CN108830554B - Task model-based intelligent detection method and system for data result information quality - Google Patents
Task model-based intelligent detection method and system for data result information quality Download PDFInfo
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Abstract
The invention provides a data result information quality intelligent detection method and system based on a task model, wherein the method comprises the steps of firstly, carrying out detection task calculation modeling on a plurality of detection task information corresponding to a data result multi-dimensional quality detection factor based on a quality inspection algorithm, so as to establish the task model; acquiring data achievements to be detected and selecting required detection tasks to form a task group, inputting the task group into a task model, and executing all detection tasks in the task group through the task model to further obtain a detection task calculation result; and then, carrying out data analysis processing on the calculation result of the detection task and outputting a data result information quality detection report to realize intelligent detection. The method can process a large amount of data, and directly inputs the selected detection task into the task model for automatic detection, thereby saving time cost and labor cost, and integrally improving the efficiency, accuracy and comprehensiveness of data result information detection.
Description
Technical Field
The invention relates to the field of system task management, in particular to a task model-based intelligent detection method and system for data result information quality.
Background
In the "specification of database for determining right to register for the rural land contract operation authority" (hereinafter referred to as "specification") and related accessories of the ministry of agriculture, a rule for detecting the data result information of the determining right of the rural land contract operation authority is stipulated, and the content detection of the data result information is realized so as to ensure the quality of the data result information. Wherein, the detection items include: integrity, logic consistency, vector data topology correctness and the like of data result information. Because the file types in the rural land contract right-confirming registration data result information are various, the data files comprise mdb data base, raster image, vector file, word file, Excel file and other data files with various formats; the data volume and the occupied storage space of the data result information are large, the number of the land parcels is in the million level, the data volume of the boundary point and the boundary line is in the million level, and the occupied storage space of the single data result information can reach the TB level; in addition, there are 2800 counties across the country, and the amount of data of the data result information across the country is an enormous number, and the number of rules stipulated by the "norm" and its accessories is also large, and may be adjusted and updated as the validation work proceeds deeply, which is difficult to accomplish by using the conventional detection method. Finally, the whole authority-confirming registration work is limited in time, and the efficiency of the detection work and the performance of the detection tool software are extremely high in the specified time. Due to the reasons, the detection of the data result information registered by the right of the rural land contract-administration right is difficult, and the quality detection work faces huge challenges when the data result information is collected.
At present, the existing quality detection method is completed by combining various tool software with manual detection, for example, Microsoft Access is adopted to open mdb database files, scri ArcGis is adopted to open raster images and vector files, word processing and spreadsheet software is used to open various documents and tables, and sql statements and spreadsheet formulas are combined to detect whether data result information meets the 'specification' requirements according to rules. The traditional quality detection method needs to manually utilize the software to carry out calculation, integration and calibration, and has the problems of low reliability of detection results and incomplete detection caused by certain errors or randomness. In addition, there are problems that detection efficiency is low, tools are disordered and difficult to manage, and the like.
Disclosure of Invention
The invention discloses a method for intelligently detecting the quality of data result information based on a task model, which aims at solving the problems that the reliability of a detection result is not high, the detection is not comprehensive, the efficiency is low, tools are easy to be confused and the like because various tool software is opened and manual work is combined in the conventional quality detection method. The invention also relates to a task model-based intelligent detection system for the information quality of the data result.
The technical scheme of the invention is as follows:
a data result information quality intelligent detection method based on a task model is characterized in that the method firstly carries out detection task calculation modeling on a plurality of detection task information corresponding to a data result multi-dimensional quality detection factor based on a quality inspection algorithm, so as to establish the task model; then collecting data results to be detected and selecting required detection tasks to form a task group; inputting the task group into the task model, and executing all detection tasks in the task group through the task model to further obtain a detection task calculation result; and then, carrying out data analysis processing on the detection task calculation result and outputting a data result information quality detection report to realize intelligent detection.
Furthermore, the multidimensional quality detection factor of the data result comprises an integrity detection factor, a format type data detection factor and a consistency detection factor, wherein a plurality of detection tasks corresponding to the integrity detection factor are subjected to detection task calculation modeling based on a data integrity detection algorithm, a vector data detection task corresponding to the format type data detection factor is subjected to detection task calculation modeling based on an attribute detection algorithm and a graph detection algorithm, a ownership data detection task corresponding to the format type data detection factor is subjected to detection task calculation modeling based on a field detection algorithm, a raster data detection task corresponding to the format type data detection factor is subjected to detection task calculation modeling based on a raster data space analysis algorithm, and a metadata detection task corresponding to the format type data detection factor is based on a metadata structure, Carrying out detection task calculation modeling by using a detection algorithm of space and content, wherein a data consistency detection task and a summary table consistency detection task corresponding to the consistency detection factor are both based on the consistency detection algorithm to carry out detection task calculation modeling; the task model is in a nested model architecture.
Further, selecting a required detection task from the associated detection task information according to the content and the data type of the acquired data result to be detected to form a task group, wherein the required detection task is all or part of the detection task information associated with the data result to be detected, the detection task in the task group comprises an independent task and/or at least one stage of cascaded subtask group, and the independent task is a task for realizing a single specific detection function; the subtask group refers to a task that implements a plurality of specific detection functions.
Furthermore, a task group library for storing task groups is also arranged; and for the data result to be detected, calling the saved task group from the task group library, acquiring the path of the data result to be detected, acquiring the data result to be detected, selecting the required detection task to realize task parameter configuration to form a new task group, inputting the new task group into the task model, and saving the formed new task group into the task group library.
Further, the task model executes all detection tasks in the task group according to preset priorities based on a topological algorithm, processes each independent task according to the priorities when the detection tasks in the task group are all independent tasks, processes the subtask group according to a cascading algorithm when the detection tasks in the task group are at least one cascading subtask group, and then obtains a detection task calculation result.
Further, when quality detection error information exists in the output data result information quality detection report, correcting the detected data result information, wherein the quality detection error information comprises attribute error information and topology error information; and the attribute error information is checked through an error list, the topology error information is checked through an ArcGIS map, the detected data result information is corrected according to the checked quality detection error information, and the detection tasks required by the task group are executed by inputting the task model again after correction until the data result information quality detection report is output to indicate that the data result to be detected eliminates the detection errors so as to achieve the detection standard.
The system is characterized by comprising a task model creating module, a task configuration module, a task group execution module and a data analysis processing module which are sequentially connected:
the task model creating module carries out detection task calculation modeling on a plurality of detection task information corresponding to the data result multi-dimensional quality detection factors based on a quality inspection algorithm, so as to create a task model;
the task configuration module collects data achievements to be detected and selects required detection tasks to form a task group, and the task group is input into the task model;
the task group execution module executes all detection tasks in the task group through the task model so as to obtain a detection task calculation result;
and the data analysis processing module performs data analysis processing on the detection task calculation result and outputs a data result information quality detection report to realize intelligent detection.
The system is further characterized by further comprising a task group library, wherein the task group library is used for storing task groups and is connected with the task configuration module, the task configuration module calls the stored task groups from the task group library to acquire paths of the data results to be detected so as to collect the data results to be detected and selects the required detection tasks to realize task parameter configuration so as to form new task groups, the new task groups are input into the task model, and the formed new task groups are also stored into the task group library.
The system further comprises a storage module and an error judgment processing module, wherein the storage module and the error judgment processing module are both connected with the data analysis processing module, the error judgment processing module is also connected with the task group execution module, and the storage module stores a data result information quality detection report output by the data analysis processing module;
the error judgment processing module is used for judging whether quality detection error information exists in the output data result information quality detection report or not, and correcting the detected data result information when the quality detection error information exists, wherein the quality detection error information comprises attribute error information and topology error information; the attribute error information is checked through an error list, the topology error information is checked through an ArcGIS map, the detected data result information is corrected according to the checked quality detection error information, the corrected data result information is returned to the task group execution module to execute the detection tasks required by the task group through re-inputting the task model after correction, and the data result information quality detection report is output to indicate the data result to be detected to eliminate the detection errors so as to achieve the detection standard.
The method is characterized in that the task configuration module selects a required detection task from the associated detection task information according to the content and the data type of the acquired data result to be detected to form a task group, the required detection task is all or part of the detection task information associated with the data result to be detected, the detection task in the task group comprises an independent task and/or at least one stage of cascading subtask group, and the task group is input into the task model; the task group execution module executes all detection tasks in the task group through the task model, the task model executes all detection tasks in the task group according to a preset priority based on a topological algorithm, each independent task is processed according to the priority when all the detection tasks in the task group are independent tasks, and the subtask group is processed according to a cascading algorithm when the detection tasks in the task group are at least one-stage cascading subtask group, so that a detection task calculation result is obtained.
The invention has the following technical effects:
the invention provides a data result information quality intelligent detection method based on task model, which carries out detection task calculation modeling on a plurality of detection task information corresponding to data result multidimensional quality detection factors based on a quality inspection algorithm, thereby establishing a task model, wherein the task model can also be called a task management model or a detection task calculation model, and carries out automatic quality inspection based on the task management model, namely a task, which originally refers to a basic working unit finished by a computer under the environment of a multiprogram or multiprogram in a computer system and is one or more instruction sequences processed by a control program, the task model related to the invention utilizes and extends the concept of the task model in a computer operating system, and the definition and mapping work from a quality inspection rule to the task is well realized by utilizing a task model mechanism, the quality detection rules specified by the 'specification' and accessories thereof are decomposed into a task sequence consisting of a plurality of detection task information, each quality detection rule is defined as one task or a plurality of tasks to form a task management model, the task management model is compatible with a plurality of detection task information corresponding to multidimensional quality detection factors of various types of data results, so that the data results to be detected are collected in practical application, the required detection tasks are selected, namely, the inspection parameters of a task group are configured, the task group to be executed is formed, the task group is input into the task model, all detection tasks in the task group can be executed through the task model, further, the calculation results of the detection tasks are obtained, and different tool software does not need to be opened in sequence to execute the detection tasks like the prior art, the volume of the data result to be detected is huge and is usually more than TB magnitude and the data types are various, the selected task to be detected is directly input to a task model which is unified, standardized and powerful in function for automatic detection, and the method can simultaneously carry out quality detection on the data result information in batches, so that the problems of low efficiency, high error rate and the like caused by the fact that corresponding auxiliary software is adopted manually and the detection data result information is detected one by one according to detection rules are solved, the time cost and the labor cost are saved, and the efficiency of detecting the data result information is integrally improved.
Furthermore, the related data result multi-dimensional quality detection factors comprise integrity detection factors, format type data detection factors and consistency detection factors, detection task calculation modeling can be performed by using a plurality of quality detection algorithms such as a data integrity detection algorithm, an attribute detection algorithm, a graph detection algorithm, a field detection algorithm, a raster data space analysis algorithm, a metadata structure space content detection algorithm and a consistency detection algorithm, and the established task model is in a nested model architecture. That is, the quality inspection rules are mapped to corresponding tasks according to the quality inspection rules, and when the definition rules are tasks, the rules need to be classified and managed, and the rules of each class and the inspected data have different emphasis, and the defined task details are also different. The classification mainly comprises data integrity check, vector data check, ownership data check, raster data check, metadata check, data consistency check and summary table consistency check, and each quality inspection rule is defined as one task or a plurality of tasks, so that a powerful standardized task management model is formed, and the quality inspection efficiency is improved.
Further, the task group comprises independent tasks and/or at least one stage of cascade subtask group, such as a first stage subtask group, a second stage subtask group and a multi-stage subtask group, the task groups are nested layer by layer to form a nested task structure, the task model is preferably also in a nested model architecture, the task model executes all detection tasks in the task group according to a preset priority based on a topological algorithm, so that a user interface is friendly, and when a new task group is created, a user can conveniently select corresponding detection items according to the data type to form the task group, namely inspection parameters of the tasks can be flexibly configured according to needs, the management is convenient, and the accuracy of data detection is indirectly improved.
Furthermore, when quality detection error information exists in an output data result information quality detection report, a correction step is preferably set to correct the detected data result information, wherein the quality detection error information comprises attribute error information and topology error information; the attribute error information is checked through an error list, the topology error information is checked through an ArcGIS map, the detected data result information can be corrected by adopting an auxiliary tool aiming at the checked quality detection error information, the detection items which fail to pass the detection can form a new task group, and the corrected data result information is returned to a task model to execute the detection tasks required by the task group until the output data result information quality detection report indicates that the data result to be detected eliminates the detection error to reach the detection standard, namely, the detection error information does not exist in the detection result information, so that the accuracy of data detection is further improved.
The invention also relates to a task model-based intelligent detection system for the information quality of the data result, which corresponds to the method for the task model-based intelligent detection system for the information quality of the data result, can be understood as a system corresponding to the method for realizing the task model-based intelligent detection method for the information quality of the data result, and comprises a task model creation module, a task configuration module, a task group execution module and a data analysis processing module which are connected. The task model creating module works specifically to create a standardized powerful task model, and works together with other modules, the system can process a large amount of data, a task group formed by tasks to be detected can be directly executed in batches, errors caused by manually adopting corresponding auxiliary software and detecting data result information one by one according to detection rules are avoided, time cost and labor cost are saved, and the efficiency of data result information detection is integrally improved.
Further, the system for intelligently detecting the quality of the data result information based on the task model further comprises a storage module and an error judgment processing module, wherein the storage module stores a data result information quality detection report output by the data analysis processing module, the error judgment processing module is used for judging whether quality detection error information exists in the output data result information quality detection report, when the quality detection error information exists, an auxiliary tool can be adopted to correct the detected data result information according to the detection error information, the detection items which fail to be detected form a new task group, the corrected data result information is returned to the task model for re-detection until the detection error information does not exist in the detection result information, the setting of the preferable module enriches the system architecture, so that the system function is more complete, and the accuracy of data detection is further improved, and the reliability of the system is enhanced.
Drawings
Fig. 1 is a flowchart of an embodiment of an intelligent data result information quality detection method based on a task model.
FIG. 2 is a schematic diagram of the task model of the present invention.
FIG. 3 is a preferred flowchart of the task model-based intelligent data result information quality detection method.
FIG. 4 is a schematic structural diagram of an intelligent detection system for information quality of data results based on a task model.
FIG. 5 is a schematic diagram of a preferred structure of the intelligent detection system for information quality of data result based on task model.
Fig. 6a to 6l are preferred working state diagrams of specific examples of the task model-based intelligent data result information quality detection method.
Detailed Description
The present invention will be described with reference to the accompanying drawings.
The invention relates to a task model-based data result information quality automatic detection method, the flow of which is shown in figure 1, and the method comprises the following steps: s1, firstly, carrying out detection task calculation modeling on a plurality of detection task information corresponding to the data result multi-dimensional quality detection factors based on a quality inspection algorithm, thereby establishing a task model; s2, collecting the data result to be detected and selecting the required detection task to form a task group, inputting the task group into the task model, and executing all detection tasks in the task group through the task model to further obtain a detection task calculation result; and S3, performing data analysis processing on the detection task calculation result and outputting a data result information quality detection report to realize intelligent detection.
Specifically, the intelligent detection method for the information quality of the data result based on the task model is further described in detail.
S1, firstly, carrying out detection task calculation modeling on a plurality of detection task information corresponding to the data result multi-dimensional quality detection factors based on a quality inspection algorithm, thereby establishing a task model;
in this embodiment, the data result is an authority confirming registration database result preferably formed by taking a county-level administrative district as a basic unit in the process of the country land contract operation authority confirming registration. And vector data (graphic data such as spatial points, lines, planes, etc., represented by coordinates or ordered coordinate strings, and related attribute data associated therewith), raster data (data sets in which a geographic space is divided into regular rows and columns and each cell has a different "value"), graphic data (data representing the position, form, size, and distribution characteristics of a geographic object, and collection type). The data result multi-dimensional quality detection factor refers to at least one data result information quality intelligent detection item. Preferably, the data result multidimensional quality detection factor may include an integrity detection factor, a format type data detection factor, a consistency detection factor, and the like; the detection task calculation modeling is carried out on a plurality of detection tasks corresponding to the integrity detection factors based on a data integrity detection algorithm, the detection task calculation modeling is carried out on a vector data detection task corresponding to the format type data detection factors based on an attribute detection algorithm and a graph detection algorithm, the detection task calculation modeling is carried out on a raster data detection task corresponding to the format type data detection factors based on a raster data space analysis algorithm, and the detection task calculation modeling is carried out on a metadata detection task corresponding to the format type data detection factors based on a detection algorithm of a metadata structure, a space and contents; the data consistency detection task and the summary table consistency detection task corresponding to the consistency detection factor are based on a consistency detection algorithm to perform detection task calculation modeling; the task model is in a nested model architecture. Referring to table 1, as a preferred task model, in the present embodiment, the multidimensional detection factors include: data integrity detection factor, vector data detection factor, ownership data detection factor, raster data detection factor, metadata detection factor, data consistency detection factor, and summary table consistency detection factor. The vector data detection factor, the ownership data detection factor, the raster data detection factor and the metadata detection factor belong to the format type data detection factor.
In this embodiment, a task refers to one or more instruction sequences processed by a control program, which is a basic unit completed by a computer under a multiprogramming or multiprocessing environment in a computer system. The task model is a layer-by-layer nested task structure, which adopts the idea of tasks in a computer operating system, classifies the data detection standard specified by the state and the rules specified by the accessories thereof for detecting the information quality of the data result, and defines the detection rules as the layer-by-layer nested task structure consisting of a plurality of tasks or task groups according to the classification results.
And S2, collecting data results to be detected and selecting required detection tasks to form a task group, inputting the task group into the task model, and executing all detection tasks in the task group through the task model to further obtain a detection task calculation result.
In this embodiment, most of the data result information to be detected exists in the form of files, and the types of the files are various, including mdb databases, raster images, vector files, word files, Excel files, and other data files.
In this embodiment, the data result to be detected is collected, all the detection task information can be organized into a task group, the task group is input to the task model, and all the detection tasks in the task group are executed by the task model, so that the detection task calculation result is obtained; and selecting required detection tasks from the detection task information associated with the data achievements to be detected to form a task group according to the content and the data type of the acquired data achievements to be detected. Preferably, in this embodiment, according to the content and the data type of the acquired data result to be detected, a required detection task is selected from the associated detection task information to form a task group, and the required detection task is all or part of the detection task information associated with the data result to be detected. And inputting the obtained task group into a pre-established task model, executing all detection tasks in the task group by using the task model, and obtaining a calculation result of the detection tasks according to a corresponding detection algorithm.
In this embodiment, the task model is a task group structure nested in a layer, and referring to fig. 2, it is a schematic diagram of the task model of the present invention, where the detection tasks in the task group include independent tasks and/or at least one stage of cascaded subtask groups, and an independent task is a task that implements a single specific detection function; the subtask group refers to a task that implements a plurality of specific detection functions. As shown in fig. 2, the label 1 represents a task group, and includes a plurality of independent tasks, for example, the task 1 represented by the label 2 and the task n are independent tasks, and the independent tasks may have different functions or the same function. The detection task further includes a cascaded subtask group, for example, the subtask group represented by the label 3 and the subtask group represented by the label 4 form the cascaded subtask group, that is, the cascaded subtask group is a task group in the task group, that is, the task group may also include other task groups, for example, the task 2 includes a plurality of tasks that may have different functions or the same function: task 2.1, task 2.2 (label 5) … task 2. n. Whether a task group appears as an independent task or a cascaded set of subtasks, is essentially a task, differing only in the content and manner in which it is implemented. Any task has a complete life cycle, and the life cycle of the task can be rewritten according to different checking rules so as to realize different business functions. Referring to table 1 again, the data result quality detection rule is defined as a task model, the multidimensional detection factor is defined as a task group, the detection item corresponding to the multidimensional detection factor is defined as an independent task and/or a primary cascade subtask group, and table 1 correspondingly includes the inspection item, the inspection content, the inspection requirement and the requirement description. The inspection items are the task groups defined by the multidimensional detection factor, the sequence among the task groups can be fixed or random arrangement, and the subtask groups (inspection content in the corresponding table) in the inspection items are preferably arranged in a fixed sequence. For example, a data integrity detection factor is defined as a task group, and a detection item corresponding to the detection factor: directory and file normative check, data validity check, vector data integrity check and attribute data integrity check are defined as a first-level cascading subtask group.
In this embodiment, the execution of the task group may be performed by the task model according to the selection order of the selected detection items; all detection tasks in the task group can be executed by the task model based on the topological algorithm according to the priority preset by the user. Preferably, in this embodiment, the task model executes all detection tasks in the task group according to a preset priority based on a topology algorithm, processes each independent task according to the priority when the detection tasks in the task group are all independent tasks, and processes the subtask group according to a cascade algorithm when the detection tasks in the task group are at least one cascade subtask group, so as to obtain a detection task calculation result.
When the task group is executed, the specific steps are as follows:
l1: initializing the task group based on the task model, and acquiring a first task in the task group;
l2: initializing information of the selected task;
l3: judging whether the task is an independent task, if so, configuring a first task, and calculating the obtained data result information according to a quality inspection algorithm to obtain detection result information; if not, executing the next step;
l4: and if the task group is a primary cascade subtask group, configuring the primary cascade subtask group, selecting a first primary cascade independent task in the primary cascade subtask group, and calculating the obtained data result information according to a quality inspection algorithm to obtain detection result information.
And step S3, carrying out data analysis processing on the detection task calculation result and outputting a data result information quality detection report to realize intelligent detection.
It should be noted that, according to the content and data type of the data result to be detected, the rules specified by the detection specifications and their accessories specified by the country can be classified more thoroughly and finely, and according to the classification result, a secondary cascade task group or even a multi-stage cascade task group is defined. Referring again to table 1, for example, the vector data checking factor in the format type data detection factor includes a first-order cascading subtask group: graph inspection, secondary cascading subtask group: spot element inspection, linear element inspection, planar element inspection, multi-part inspection. And sequentially executing each stage of cascading subtask groups according to a cascading algorithm, wherein when a secondary task group or a multi-stage task group is executed, the execution process and the execution mechanism are completely the same as those of the execution mechanism when the primary cascading task group is executed, and the details are not repeated herein.
TABLE 1
The invention provides a data result information quality intelligent detection method based on task model, which carries out detection task calculation modeling on a plurality of detection task information corresponding to data result multidimensional quality detection factors based on a quality inspection algorithm, thereby establishing a task model, wherein the task model can also be called a task management model or a detection task calculation model, and carries out automatic quality inspection based on the task management model, namely a task, which originally refers to a basic working unit finished by a computer under the environment of a multiprogram or multiprogram in a computer system and is one or more instruction sequences processed by a control program, the task model related to the invention utilizes and extends the concept of the task model in a computer operating system, and the definition and mapping work from a quality inspection rule to the task is well realized by utilizing a task model mechanism, the quality detection rules specified by the 'specification' and accessories thereof are decomposed into a task sequence consisting of a plurality of detection task information, each quality detection rule is defined as one task or a plurality of tasks to form a task management model, the task management model is compatible with a plurality of detection task information corresponding to multidimensional quality detection factors of various types of data results, so that the data results to be detected are collected in practical application, the required detection tasks are selected, namely, the inspection parameters of a task group are configured, the task group to be executed is formed, the task group is input into the task model, all detection tasks in the task group can be executed through the task model, further, the calculation results of the detection tasks are obtained, and different tool software does not need to be opened in sequence to execute the detection tasks like the prior art, the volume of the data result to be detected is huge and is usually more than TB magnitude and the data types are various, the selected task to be detected is directly input to a task model which is unified, standardized and powerful in function for automatic detection, and the method can simultaneously carry out quality detection on the data result information in batches, so that the problems of low efficiency, high error rate and the like caused by the fact that corresponding auxiliary software is adopted manually and the detection data result information is detected one by one according to detection rules are solved, the time cost and the labor cost are saved, and the efficiency of detecting the data result information is integrally improved.
The invention well realizes the definition and mapping work from the quality inspection rule to the task by utilizing a task model mechanism. After the task management function is realized by adopting the task model established by the invention, all quality detection rules can be established into a task group readable by computer software. After all the quality inspection rules are defined into corresponding task groups, it can be understood that the task management mechanism constructed by the component development framework configures and schedules the automatic execution of the task groups, that is, the selected task groups are automatically input into the task model, and finally the quality inspection work of the data result is completed.
The invention discloses a preferable intelligent detection method for data result information quality based on a task model, which is based on the first embodiment and comprises the following steps: a task group library for storing task groups is arranged; the method comprises the steps of calling a stored task group from a task group library to acquire a path of a data result to be detected so as to acquire the data result to be detected, selecting a required detection task to realize task parameter configuration to form a new task group, inputting the new task group into a task model, and storing the formed new task group into the task group library.
On the basis of the first embodiment, the preferred intelligent detection method for the information quality of the data result based on the task model further comprises the following steps: when quality detection error information exists in the output data result information quality detection report, correcting the detected data result information, wherein the quality detection error information comprises attribute error information and topology error information; and checking the attribute error information through an error list, checking the topology error information through an ArcGIS map, correcting the detected data result information according to the checked quality detection error information, and executing a detection task required by a task group by inputting a task model again after correction until a data result information quality detection report is output to mark the data result to be detected to eliminate the detection error so as to reach the detection standard. Preferably, a correction step is set, the detected data result information can be corrected by adopting an auxiliary tool aiming at the checked quality detection error information, the detected failed detection items can form a new task group, and the corrected data result information is returned to the task model to execute the detection tasks required by the task group until the detection result information does not contain the detection error information any more, so that the accuracy of data detection is further improved.
Referring to fig. 3, it is a preferred flowchart of the intelligent detection method for information quality of data result based on task model. The flow chart can be understood as a simplified schematic diagram of a preferred flow, wherein the step of creating the task model is not shown in the diagram, and the task model is considered to be created, and quality detection is started; after starting, a group of new task groups is created or a previously stored task group is opened by using an opening button; configuring inspection parameters of tasks, namely acquiring data results to be detected and selecting required detection tasks to form a task group, mainly comprising: selecting a result data path, checking required items and the like; executing a task group, namely inputting the task group into the task model, executing all detection tasks in the task group through the task model, starting to check, and executing the checking process according to quality check rules and checked check items in sequence, wherein the process substantially comprises the following steps: directory checks, attribute checks, topology checks, consistency checks, and the like; the inspection process may generate several pieces of information, including: task execution logs, topology errors, attribute errors, other errors, and the like; obtaining a detection task calculation result; using the task saving function in the task model to save the parameters of the inspection items, the inspection results and the like into files; and performing data analysis processing on the calculation result of the detection task, outputting a data result information quality detection report, and checking the inspection result by using a proper function, such as: using an error list function to check attribute errors and a check log; checking the topological error by using an ArcGIS map function; judging whether an error exists, and if the error does not exist, marking the data to be detected to pass the inspection; if the output data result information quality detection report has errors, the errors need to be modified, namely, when the output data result information quality detection report has quality detection error information, the detected data result information is modified; the error is modified in a suitable manner. The quality inspection software does not provide data modification functionality and the user needs to use other tools to modify the error. For example: ArcGIS or Access. And after the error modification is finished, the checking is executed again, and the steps are repeated until the error does not occur any more, and finally, the intelligent detection of the data result information quality is realized.
The invention also relates to a data result information quality intelligent detection system based on the task model, which corresponds to the data result information quality intelligent detection system based on the task model, can be understood as a system corresponding to the data result information quality intelligent detection method based on the task model, has a structure shown in fig. 4, and comprises a task model establishing module, a task configuration module, a task group execution module and a data analysis processing module which are connected in sequence, wherein the task model establishing module carries out detection task calculation modeling on a plurality of detection task information corresponding to the data result multidimensional quality detection factor based on a quality inspection algorithm, so as to establish the task model; the task configuration module collects data achievements to be detected and selects required detection tasks to form a task group, and the task group is input into the task model; the task group execution module executes all detection tasks in the task group through the task model so as to obtain a detection task calculation result; and the data analysis processing module performs data analysis processing on the detection task calculation result and outputs a data result information quality detection report to realize intelligent detection. The task model creating module works specifically to create a standardized powerful task model, and works together with other modules, the system can process a large amount of data, a task group formed by tasks to be detected can be directly executed in batches, errors caused by manually adopting corresponding auxiliary software and detecting data result information one by one according to detection rules are avoided, time cost and labor cost are saved, and the efficiency of data result information detection is integrally improved.
Referring to fig. 5, the system optimal structure diagram is an optimal structure diagram of an intelligent detection system for information quality of data results based on a task model, and in the system optimal structure diagram, in addition to the four modules, the system optimal structure diagram further includes a task group library, the task group library is used for storing task groups and is connected to the task configuration module, and the task configuration module calls the stored task groups from the task group library for the data results to be detected, obtains a path of the data results to be detected, acquires the data results to be detected, selects a required detection task to implement task parameter configuration to form a new task group, and inputs the new task group into the task model, and the formed new task group is also stored in the task group library.
Referring to fig. 5, the preferred intelligent detection system for the information quality of the data result further includes a storage module and an error judgment processing module, both of which are connected to the data analysis processing module, the error judgment processing module is also connected to the task group execution module, and the storage module stores the data result information quality detection report output by the data analysis processing module; the error judgment processing module is used for judging whether quality detection error information exists in the output data result information quality detection report or not, and correcting the detected data result information when the quality detection error information exists, wherein the quality detection error information comprises attribute error information and topology error information; and checking the attribute error information through an error list, checking the topology error information through an ArcGIS map, correcting the detected data result information according to the checked quality detection error information, returning to the task group execution module after correction to execute the detection tasks required by the task group by re-inputting the task model until a data result information quality detection report is output to indicate that the data result to be detected eliminates the detection errors so as to achieve the detection standard. The task configuration module selects a required detection task from the associated detection task information according to the content and the data type of the acquired data result to be detected to form a task group, the required detection task is all or part of the detection task information associated with the data result to be detected, the detection task in the task group comprises an independent task and/or at least one stage of cascade subtask group, and the task group is input into the task model; the task group execution module executes all detection tasks in the task group through the task model, the task model executes all detection tasks in the task group according to preset priorities based on a topological algorithm, each independent task is processed according to the priorities when the detection tasks in the task group are all independent tasks, and the subtask group is processed according to a cascading algorithm when the detection tasks in the task group are at least one cascading subtask group, so that a detection task calculation result is obtained.
The invention provides a preferable scheme of a data result information quality intelligent detection system based on a task model, which also comprises a storage module and an error judgment processing module, wherein the storage module stores a data result information quality detection report output by a data analysis processing module, the error judgment processing module is used for judging whether quality detection error information exists in the output data result information quality detection report, when the quality detection error information exists, an auxiliary tool can be adopted to correct the detected data result information according to the detection error information, the detection items which fail to pass the detection are combined into a new task group, the corrected data result information is returned to the task model to be detected again until the detection result information does not have the detection error information any more, the preferable module enriches the system architecture, so that the system function is more complete, and the accuracy of data detection is further improved, and the reliability of the system is enhanced.
The intelligent data result information quality detection system based on the task model establishes a standardized task model with powerful functions by the specific work of the task model establishing module, and after the task management function is realized by adopting the task model, all quality inspection rules can be established into a task group readable by computer software. After all the quality inspection rules are defined into corresponding task groups, the task groups can be understood as the task configuration module, the task group execution module and the data analysis processing module which are constructed through a component development framework, the automatic execution of the task groups is configured and scheduled, and the quality inspection work of the data results is finally completed.
The following describes the intelligent data result information quality detection method based on the task model in detail, taking the data result information quality detection in Wenxian county as an example.
And carrying out detection task calculation modeling on a plurality of detection task information corresponding to the data result multi-dimensional quality detection factors based on a quality inspection algorithm, thereby establishing a task model. A task model as shown in table 1 above may be established. The data result multi-dimensional quality detection factors comprise integrity detection factors, format type data detection factors and consistency detection factors; the format type data detection factor comprises vector data detection, raster data detection and metadata detection; the consistency detection comprises the following steps: data consistency detection and confluent material consistency detection. Each detection factor in the multi-dimensional quality detection factors of the data result corresponds to a plurality of pieces of detection task information. It should be noted that each detection task corresponds to a corresponding quality inspection algorithm, for example, in the quality inspection algorithm, the "vector data inspection" — the "graphic inspection" — the "planar element small patch inspection" — the "inspection item requirement" — when the area of the parcel element is less than 1 square meter, the authenticity of the parcel element should be verified, and the quality inspection algorithm corresponding to the inspection item is: traversing all the plot spots of the plot elements, and solving the area of each plot spot; if the area is larger than or equal to 1 square meter, skipping, and if the area is smaller than 1 square meter, prompting that the area of the plot XXXX is smaller than 1 square meter, and please verify the authenticity of the plot elements; until all the traversal of the plot patches is completed. The detection of the small broken pattern spots of the planar elements is finished through the quality inspection algorithm. Through the steps, the task model can be established.
The subsequent steps can be executed under a constructed component type development framework during application, namely can be integrated into an APP for execution, and are called rural land contract operation right registration data result information quality intelligent detection software. In connection with the preferred operating state diagram of fig. 6a to 6 l. As shown in fig. 6a, the main interface is a function area above the main interface, the left side below the function area is a new task access bar, and the right side below the function area is a data display area.
The creation of a task group, see fig. 6 b-6 e, is then the process of task group creation. Referring to fig. 6b, namely, in the new task access column on the left side, a quality inspection task is created by clicking "+" on the right side of the inspection data task, and a window of the inspection data task is popped up, wherein parameter configurations in the window comprise items such as a data path to be inspected, a name of an inspected unit, a county-level area code, a data year code, an inspection result path, an inspection configuration, an inspection item configuration and the like. The inspection of this embodiment is configured as a rendezvous inspection. The path selection is performed in the button of the data path to be detected, as shown in fig. 6c, the position indicated by the arrow in the figure is clicked, the position for searching the data information storage of the result of the data to be detected can be opened, the data to be detected can be collected after the selection, and the position for storing the data to be detected can also be directly input in the path of the data file to be detected in the figure. Namely determining to collect the result data to be detected. Then, sequentially configuring each parameter, referring to fig. 6d, clicking a configuration button in the check configuration item of the configuration page, and entering a check item check dialog box; referring to fig. 6e, in the checking item dialog page, a required detection task is selected from the associated detection task information for the content and the data type of the acquired data result to be detected to form a task group, the required detection task may be all or part of the detection task information associated with the data result to be detected, and the several pieces of detection task information corresponding to the data integrity check (i.e., the data integrity detection factor) shown in the figure include: directory structure integrity, required file integrity, vector required file integrity, raster required file integrity, ownership source file path, outsourcing directory consistency, drawing file correctness, etc. in this embodiment, the required check items to be selected are all options (i.e. all tasks).
Referring to fig. 6f to 6g, the formed task group is imported into the task model, the start inspection button is clicked, the task model executes all the detection tasks in the task group, the convergence task inspection is started, that is, whether the corresponding data of Wenxian county meets the normative requirements or not can be inspected, the execution progress of the detection tasks can be displayed immediately, the execution state can be displayed in real time in the inspection process, for example, when the progress is 21%, the state is the vector data identification code conformity inspection, if fig. 6g shows that when the progress is 23%, the state is the boundary line field element code inspection, the task detection result is obtained after the detection tasks are executed, and the detection task result information is expressed in a list form. Referring to fig. 6h, the detection result may indicate the type of the file detection result, including: error, completion, and warning; wherein, the indication of the error indicates that the data result information does not meet the specification requirement, for example, the directory structure integrity and the necessary file integrity of the embodiment do not meet the specification requirement, so the state of the detection result is an error; the marked data result information conforms to the specification requirements, for example, the integrity of a vector necessary file, the integrity of a grid necessary file, the consistency of a contracting party directory, the data validity and the like of the embodiment all conform to the specification requirements, so that the detection result is in a finished state; the indication warning indicates that the data result information has an error risk, for example, the state of the detection result of the graph resolution of the embodiment is a warning. Referring to fig. 6i, clicking the error list, and looking up details of the error information, the aforementioned detection error information indicating an error includes attribute error information and topology error information. The attribute error information can be checked through clicking an error list, and the topology error information can be checked through an ArcGIS map. And marking error prompts for detection results of the data results, correcting the data result information according to the quality detection error information, re-inputting the corrected data result information into the task model to execute tasks required to be detected by the task group, and allowing intelligent detection until all marks are finished in the quality detection report of the output data result information. Referring to fig. 6j, clicking the report button can check the detection report of the intelligent detection of the data quality in wenxi county (such as the result quality inspection report of the country contract management authority registration database in wenxi county shown in the figure), and clicking the export button can export and store the detection report.
Referring to fig. 6k to 6l, after the created task group is imported into the task model for data result quality information detection, the task group can be saved, as shown in fig. 6k, a save task dialog box can be popped up by clicking the save task, the save of the task group is completed, and a task group library is obtained. When the data result is detected next time, a corresponding task group can be selected from the task group library according to the data type and content of the data result to be detected, as shown in fig. 6l, the saved task can be opened by clicking to open the task, and the task group is configured and input to the task model, so that the intelligent detection of the data result information is completed.
It should be noted that the above-mentioned embodiments enable a person skilled in the art to more fully understand the invention, without restricting it in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, it will be understood by those skilled in the art that the present invention may be modified and equally replaced, and all technical solutions and modifications thereof that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the present invention.
Claims (8)
1. A data result information quality intelligent detection method based on a task model is characterized in that the method firstly carries out detection task calculation modeling on a plurality of detection task information corresponding to a data result multidimensional quality detection factor based on a quality inspection algorithm so as to establish the task model, the data result multidimensional quality detection factor comprises an integrity detection factor, a format type data detection factor and a consistency detection factor, a plurality of detection tasks corresponding to the integrity detection factor carry out detection task calculation modeling based on a data integrity detection algorithm, a vector data detection task corresponding to the format type data detection factor carries out detection task calculation modeling based on an attribute detection algorithm and a graph detection algorithm, a ownership data detection task corresponding to the format type data detection factor carries out detection task calculation modeling based on a field detection algorithm, the raster data detection task corresponding to the format type data detection factor is subjected to detection task calculation modeling based on a raster data space analysis algorithm, the metadata detection task corresponding to the format type data detection factor carries out detection task calculation modeling based on the detection algorithm of the metadata structure, space and content, the data consistency detection task and the summary table consistency detection task corresponding to the consistency detection factor and including the chart consistency check are based on a consistency detection algorithm to perform detection task calculation modeling, the map attribute consistency checks include a spatial pattern and attribute consistency check and a contract block map attribute consistency check, the space graphics and attribute consistency check requires that the space graphics of each element in the vector layer correspond to the attribute records one by one, the tile codes in the tile information table to be contracted for the contract tile map consistency check only exist in the tile map layer; the task model is in a nested model architecture and is compatible with a plurality of detection task information corresponding to multi-dimensional quality detection factors of various types of data results; acquiring data achievements to be detected and selecting required detection tasks from the associated detection task information according to the content and the data type of the acquired data achievements to form a task group, wherein the required detection tasks are all or part of the detection tasks associated with the data achievements to be detected, the detection tasks in the task group comprise independent tasks and/or at least one stage of cascaded subtask groups, the independent tasks are tasks for realizing a single specific detection function, and the subtask groups are tasks for realizing a plurality of specific detection functions; inputting the task group into the task model, executing all detection tasks in the task group through the task model, and processing the subtask group according to a cascade algorithm when the detection tasks in the task group are at least one stage of cascade subtask group, so as to obtain a detection task calculation result; and then, carrying out data analysis processing on the detection task calculation result and outputting a data result information quality detection report to realize intelligent detection.
2. The intelligent detection method for the information quality of the data result according to claim 1, characterized in that a task group library for storing task groups is further provided; and for the data result to be detected, calling the saved task group from the task group library, acquiring the path of the data result to be detected, acquiring the data result to be detected, selecting the required detection task to realize task parameter configuration to form a new task group, inputting the new task group into the task model, and saving the formed new task group into the task group library.
3. The intelligent detection method for the information quality of the data results according to claim 1, wherein the task model executes all detection tasks in the task group according to a preset priority based on a topological algorithm, and when the detection tasks in the task group are all independent tasks, each independent task is processed according to the priority.
4. The intelligent detection method for the quality of the data result information according to one of claims 1 to 3, characterized in that, when there is quality detection error information in the output data result information quality detection report, the detected data result information is corrected, and the quality detection error information includes attribute error information and topology error information; and the attribute error information is checked through an error list, the topology error information is checked through an ArcGIS map, the detected data result information is corrected according to the checked quality detection error information, and the detection tasks required by the task group are executed by inputting the task model again after correction until the data result information quality detection report is output to indicate that the data result to be detected eliminates the detection errors so as to achieve the detection standard.
5. The system is characterized by comprising a task model creating module, a task configuration module, a task group execution module and a data analysis processing module which are sequentially connected:
the task model creating module carries out detection task calculation modeling on a plurality of detection task information corresponding to the data result multi-dimensional quality detection factors based on a quality inspection algorithm, so as to create a task model; the data result multi-dimensional quality detection factors comprise integrity detection factors, format type data detection factors and consistency detection factors, a plurality of detection tasks corresponding to the integrity detection factors are subjected to detection task calculation modeling based on a data integrity detection algorithm, vector data detection tasks corresponding to the format type data detection factors are subjected to detection task calculation modeling based on an attribute detection algorithm and a graph detection algorithm, ownership data detection tasks corresponding to the format type data detection factors are subjected to detection task calculation modeling based on a field detection algorithm, raster data detection tasks corresponding to the format type data detection factors are subjected to detection task calculation modeling based on a raster data space analysis algorithm, and metadata detection tasks corresponding to the format type data detection factors are subjected to detection task calculation modeling based on detection algorithms of metadata structures, spaces and contents, data consistency detection tasks and summary table consistency detection tasks corresponding to the consistency detection factors, wherein the data consistency detection tasks and the summary table consistency detection tasks comprise consistency detection algorithms for performing detection task calculation modeling, the graph consistency detection comprises space graph and attribute consistency detection and contract block graph consistency detection, space graphs of all elements in a vector layer required by the space graph and attribute consistency detection correspond to attribute records one by one, and block codes in a block information table required by the contract block graph consistency detection exist only in the block layer; the task model is in a nested model architecture and is compatible with a plurality of detection task information corresponding to multi-dimensional quality detection factors of various types of data results;
the task configuration module collects data achievements to be detected and selects required detection tasks from the associated detection task information according to the content and the data type of the collected data achievements to form a task group, the required detection tasks are all or part of the detection tasks in the associated detection task information of the data achievements to be detected, the detection tasks in the task group comprise independent tasks and/or at least one stage of cascade subtask group, and the task group is input into the task model;
the task group execution module executes all detection tasks in the task group through the task model, and when the detection tasks in the task group are at least one stage of cascade subtask group, the subtask group is processed according to a cascade algorithm, so that a detection task calculation result is obtained;
and the data analysis processing module performs data analysis processing on the detection task calculation result and outputs a data result information quality detection report to realize intelligent detection.
6. The system according to claim 5, further comprising a task group library, the task group library being used for storing task groups and connected to the task configuration module, the task configuration module calling a stored task group from the task group library for a data result to be detected and then obtaining a path of the data result to be detected to collect the data result to be detected and select a required detection task to implement task parameter configuration to form a new task group, and inputting the new task group to the task model, and the formed new task group is also stored in the task group library.
7. The system of claim 6, further comprising a storage module and an error determination processing module, both of which are connected to the data analysis processing module, the error determination processing module being further connected to the task group execution module,
the storage module stores a data result information quality detection report output by the data analysis processing module;
the error judgment processing module is used for judging whether quality detection error information exists in the output data result information quality detection report or not, and correcting the detected data result information when the quality detection error information exists, wherein the quality detection error information comprises attribute error information and topology error information; the attribute error information is checked through an error list, the topology error information is checked through an ArcGIS map, the detected data result information is corrected according to the checked quality detection error information, the corrected data result information is returned to the task group execution module to execute the detection tasks required by the task group through re-inputting the task model after correction, and the data result information quality detection report is output to indicate the data result to be detected to eliminate the detection errors so as to achieve the detection standard.
8. The system for intelligently detecting the information quality of the data achievements according to one of claims 5 to 7, wherein the task group execution module executes all detection tasks in the task group through the task model, the task model executes all detection tasks in the task group according to a preset priority based on a topological algorithm, and when all the detection tasks in the task group are independent tasks, each independent task is processed according to the priority, so that a detection task calculation result is obtained.
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