight σ i , the limit value coefficient m of the number of objects i and the fault tolerance ratio

[0074] In a feasible implementation manner, the step S20 specifically includes:

[0075] Step S201: Based on the expert knowledge base, determine the influence degree of the error category on the data quality in the land survey data, the lower limit and upper limit of the score range of the error category, the limit value coefficient of the number of objects of the error category, and the fault tolerance ratio.

[0076] It should be noted that in this step, the system will evaluate the influence degree of various error categories based on the industry historical experience and professional knowledge in the expert knowledge base. The evaluation content covers the influence degree of the error category on data integrity, summary statistical accuracy, and data application effect. It can be understood that the expert knowledge base is a dynamically updated database. With the continuous update of industry specifications and data quality evaluation standards, as well as the research and discussion of new problems by the expert team, the classification of error categories and the setting of evaluation reference quantities will also be continuously improved and optimized.

[0077] Additionally, it should be noted that the lower limit a of the score range i and the upper limit b i refer to the highest and lowest possible scores of this error category in the evaluation, providing a boundary for the deduction calculation. The relative weight σ i refers to the difference in importance of different error categories in the overall evaluation, and is used to adjust the influence degree of each category on the final score. The object number limit coefficient μ i refers to when the number of objects involved in the error exceeds a certain limit value, this coefficient is used to adjust the deduction intensity to reflect the extremity of the error scale. The fault tolerance ratio refers to the upper limit of the allowable error ratio of a certain type, and is used to distinguish tolerable errors from errors that require strict deductions.

[0078] Step S202: Based on the influence degree, divide to obtain the error severity level and error category, and assign relative weights to the error category according to the error severity level.

[0079] It should be noted that according to the influence degree of each error category on the data quality determined in step S201, the errors are divided into different severity levels. The division of the severity level can be comprehensively considered based on the influence degree of the error on data integrity, summary statistics accuracy, and data application effect. For example, the errors can be divided into three levels: minor, general, and severe. Then, relative weights are assigned to each error category according to the error severity level. The assignment of relative weights should reflect the differences in the influence of different error categories on the data quality. The larger the weight, the greater the influence of this error category on the data quality, and more attention and handling should be given during the evaluation process. The assignment of weights can be determined through expert scoring, the Delphi method, or other statistical methods.

[0080] In a feasible implementation manner, the step S202 specifically includes:

[0081] Step A10: Obtain the preset complexity and workload for repairing the error category.

[0082] It should be noted that obtaining the preset complexity and workload for repairing the error category is an important prerequisite for evaluating the quality of land survey data. This step mainly analyzes various errors to determine the technical means, human input, and time cost required to repair these errors. Additionally, it should be noted that complexity and workload are relative concepts, which depend on various factors such as the specific type of error and the volume of data.

[0083] Step A20: Divide the error category according to the influence degree to obtain the main categories, and the main categories include those affecting the integrity of the results, those affecting the summary statistics, and those affecting the data application.

[0084] It should be noted that in this step, the system will classify the error categories into three main categories according to the impact degree of the error on the land survey data, namely, the categories affecting the integrity of the results, the categories affecting the summary statistics, and the categories affecting the data application. For example, during the land survey, if it is found that the report data is missing in the data of a certain district or county, this will directly affect the integrity of the results, so this type of error can be classified into the category affecting the integrity of the results. If there are obvious deviations in the summary statistics of the data of a certain district or county, such as the area field not conforming to the value range, this will affect the accuracy and reliability of the data, so this type of error can be classified into the category affecting the summary statistics. In addition, if there are problems such as violating topological consistency, mapping standardization, or within-genus consistency during the data application process, this will affect the use effect and value of the data, so this type of error can be classified into the category affecting the data application.

[0085] Step A30: Divide the main categories according to the complexity and the workload to obtain sub-categories, and the sub-categories include the return for modification category, the individual modification category, and the batch modification category.

[0086] It should be noted that, for example, there are existing districts or counties a, b, and c, and the data volumes obtained in the land survey work are different. The order of the data volumes of the three is b < a << c (County b is smaller than County a, and County a is much smaller than County c). The supervision department needs to conduct a unified evaluation on them, that is, complete the horizontal comparison of the three districts or counties. Specifically, as Figure 3 shown, the system will further divide the above main categories into three sub-categories: the return for modification category, the individual modification category, and the batch modification category according to the complexity and workload of fixing the errors. For example, for errors affecting the integrity of the results, such as the missing of mandatory files, layers, and reports, it is usually necessary to return to the local area for modification, and its complexity and workload are relatively high. This type of error is classified into the return for modification category of errors. For errors affecting the summary statistics, such as the area field not conforming to the value range, the cultivated land slope grade not filled in for the cultivated land, etc., they can be individually modified on the basis of the existing data through technical means, and their complexity and workload are relatively low. This type of error is classified into the individual modification category of errors. In addition, for errors affecting the data application, such as violating topological consistency, mapping standardization, within-genus consistency, etc., they can be batch-modified by writing scripts or programs. This type of error is classified into the batch modification category of errors.

[0087] Step S30: Obtain the first-class deduction and the second-class deduction of the error category according to the evaluation reference quantity and the statistic quantity.

[0088] It should be noted that the first type of deduction refers to the deduction due to the number of objects involved in the error, and the second type of deduction refers to the deduction due to the error ratio. In this step, the first type of deduction refers to the deduction based on the actual number of error instances of a certain error type in the land survey data. Specifically, the more the number of data objects (such as layers, elements, reports, etc.) involved in a certain type of error, the more significant the deduction. The deduction logic is based on a preset dynamic threshold (such as the limit value of the number of objects related to the data volume). When the number of errors approaches or exceeds this threshold, the deduction amplitude will increase non-linearly to reflect the high sensitivity to serious errors and the mandatory constraint on the tolerance limit. The second type of deduction refers to the deduction based on the occurrence frequency or proportion of a certain type of error in the overall data. Specifically, when the ratio of the occurrence times of a certain type of error to the total number of rule validations exceeds the preset fault tolerance threshold, the maximum deduction will be triggered; if it is lower than the threshold, the deduction will increase proportionally. This deduction method aims to reflect the universality of errors rather than simply relying on the absolute quantity, so as to avoid evaluation biases caused by differences in data volume. At the same time, the non-linear deduction mechanism is used to encourage the priority solution of high-frequency errors.

[0089] Step S40: Obtain the evaluation scores of each district or county according to the first type of deduction and the second type of deduction of the error category, and conduct the quality evaluation of the land survey data according to the evaluation scores.

[0090] It should be noted that as Figure 4 shown, the evaluation scores of each district or county are obtained by comprehensively considering the first type of deduction and the second type of deduction of all error categories, and the quality evaluation of the land survey data is conducted according to the evaluation scores. In this embodiment, the system will calculate the total deduction of each district or county according to the first type of deduction and the second type of deduction of various errors. Then, subtract the total deduction from the upper limit of the score range to obtain the final evaluation score of each district or county. The evaluation score reflects the quality level of the land survey data of each district or county. The system will rank each district or county according to the evaluation scores and conduct horizontal and vertical comparisons to intuitively show the advantages and disadvantages of the data quality. In addition, the system will also analyze the efforts and effects of each district or county in improving the data quality according to the changes in the evaluation scores. For example, if the score of a certain district or county continuously increases in several consecutive evaluations, it indicates that the district or county has made effective improvements in data quality.

[0091] It can be understood that assume there is an existing district or county a. When the land survey data is submitted for the first time and requires to be returned for improvement after inspection, after County a completes the improvement and submits it for the second time, it still needs to be further improved after inspection, and then continues to be submitted after the second improvement. In the case of adopting this embodiment, by Figure 5It can be seen that the main errors affecting the data quality of the land survey data (a1) first submitted by County A are the 6th and 8th types of errors. After the first inspection, County A improved the 6th type of error, and the quality of the data (a2) submitted again has been improved, but it did not reach the expected quality standard. After the second inspection of the data is returned, County A continues to improve the 6th type of error. From Figure 5 It can be seen that while County A continuously reduces the 6th type of error, the next step can focus on improving the 8th type of error. The 8th type of error will be the bottleneck for County A to improve the score in the next stage. It can help County A break through the score bottleneck and significantly improve the score. The evaluation score of this embodiment not only considers the severity of the error, but also considers the unity among districts and counties with different data volumes and the tolerance limit of errors within the same district or county. Such evaluation results are more objective and fair, and can more effectively drive each district or county to improve the data quality.

[0092] This embodiment provides a method for evaluating the quality of land survey data. The method of this embodiment includes: obtaining the land survey data of a district or county, and obtaining a statistic based on the land survey data; dividing the error categories in the land survey data based on an expert knowledge base, and determining the evaluation reference quantity of the error categories; obtaining the first-class deduction and the second-class deduction of the error categories according to the evaluation reference quantity and the statistic; obtaining the evaluation score of each district or county according to the first-class deduction and the second-class deduction of the error categories, and evaluating the quality of the land survey data according to the evaluation score. In summary, through the quality evaluation method that takes into account both the severity of the error and the deduction limit, this embodiment realizes the horizontal and vertical comparison of different data, solves the problem of how to evaluate the quality of land survey data under the condition of taking into account both the severity of the error and the deduction limit, and improves the objectivity and accuracy of the data quality evaluation.

[0093] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as in the above-mentioned embodiment one can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 6 , Figure 6 is a schematic flowchart of the second embodiment of the method for evaluating the quality of land survey data of the present application. The specific steps of step S30 include:

[0094] Step S301: Obtain a process quantity according to the evaluation reference quantity and the statistic. The process quantity includes the upper limit of deduction, the lower limit of deduction, the difference between the upper and lower deduction ranges, the first-class limit value, the first-class deduction limit value, the second-class deduction limit value, the first-class deduction proportion coefficient, and the second-class deduction proportion coefficient.

[0095] It should be noted that as Figure 7 shown, in this step, the system will extract the statistics of each district or county for various types of errors, including the total number of objects X ij 、the number of objects involved in the error xij 、The number of rule executions M ij 、The cumulative number of error reports m ij and the error ratio ε ij . Then, based on these statistics and the pre-determined evaluation reference quantities (such as the limit value coefficient of the number of objects involved in errors, the allowable error ratio, etc.), various process quantities are calculated through a preset logical formula. It can be understood that the first category refers to the category of the number of objects involved in errors, and the second category refers to the category of the error ratio.

[0096] In addition, it should be noted that as Figure 8 and Figure 9 shown, the process quantity refers to the preparation quantity before calculating the final score for type i errors in county j, including the limit value of the number of objects involved in errors (the first category limit value) N ij for type i errors in county j, the upper limit of score deduction A i and the lower limit of score deduction B i , the difference Δ between the upper and lower deduction ranges i , and the limit value of score deduction due to the number of objects involved in errors (the first category score deduction limit value) α i , the limit value of score deduction due to the error ratio (the second category score deduction limit value) β i , the proportion coefficient of score deduction due to the number of objects involved in errors (the first category score deduction proportion coefficient) γ i , the proportion coefficient of score deduction due to the error ratio (the second category score deduction proportion coefficient)

[0097] In a feasible implementation manner, the step S301 specifically includes:

[0098] Step B10: Obtain the upper limit and the lower limit of score deduction according to the lower limit and the upper limit of the score interval.

[0099] It should be noted that specifically, the calculation of the upper limit of score deduction is shown in Formula 1:

[0100] A i = 100 - a i (Formula 1)

[0101] where A i is the upper limit of score deduction for type i errors, and a i is the lower limit of the score interval. The calculation of the lower limit of score deduction is shown in Formula 2:

[0102] B i = 100 - b i (Formula 2)

[0103] where B i is the lower limit of score deduction for type i errors, and b iis the upper limit of the score range.

[0104] Step B20: Obtain the difference between the upper and lower deduction ranges based on the upper deduction limit and the lower deduction limit.

[0105] It should be noted that specifically, the calculation of the difference between the upper and lower deduction ranges is shown in Formula 3:

[0106] Δ i = A i - B i (Formula 3)

[0107] where Δ i is the difference between the upper and lower deduction ranges for type-i errors.

[0108] Step B30: Obtain a first type of limit value based on the limit value coefficient of the number of objects and the total number of objects.

[0109] It should be noted that specifically, the calculation of the limit value of the number of objects involved in the error (the first type of limit value) is shown in Formula 4:

[0110] N ij = v i * X ij (Formula 4)

[0111] where N ij is the limit value of the number of objects involved in the error for type-i errors in county j, v i is the limit value coefficient of the number of objects involved in the error, and X ij is the total number of objects used for rule verification for type-i errors in county j.

[0112] In addition, it should be noted that the limit value of the number of objects involved in the error (the first type of limit value) N ij , means that when the number of objects involved in the error x ij exceeds this value, the deduction for type-i errors due to the number of objects involved in the error is equal to 95% of (the first type of deduction limit value) α i . The limit value coefficient μ i of the number of objects involved in the error is mainly used to calculate the limit value N ij of the number of objects involved in the error for type-i errors in county j. However, for certain types of errors, the limit value N ij of the number of objects involved in the error for type-i errors in county j can also be set as a fixed value without going through the above conversion.

[0113] Step B40: Obtain a second type of deduction limit value based on the relative weight.

[0114] It should be noted that specifically, the calculation of the limit value of the deduction due to the error ratio (the second type of deduction limit value) is shown in Formula 5:

[0115]

[0116] Among them, β i is the limit value for deducting points due to the error ratio, and s is the number of error types corresponding to all error types.

[0117] Step B50: Obtain the first-class deduction limit value according to the upper limit of deduction and the second-class deduction limit value.

[0118] It should be noted that specifically, the limit value for deducting points due to the number of objects involved in the error (the first-class deduction limit value) is shown in Formula 6:

[0119] α i = A i - β i (Formula 6)

[0120] Among them, α i is the limit value for deducting points due to the number of objects involved in the error.

[0121] Step B60: Obtain the first-class deduction proportion coefficient and the second-class deduction proportion coefficient according to the first-class deduction limit value and the second-class deduction limit value.

[0122] It should be noted that specifically, the calculation of the first-class deduction proportion coefficient and the second-class deduction proportion coefficient is shown in Formulas 7 and 8:

[0123] γ i = α i / (α i + β i ) (Formula 7)

[0124]

[0125] Among them, γ i is the proportion coefficient for deducting points due to the number of objects involved in the error (i.e., the first-class deduction proportion coefficient), is the proportion coefficient for deducting points due to the error ratio (i.e., the second-class deduction proportion coefficient).

[0126] Step S302: Obtain the first-class deduction and the second-class deduction of the error category according to the process quantity and the statistical quantity.

[0127] It should be noted that the first type of deduction refers to the deduction caused by the number of objects involved in the error, and the second type of deduction refers to the deduction caused by the error ratio. Specifically, in this step, the system will calculate the first type of deduction through a preset formula based on the number of objects involved in the error, the proportion coefficient of the first type of deduction, and process quantities such as the deduction upper limit, deduction lower limit, and the difference between the deduction increase and decrease amplitudes. Then, the error ratio is calculated based on the cumulative number of error reports and the number of rule executions, and combined with process quantities such as the proportion coefficient of the second type of deduction and the limit value of the second type of deduction, the second type of deduction is calculated through a preset formula.

[0128] In a feasible implementation manner, step S302 specifically includes:

[0129] Step C10: Obtain a predefined first deduction function and a second deduction function.

[0130] It should be noted that, as Figure 10 shown, in this step, the system will obtain a predefined first deduction function and a second deduction function from the configuration file. Specifically, the first deduction function is as shown in Formula 9:

[0131]

[0132] where x is the function independent variable and x ≥ 0; this function can be obtained by shifting the sigmoid function downward by 0.5 and stretching it vertically by a factor of 2, and only taking the first quadrant.

[0133] Specifically, the second deduction function custom(x) is as shown in Formula 10:

[0134]

[0135] It can be understood that the first deduction function is a non-linear function used to dynamically adjust the deduction amplitude according to the standardized value of the number of objects involved in the error; the second deduction function is a piecewise function used to determine whether an error exists and trigger a basic deduction.

[0136] Step C20: Obtain a first type of conversion quantity based on the first type of limit value and the number of objects involved in the error.

[0137] It should be noted that the first type of limit value refers to the limit value N of the number of objects involved in the error ij , and the first type of conversion quantity refers to the standardized number of objects involved in the error. It can be understood that, due to therefore, combining the limit deduction idea, the number of objects involved in the error x ij is converted into The conversion relationship is: where x ij is the number of objects involved in the error, is the conversion quantity of the number of objects involved in the error.

[0138] Step C30: Obtain the error ratio based on the cumulative number of error reports and the number of rule executions.

[0139] It should be noted that the error ratio ε ij refers to, for a certain county regarding a certain type of error, the cumulative number of error reports m ij and the number of rule executions M ij The ratio, that is, ε ij = m ij / M ij . If M ij = 0 (that is, the rule verification is not performed), then by default, ε ij = 0.

[0140] It can be understood that the error ratio can reflect the universality of errors and avoid the evaluation distortion caused by relying on absolute quantities. Combining with the fault tolerance ratio it can be determined whether the maximum deduction is triggered.

[0141] Step C40: Obtain the first-class deduction and the second-class deduction of the error category based on the process quantity, the statistical quantity, the first deduction function, the second deduction function, the first-class conversion quantity, and the error ratio.

[0142] It should be noted that in this step, the system will calculate the first-class deduction and the second-class deduction for each district and county regarding various types of errors respectively through a preset calculation formula according to the obtained first deduction function, second deduction function, first-class conversion quantity, error ratio, and other relevant parameters (such as the deduction upper limit, deduction lower limit, relative weight, etc. for various types of errors). Then, the final scores of each district and county are calculated and summarized based on the first-class deduction and the second-class deduction.

[0143] In a feasible implementation manner, the step C40 specifically includes:

[0144] Step C401: Obtain the first-class deduction based on the first deduction function, the second deduction function, the deduction lower limit, the first-class deduction proportion coefficient, the first-class conversion quantity, and the deduction upper and lower amplitude difference.

[0145] It should be noted that the process of calculating the deduction due to the number of objects involved in the error (that is, the first-class deduction) is specifically shown in Formula 11:

[0146]

[0147] Among them, i is the error code, j is the district and county number, is the deduction generated by the number of objects involved in the error for County j regarding Error i, B iLower limit of deduction for type I error, Δ i Range difference of deduction, γ i Proportion coefficient for deduction due to the number of objects involved in the error (i.e., proportion coefficient for type I deduction), x ij Number of objects involved in the error Conversion quantity of the number of objects involved in the error (i.e., type I conversion quantity) Is the first deduction function, custom(x) is the second deduction function, N ij Limit value of the number of objects involved in the error

[0148] Step C402: Obtain the type II deduction according to the first deduction function, the deduction lower limit, the type II deduction proportion coefficient, the error ratio, and the error tolerance ratio

[0149] It should be noted that the process of calculating the deduction due to the error ratio (i.e., type II deduction) is specifically shown in Formula 12

[0150]

[0151] Among them Is the deduction for county j due to the error ratio for type I error Proportion coefficient for deduction due to error ratio, ε ij Error ratio Is the error tolerance ratio. Further, the system will calculate the final evaluation score Score_j of county j, that is

[0152] In this embodiment, by considering the severity of the error and setting the upper and lower limits of deduction, etc., the rationality of the deduction is realized. Through the standardized scoring method, the effect of data quality improvement is objectively reflected, enabling unified horizontal comparison between districts and counties with different data volumes. The error tolerance limit is fully considered, so that the score can objectively reflect the actual efforts and improvement effects made by the same district and county to improve data quality

[0153] This application also provides a device for evaluating the quality of land survey data. Please refer to Figure 11 The device for evaluating the quality of land survey data includes

[0154] Data extraction module 10, which is used to obtain the land survey data of the district and county, and perform statistics according to the land survey data to obtain statistical quantities, where the statistical quantities include the total number of objects, the number of objects involved in the error, the number of rule executions, and the cumulative number of error reports

[0155] The data division module 20 is used to divide the error categories in the land survey data based on the expert knowledge base and determine the evaluation reference quantities for the error categories. The evaluation reference quantities include the upper limit of the score range, the lower limit of the score range, the relative weight, the object number limit coefficient, and the fault tolerance ratio.

[0156] The data processing module 30 is used to obtain the first-class deductions and the second-class deductions for the error categories according to the evaluation reference quantities and the statistics.

[0157] The quality evaluation module 40 is used to obtain the evaluation scores of each district and county according to the first-class deductions and the second-class deductions of the error categories, and conduct the quality evaluation of the land survey data according to the evaluation scores.

[0158] The land survey data quality evaluation device provided by this application adopts the land survey data quality evaluation method in the above embodiment, and can solve the technical problem of how to conduct the quality evaluation of land survey data under the condition of taking into account the severity of errors and the deduction limit. Compared with the prior art, the beneficial effects of the land survey data quality evaluation device provided by this application are the same as those of the land survey data quality evaluation method provided by the above embodiment, and other technical features in the land survey data quality evaluation device are the same as the features disclosed in the method of the above embodiment, and will not be elaborated here.

[0159] In one embodiment, the data division module 20 is further used to determine the influence degree of the error categories in the land survey data on the data quality, the lower limit and the upper limit of the score range of the error categories, the object number limit coefficient of the error categories, and the fault tolerance ratio based on the expert knowledge base; divide the error severity level and the error categories based on the influence degree, and assign relative weights to the error categories according to the error severity level.

[0160] In one embodiment, the data division module 20 is further used to obtain the preset complexity and workload for repairing the error categories; divide the error categories according to the influence degree to obtain the main categories, and the main categories include those affecting the integrity of the results, those affecting the summary statistics, and those affecting the data application; divide the main categories according to the complexity and the workload to obtain the sub-categories, and the sub-categories include those for returning and modifying, those for modifying one by one, and those for batch modification.

[0161] In one embodiment, the data processing module 30 is further used to obtain the process quantities according to the evaluation reference quantities and the statistics. The process quantities include the upper limit of deductions, the lower limit of deductions, the difference between the upper and lower deduction ranges, the first-class limit value, the first-class deduction limit value, the second-class deduction limit value, the first-class deduction proportion coefficient, and the second-class deduction proportion coefficient; obtain the first-class deductions and the second-class deductions for the error categories according to the process quantities and the statistics.

[0162] In one embodiment, the data processing module 30 is further configured to obtain an upper limit of deductions and a lower limit of deductions based on the lower limit of the score range and the upper limit of the score range; obtain a difference between the upper and lower deduction ranges based on the upper limit of deductions and the lower limit of deductions; obtain a first type of limit value based on the object number limit coefficient and the total number of objects; obtain a second type of deduction limit value based on the relative weight; obtain a first type of deduction limit value based on the upper limit of deductions and the second type of deduction limit value; and obtain a first type of deduction proportion coefficient and a second type of deduction proportion coefficient based on the first type of deduction limit value and the second type of deduction limit value.

[0163] In one embodiment, the data processing module 30 is further configured to obtain a predefined first deduction function and a second deduction function; obtain a first type of conversion quantity based on the first type of limit value and the number of objects involved in the error; obtain an error ratio based on the cumulative number of error reports and the number of rule executions; and obtain a first type of deduction and a second type of deduction for the error category based on the process quantity, the statistic quantity, the first deduction function, the second deduction function, the first type of conversion quantity, and the error ratio.

[0164] In one embodiment, the data processing module 30 is further configured to obtain a first type of deduction based on the first deduction function, the second deduction function, the lower limit of deductions, the first type of deduction proportion coefficient, the first type of conversion quantity, and the difference between the upper and lower deduction ranges; and obtain a second type of deduction based on the first deduction function, the lower limit of deductions, the second type of deduction proportion coefficient, the error ratio, and the allowable error ratio.

[0165] The present application provides a device for evaluating the quality of land survey data. The device for evaluating the quality of land survey data includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for evaluating the quality of land survey data in the first embodiment above.

[0166] Next, refer to Figure 12, which shows a schematic structural diagram of a land survey data quality evaluation device suitable for implementing the embodiments of the present application. The land survey data quality evaluation device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions, tablet computers), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 12 The shown land survey data quality evaluation device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0167] As Figure 12 shown, the land survey data quality evaluation device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to the program stored in the ROM (Read Only Memory) 1002 or the program loaded from the storage device 1003 into the RAM (Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the land survey data quality evaluation device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, an LCD (Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the land survey data quality evaluation device to communicate with other devices wirelessly or wireline to exchange data. Although the figure shows a land survey data quality evaluation device with various systems, it should be understood that it is not required to implement or have all the shown systems. Instead, more or fewer systems may be implemented or had.

[0168] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, the above functions defined in the methods of the embodiments disclosed in the present application are executed.

[0169] The land survey data quality evaluation device provided by the present application adopts the land survey data quality evaluation method in the above embodiments, and can solve the technical problem of how to evaluate the quality of land survey data under the condition of taking into account the severity of errors and the deduction limit. Compared with the prior art, the beneficial effects of the land survey data quality evaluation device provided by the present application are the same as those of the land survey data quality evaluation method provided by the above embodiments, and other technical features in the land survey data quality evaluation device are the same as the features disclosed in the method of the previous embodiment, which will not be elaborated here.

[0170] It should be understood that each part disclosed in the present application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0171] As mentioned above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0172] [[ID=I2]]The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the land survey data quality evaluation method in the above embodiments.

[0173] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or flash memory), optical fibers, CD-ROM (Compact Disk Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0174] The above computer-readable storage medium can be included in the land survey data quality evaluation device; or it can exist separately without being assembled into the land survey data quality evaluation device.

[0175] The above computer-readable storage medium carries one or more programs. When the one or more programs are executed by the land survey data quality evaluation device, the land survey data quality evaluation device is caused to: obtain the land survey data of the district or county, and perform statistics based on the land survey data to obtain a statistic; divide the error categories in the land survey data based on the expert knowledge base, and determine the evaluation reference quantity of the error categories; obtain the first-class deductions and second-class deductions of the error categories according to the evaluation reference quantity and the statistic; obtain the evaluation scores of each district or county according to the first-class deductions and the second-class deductions of the error categories, and perform a quality evaluation of the land survey data according to the evaluation scores.

[0176] Computer program code for performing the operations of this application can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or it can be connected to an external computer (for example, by connecting through the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and this module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0178] The modules involved in the embodiments described in this application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0179] The readable storage medium provided in this application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for performing the above-mentioned national land survey data quality evaluation method, and can solve the technical problem of how to perform the national land survey data quality evaluation under the condition of taking into account the severity of errors and the deduction limit. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the national land survey data quality evaluation method provided in the above embodiments, and will not be elaborated here.

[0180] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the steps of the method for evaluating the quality of land survey data as described above.

[0181] The computer program product provided by the present application can solve the technical problem of how to evaluate the quality of land survey data under the conditions of taking into account the severity of errors and the deduction limit. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the method for evaluating the quality of land survey data provided in the above embodiments, and will not be elaborated herein.

[0182] The above are only some embodiments of the present application, and thus do not limit the patent scope of the present application. Any equivalent structural transformation made under the technical concept of the present application by using the content of the specification and drawings of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A method for evaluating the quality of land survey data, characterized in that, The method includes: Obtaining the land survey data of the district or county, and performing statistics based on the land survey data to obtain statistics, where the statistics include the total number of objects, the number of objects involved in errors, the number of rule executions, and the cumulative number of error reports; Dividing the error categories in the land survey data based on an expert knowledge base, and determining the evaluation reference quantities for the error categories, where the evaluation reference quantities include the upper limit of the score range, the lower limit of the score range, the relative weight, the object number limit coefficient, and the fault tolerance ratio; Obtaining the first-class deductions and second-class deductions for the error categories based on the evaluation reference quantities and the statistics; Obtaining the evaluation scores of each district or county based on the first-class deductions and the second-class deductions of the error categories, and performing quality evaluation of the land survey data based on the evaluation scores.

2. The method according to claim 1, characterized in that, The step of dividing the error categories in the land survey data based on an expert knowledge base and determining the evaluation reference quantities for the error categories includes: Determining, based on an expert knowledge base, the impact degree of the error categories in the land survey data on the data quality, the lower limit and the upper limit of the score range of the error categories, the object number limit coefficient of the error categories, and the fault tolerance ratio; Dividing based on the impact degree to obtain the error severity levels and error categories, and assigning relative weights to the error categories according to the error severity levels.

3. The method according to claim 2, characterized in that, The step of dividing the error categories based on the impact degree includes: Obtaining the preset complexity and workload for repairing the error categories; Dividing the error categories according to the impact degree to obtain main categories, where the main categories include those affecting the integrity of the results, those affecting the summary statistics, and those affecting the data application; Dividing the main categories according to the complexity and the workload to obtain sub-categories, where the sub-categories include those requiring return for modification, those requiring individual modification, and those requiring batch modification.

4. The method according to claim 1, wherein The step of obtaining the first-class deductions and second-class deductions for the error categories based on the evaluation reference quantities and the statistics includes: Obtaining process quantities based on the evaluation reference quantities and the statistics, where the process quantities include the upper limit of deductions, the lower limit of deductions, the difference between the upper and lower deduction ranges, the first-class limit value, the first-class deduction limit value, the second-class deduction limit value, the first-class deduction proportion coefficient, and the second-class deduction proportion coefficient; Obtaining the first-class deductions and second-class deductions for the error categories based on the process quantities and the statistics.

5. The method according to claim 4, wherein The step of obtaining process quantities based on the evaluation reference quantities and the statistics includes: Obtaining the upper limit of deductions and the lower limit of deductions based on the lower limit of the score range and the upper limit of the score range; Obtaining the difference between the upper and lower deduction ranges based on the upper limit of deductions and the lower limit of deductions; Obtaining the first-class limit value based on the object number limit coefficient and the total number of objects; Obtaining the second-class deduction limit value based on the relative weight; Obtaining the first-class deduction limit value based on the upper limit of deductions and the second-class deduction limit value; Obtaining the first-class deduction proportion coefficient and the second-class deduction proportion coefficient based on the first-class deduction limit value and the second-class deduction limit value.

6. The method according to claim 4, wherein The step of obtaining the first-class deductions and second-class deductions for the error categories based on the process quantities and the statistics includes: Obtaining a predefined first deduction function and a second deduction function; Obtain a first type of conversion quantity based on the first type of limit value and the number of objects involved in the error; Obtain an error ratio based on the cumulative number of error reports and the number of rule executions; Obtain a first type of deduction score and a second type of deduction score for the error category based on the process quantity, the statistical quantity, the first deduction function, the second deduction function, the first type of conversion quantity, and the error ratio; 7. The method according to claim 6, wherein The step of obtaining a first type of deduction score and a second type of deduction score for the error category based on the process quantity, the statistical quantity, the first deduction function, the second deduction function, the first type of conversion quantity, and the error ratio includes: Obtain a first type of deduction score based on the first deduction function, the second deduction function, the lower limit of deduction, the proportion coefficient of the first type of deduction score, the first type of conversion quantity, and the difference between the upper and lower deduction ranges; Obtain a second type of deduction score based on the first deduction function, the lower limit of deduction, the proportion coefficient of the second type of deduction score, the error ratio, and the error tolerance ratio; 8. A device for evaluating the quality of land survey data, characterized in that, The device includes: A data extraction module, configured to obtain the national land survey data of the district or county, and perform statistics based on the national land survey data to obtain a statistical quantity, where the statistical quantity includes the total number of objects, the number of objects involved in the error, the number of rule executions, and the cumulative number of error reports; A data division module, configured to divide the error categories in the national land survey data based on an expert knowledge base, and determine an evaluation reference quantity for the error category, where the evaluation reference quantity includes the upper limit of the score range, the lower limit of the score range, the relative weight, the limit value coefficient of the number of objects, and the error tolerance ratio; A data processing module, configured to obtain a first type of deduction score and a second type of deduction score for the error category based on the evaluation reference quantity and the statistical quantity; A quality evaluation module, configured to obtain an evaluation score for each district or county based on the first type of deduction score and the second type of deduction score of the error category, and perform quality evaluation on the national land survey data based on the evaluation score; 9. A device for evaluating the quality of land survey data, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the computer program is configured to implement the steps of the national land survey data quality evaluation method according to any one of claims 1 to 7; 10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the national land survey data quality evaluation method according to any one of claims 1 to 7.

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