Data warehouse quality detection method, device and electronic equipment

Abstract

The embodiment of the present application provides a data warehouse quality detection method, device and electronic device, which belongs to the field of big data application technology. Among them, the data warehouse quality detection method includes: determining at least one target indicator item of the data warehouse, each target indicator item includes at least one secondary indicator item; using the hierarchical analysis method to determine the weight value of at least one secondary indicator item; according to the weight value of at least one secondary indicator item, determine the weight value of each target indicator item; according to the indicator value, weight value and weight value of at least one secondary indicator item, determine the indicator value of each target indicator item; according to the indicator value and weight value of at least one target indicator item, generate a quality detection report of the data warehouse.

Description

Technical Field

[0001] The present application relates to the field of big data application technology, and in particular to a data warehouse quality detection method, a data warehouse quality detection device and an electronic device. Background Art

[0002] As the big data craze continues to heat up in recent years, people are increasingly aware that the use of data can bring great convenience to their production and life, but it also brings considerable challenges, including: big data security and privacy issues; big data integration and management issues; big data IT technology architecture issues; big data ecological environment issues.

[0003] In related technologies, Big Data Warehouse can usually record information data from a certain point in the past to the current stage. Managers can use this data to make quantitative analysis and predictions on the development process and future trends. Big Data Warehouse is an increasingly popular data solution. As a big data quantification solution, big data problem solving, and data value discovery, big data warehouses are adopted and used by many companies. However, in the big data environment, the construction quality of the data warehouse cannot be measured, and a method needs to be invented to detect the pros and cons of the data warehouse. Summary of the invention

[0004] The embodiments of the present application provide a data warehouse quality detection method, device, electronic device and readable storage medium to solve the problem that the construction quality of the data warehouse cannot be measured.

[0005] In a first aspect, an embodiment of the present application provides a data warehouse quality detection method, including:

[0006] Determine at least one target indicator item of the data warehouse, each target indicator item includes at least one secondary indicator item;

[0007] Using the analytic hierarchy process, determine the weight value of at least one secondary indicator item;

[0008] Determine the weight value of each target indicator item according to the weight value of at least one secondary indicator item;

[0009] Determine the index value of each target index item according to the index value and weight value of at least one secondary index item and the weight value of each target index item;

[0010] Generate a quality inspection report for the data warehouse based on the indicator value and weight value of at least one target indicator item.

[0011] In a second aspect, an embodiment of the present application provides a data warehouse quality detection device, including:

[0012] A determination module, used to determine at least one target indicator item of the data warehouse, each target indicator item including at least one secondary indicator item;

[0013] Using the analytic hierarchy process, determine the weight value of at least one secondary indicator item;

[0014] Determine the weight value of each target indicator item according to the weight value of at least one secondary indicator item;

[0015] Determine the index value of each target index item according to the index value and weight value of at least one secondary index item and the weight value of each target index item;

[0016] The evaluation module is used to generate a quality inspection report of the data warehouse based on the indicator value and weight value of at least one target indicator item.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a program or instruction stored in the memory and running on the processor, which, when executed by the processor, implements the steps of the data warehouse quality inspection method provided in the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the data warehouse quality inspection method provided in the first aspect are implemented.

[0019] In a fifth aspect, an embodiment of the present application provides a chip, the chip including a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the steps of the data warehouse quality inspection method provided in the first aspect.

[0020] In an embodiment of the present application, at least one target indicator item of the data warehouse is determined, and each target indicator item includes at least one secondary indicator item; the weight value of at least one secondary indicator item is determined by using the hierarchical analysis method; the weight value of each target indicator item is determined according to the weight value of at least one secondary indicator item; the index value of each target indicator item is determined according to the index value, weight value and weight value of each target indicator item of at least one secondary indicator item; the quality inspection report of the data warehouse is generated according to the index value and weight value of at least one target indicator item. Thus, the index value and weight value of at least one secondary indicator item in the target indicator item to be evaluated are used to comprehensively evaluate the index value of each target indicator item of the data warehouse, and the overall performance of the data warehouse is analyzed through the index value and weight value of each target indicator item. Then, a measurement system for evaluating the quality of the data warehouse model is established, and a quality inspection report is generated by combing the applicable quantitative indicators. On the one hand, it realizes the quality inspection mechanism of data warehouse, which can evaluate the data warehouse during the construction process of data warehouse, and does not need to conduct manual observation and testing on the performance of data warehouse after the data warehouse is put into use. It not only saves manpower, but also improves the efficiency of data warehouse performance evaluation, and effectively reduces the construction cost of data warehouse. On the other hand, it can unify the evaluation standards and specifications of data warehouse, making the evaluation results of data warehouse more objective and accurate, and providing reliable data support for the establishment and maintenance of data warehouse. On the other hand, since there is no need for subjective judgment, users do not need to fully master the principles of data warehouse construction, data processing and other skills, which is conducive to reducing the difficulty of data warehouse quality inspection and expanding the application scope of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 One of the flow charts of the data warehouse quality detection method according to an embodiment of the present application is shown;

[0022] Figure 2 A second flowchart of a data warehouse quality detection method according to an embodiment of the present application is shown;

[0023] Figure 3 A third flowchart of a data warehouse quality detection method according to an embodiment of the present application is shown;

[0024] Figure 4 A fourth flowchart of a data warehouse quality detection method according to an embodiment of the present application is shown;

[0025] Figure 5 A fifth flowchart of a data warehouse quality detection method according to an embodiment of the present application is shown;

[0026] Figure 6A sixth flowchart of a data warehouse quality detection method according to an embodiment of the present application is shown;

[0027] Figure 7 FIG7 shows a flow chart of a quality detection method for a data warehouse according to an embodiment of the present application;

[0028] Figure 8 FIG8 shows a flowchart of a quality detection method for a data warehouse according to an embodiment of the present application;

[0029] Fig. 9 A ninth flowchart of a method for quality inspection of a data warehouse according to an embodiment of the present application is shown;

[0030] Fig.10 A structural block diagram of a data warehouse quality detection device according to an embodiment of the present application is shown;

[0031] Fig.11 A structural block diagram of an electronic device according to an embodiment of the present application is shown;

[0032] Fig.12 A hardware structure block diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0034] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0035] Refer to the following Figures 1 to 12 A data warehouse quality detection method, a data warehouse quality detection device, an electronic device, and a readable storage medium are described according to some embodiments of the present application.

[0036] In one embodiment of the present application, Figure 1A flowchart of a data warehouse quality detection method according to an embodiment of the present application is shown, including:

[0037] Step 102, determining at least one target indicator item of the data warehouse;

[0038] The target indicator item is the performance evaluation direction of the data warehouse. Each of the at least one target indicator item includes at least one secondary indicator item, that is, a target indicator item can be further subdivided into one or more secondary indicator items.

[0039] For example, as shown in Table 1, taking the application scenario of big data as an example, according to the quality inspection requirements of the use and construction of the big data data warehouse (referred to as the data warehouse), the data warehouse is determined to include five target indicators (evaluation directions): security, efficiency, performance, cost, and ease of use. As shown in Table 2, combined with the evaluation direction of the big data data warehouse, 17 secondary indicators are selected from each direction to reflect the value of the current data warehouse through these secondary indicators.

[0040] It should be noted that in other application scenarios different from big data, the target indicator items and the secondary indicator items contained in the target indicator items may be different from the indicator items recorded in Table 1 and Table 2, and this application does not make specific limitations.

[0041] Table 1

[0042] Evaluation direction Detailed Description cost The computational and storage costs of the data warehouse are reasonable. efficiency Used to control the reuse rate of data warehouse models to improve R&D efficiency Safety Used to control the open use and security level of data warehouse models performance Used to control the performance and quality of data warehouse model-related tasks Ease of use Used to control the ease of use, findability, and readability of data warehouse models

[0043] Table 2

[0044]

[0045]

[0046] Step 104, using the hierarchical analysis method to determine the weight value of at least one secondary indicator item;

[0047] Step 106, determining a weight value of each target indicator item according to a weight value of at least one secondary indicator item;

[0048] Specifically, the calculation method of the weight value of the target indicator item can be reasonably set according to the evaluation and analysis standards of the data warehouse, and the embodiments of the present application do not make specific limitations. For example, the weight value of the target indicator item can be the sum of the weight values ​​of all the secondary indicator items contained in the target indicator item, or the average value. It is also possible to obtain the weight value of any target indicator item by performing a weighted operation on the weight value of each secondary indicator item and the corresponding preset proportion, that is, the weight value of the target indicator item. Among them, the weight values ​​of the secondary indicator items are U1, U2, ..., Un respectively; the preset proportions corresponding to the secondary indicator items are b1, b2, ..., bn respectively, and the preset proportions can be reasonably set according to the user's evaluation requirements for the target indicator items.

[0049] Step 108, determining the index value of each target index item according to the index value and weight value of at least one secondary index item and the weight value of each target index item;

[0050] Among them, the data warehouse is simulated to run for a period of time with preset parameters, and the operating data of the data warehouse during this period is converted into a digital signal that can reflect the performance of the secondary indicator item through the performance conversion rules corresponding to the preset secondary indicator items. The digital signal is the indicator of the secondary indicator item.

[0051] Step 110: Generate a quality inspection report for the data warehouse according to the indicator value and weight value of at least one target indicator item.

[0052] In this embodiment, the index value and weight value of at least one secondary index item in the target index item to be evaluated are used to comprehensively evaluate the index value and weight value of each target index item of the data warehouse, and the overall performance of the data warehouse is evaluated through the index value and weight value of each target index item. Thus, a measurement system for evaluating the quality of the data warehouse model is established, and a quality inspection report is generated by combing the applicable quantitative indicators. On the one hand, a quality inspection mechanism for the data warehouse is realized, and the data warehouse can be evaluated during the construction of the data warehouse. There is no need to conduct artificial observation and testing on the performance of the data warehouse after the data warehouse is put into use, which not only saves manpower, but also improves the performance evaluation efficiency of the data warehouse and effectively reduces the construction cost of the data warehouse. On the other hand, the evaluation standards and specifications of the data warehouse can be unified, so that the evaluation results of the data warehouse are more objective and accurate, and reliable data support is provided for the establishment and maintenance of the data warehouse. On the other hand, since there is no need for artificial subjective judgment, the user does not need to fully master the principles of data warehouse construction, data processing and other skills, which is conducive to reducing the difficulty of data warehouse quality inspection and expanding the application scope of the product.

[0053] Specifically, the quality inspection method of the data warehouse is applicable to electronic devices, including but not limited to mobile terminals, tablet computers, computers, laptops, wearable devices, vehicle-mounted terminals, etc.

[0054] In one embodiment of the present application, Figure 2 The second flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0055] Step 202, normalizing the index value of at least one secondary index item;

[0056] In this embodiment, the indicator values ​​of different dimensions are standardized by normalizing the indicator data of at least one secondary indicator item. The indicator values ​​are scaled proportionally so that all indicator values ​​are mapped to between [0, 1], and the normalized indicator value of at least one secondary indicator item is obtained. Thus, the unit restriction of the data is removed, and the indicator values ​​of different dimensions are converted into dimensionless pure numerical values, which facilitates the comparison and weighting of indicators of different units or magnitudes, thereby ensuring the accuracy of quality detection of the data warehouse.

[0057] Specifically, the extreme value method is used for normalization, that is, Wherein, Y represents the index value after normalization, X represents the index value before normalization, min(a) represents the minimum value of the index value of at least one secondary index item, and max(a) represents the maximum value of the index value of at least one secondary index item.

[0058] Step 204 , after performing weighted calculation on the normalized index value of at least one secondary index item and the weight value of at least one secondary index item, the weight values ​​are divided by the weight values ​​of each target index item to obtain the index value of each target index item.

[0059] In this embodiment, since the target indicator item to be evaluated includes at least one secondary indicator item. In the process of determining the indicator value of the target indicator item, the normalized indicator value of at least one secondary indicator item and the weight value of at least one secondary indicator item are weighted and calculated, and then the weighted values ​​are divided by the weight value of each target indicator item to obtain the indicator value of each target indicator item. In order to utilize indicator data from different data sources and different data dimensions, a more comprehensive and accurate evaluation result can be obtained, which is conducive to scientifically and objectively evaluating the quality of the data warehouse.

[0060] Specifically, the weight value of any target indicator item is the sum of the weight values ​​of all the secondary indicator items of the target indicator item. The indicator values ​​of the secondary indicator items are P1, P2, P3, ..., Pn; the weight values ​​of the secondary indicator items are U1, U2, U3, ..., Un. Among them, n represents the number of secondary indicator items, then the indicator value of the target indicator item is

[0061] In one embodiment of the present application, Figure 3 The third flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0062] Step 302, determining the indicator category of each secondary indicator item;

[0063] Among them, indicator categories include positive indicators, negative indicators and moderate indicators. Positive indicators (benefit indicators) refer to indicators that grow upward or forward. The larger the indicator value of the positive indicator, the better the evaluation, for example, the middle layer reuse ratio, the middle layer coverage ratio, the DM layer table usability, the DW layer table usability, the effective middle layer ratio, the DA layer table usability, the effective ODS table ratio, control of invalid storage, and ODS layer table usability. Negative indicators refer to some indicators that go against the general downward trend. The smaller the indicator value of the positive indicator, the better the evaluation, for example, data output performance, task operation performance, ODS layer sensitive data security, data warehouse fault tolerance performance, control of computing growth, middle layer sensitive data security, and control of storage growth. Moderate indicators are indicators that need to be considered comprehensively, for example, control of storage redundancy.

[0064] Step 304, when the indicator category of the secondary indicator item is a negative indicator or a moderate indicator, the indicator data of the secondary indicator item is processed positively;

[0065] In this embodiment, in the process of normalizing the indicator data of at least one secondary indicator item, since the indicator value trends corresponding to the positive indicator, the negative indicator and the appropriate indicator are different, if the normalization is directly performed, the indicator values ​​of different indicator categories will affect each other, which is not conducive to the accuracy of data processing. Therefore, the indicator values ​​corresponding to the negative indicator and the appropriate indicator are positively processed to convert the negative indicator and the appropriate indicator into positive indicators from the numerical value, thereby unifying the dimensions of different indicator categories, facilitating subsequent data processing and analysis, and ensuring the accuracy of data warehouse detection.

[0066] Specifically, the positive processing of the appropriateness index includes: determining the standard distance according to the standard value, and deriving the standard distance to adjust all the appropriateness indexes to positive indicators. The positive processing of the negative index includes: using the inverse method to process the index value corresponding to the negative index to convert the negative index into positive ones.

[0067] Step 306, normalizing the indicator data corresponding to the positive indicator and the indicator data after the positive processing.

[0068] In this embodiment, after unifying the dimensions of the indicator values ​​of different indicator categories, multiple indicator values ​​are normalized so that all data are mapped between [0, 1]. This removes the unit restriction of the data and converts different indicator data into dimensionless pure values, making it easier for indicators of different units or magnitudes to be compared and weighted.

[0069] It is understandable that, when the indicator category of the secondary indicator item is a positive indicator or a moderate indicator, the indicator data of the secondary indicator item can be negatively processed, and the dimensions can be unified by negative processing, and then the normalization processing of multiple indicator values ​​can be performed. Of course, other methods can also be used to unify the dimensions of different indicator values.

[0070] In one embodiment of the present application, Figure 4 The fourth flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0071] Step 402: performing convergence processing on the normalized index value of at least one secondary index item.

[0072] In this embodiment, after normalizing at least one indicator value, convergence processing may be performed on at least one normalized indicator value, so that all indicator values ​​are further converged to a smaller interval through data convergence, thereby further reducing errors, improving the accuracy of the obtained indicator values, and further improving the reliability of data warehouse quality detection, so as to facilitate the discovery of abnormal problems in the process of establishing or using the data warehouse.

[0073] Specifically, the convergence formula is: Z = r1 + (r2-r1) × Y. Wherein, Z represents the index value after convergence, and r1 and r2 are constants. For example, the index values ​​of multiple secondary index items in the big data scenario are converged to between 0.6 and 0.95 through the above formula.

[0074] In one embodiment of the present application, Figure 5 The fifth flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0075] Step 502, obtaining sample data from the data warehouse;

[0076] The sample data includes a plurality of sample databases, and a plurality of secondary index items of the sample databases, weight values ​​of the secondary index items and corresponding evaluation results are known.

[0077] Step 504, using the hierarchical analysis method, weight processing is performed on the weight values ​​of the secondary index items of multiple sample databases to establish a comparison matrix of the weight values;

[0078] Step 506: Determine a weight value of at least one secondary index item according to the comparison matrix.

[0079] In this embodiment, the analytic hierarchy process (AHP) is used to perform statistical analysis on the weight values ​​of the secondary index items of multiple sample databases, determine the comparison matrix of the weight values ​​as the sample, and then use the comparison matrix to obtain the weight values ​​of the secondary index items used to evaluate the data warehouse. In this way, a unified weight standard is established, and in the process of using the weight value for quality detection, the evaluation results of the data warehouse are made more objective and accurate, providing reliable data support for the establishment and maintenance of the data warehouse. At the same time, users do not need to fully master the knowledge of the principles of data warehouse construction, data processing, etc., to achieve quality detection of the data warehouse, which is conducive to reducing the difficulty of data warehouse quality detection and expanding the application scope of the product.

[0080] In one embodiment of the present application, Figure 6 The sixth flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0081] Step 602, performing weighted calculation on the index value of at least one target index item and the weight value of at least one target index item to obtain a target value;

[0082] In this embodiment, the product of the index value of each target index item and the weight value of the corresponding target index item is calculated respectively, and at least one product is added to obtain the target value. Specifically, the target value Where m represents the number of target indicators.

[0083] Step 604, determining an evaluation result according to the product of the target value and a preset evaluation coefficient;

[0084] The preset evaluation coefficient is a scoring benchmark score. For example, if the evaluation result is based on a percentage system, the preset evaluation coefficient is 100. If the evaluation result is based on a full score of 5, the preset evaluation coefficient is 5. The preset evaluation coefficient can be reasonably set according to the user's evaluation habits, and can also be 10, 50, etc.

[0085] In this embodiment, the preset evaluation coefficient and the target are multiplied to obtain the health of the final data warehouse, that is, the evaluation result. Thus, by analyzing the indicator data of different secondary indicator items, the quality of the data warehouse is quantified, so that users can clearly see the construction results of the data warehouse, which is convenient for the establishment and use of the data warehouse.

[0086] Specifically, the quality evaluation result (healthiness) of the data warehouse is S=Q×v, where v represents a preset evaluation coefficient. The higher the healthiness, the better the data warehouse construction, and vice versa.

[0087] Step 606: Analyze and process the data warehouse based on the evaluation results to generate a quality inspection report.

[0088] In this embodiment, the system pre-stores analysis standards for evaluation results, which indicate the quality levels to which different evaluation results (health) may belong. Each quality level corresponds to a score threshold. When the evaluation result is greater than or equal to the score threshold, it is determined that the evaluation result belongs to the quality level corresponding to the score threshold. A quality inspection report is generated based on the evaluation results, quality levels, and indicator values ​​of target indicator items. The quality inspection report can provide a data visualization tool to quantitatively describe the quality of the data warehouse, so that users can intuitively know whether the data warehouse is abnormal through the quality inspection report, and can assist users in determining the indicator items with abnormalities, reducing the uncertainty and ambiguity of user evaluations, and helping users accurately control the quality of the data warehouse during establishment and use.

[0089] For example, if the final health score is 60 points, it means that it is just passing, not good enough, and the indicator value of the target indicator item can be used to further analyze the direction of possible abnormalities. If the health score is 100 points, it means that the data warehouse is well built and can be put into online use.

[0090] In one embodiment of the present application, Figure 7 The seventh flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0091] Step 702: Optimize the data warehouse according to the quality inspection report.

[0092] In this embodiment, after generating a quality inspection report for the data warehouse based on the index value and weight value of at least one target index item, if it is determined that the health score (evaluation result) in the quality inspection report is lower than the threshold corresponding to the target quality level, indicating that there is a problem with a certain performance of the data warehouse, then the root cause affecting the index is found based on the target index value, and the data warehouse is optimized in a targeted manner. Thus, from the evaluation, analysis, and optimization of data warehouse quality to the prevention of performance problems and the automated processing of performance problems, a comprehensive management system for performance problems is realized, and the product's ability to analyze and solve performance problems is improved.

[0093] In one embodiment of the present application, Figure 8 The eighth flowchart of the data warehouse quality detection method according to the embodiment of the present application is shown, including:

[0094] Step 802: Update the weight value of at least one secondary indicator item according to the quality detection report.

[0095] In this embodiment, after generating a quality inspection report for the data warehouse based on the indicator value and weight value of at least one target indicator item, if it is determined that the health score (evaluation result) in the quality inspection report is lower than the threshold value corresponding to the target quality level, indicating that there is a problem with a certain performance of the data warehouse, the weight value of the secondary indicator item that may have abnormalities is adjusted according to the target indicator value, thereby completing a comprehensive and accurate quality inspection system through a large number of inspection summaries and scientific analyses, so as to improve the accuracy of the next quality inspection, making the quality inspection of the data warehouse more scientific and efficient.

[0096] In one embodiment of the present application, Fig. 9 A ninth flowchart of the data warehouse quality detection method according to an embodiment of the present application is shown, comprising:

[0097] Step 902, obtaining business attribute information of the data warehouse;

[0098] Step 904: Determine at least one target indicator item and at least one secondary indicator item included in each target indicator item according to the business attribute information.

[0099] In this embodiment, the actual application scenario of the data warehouse can be determined based on the business attribute information of the data warehouse, and then the target indicator items and secondary indicator items related to the application scenario can be matched. On the one hand, there is no need to design an independent detection model for a specific scenario, which reduces the manpower and material costs required for quality detection. On the other hand, the performance evaluation of the data warehouse can adapt to different business needs, which is conducive to expanding the application scope of the product.

[0100] In one embodiment of the present application, Fig.10 As shown, the quality inspection device 1000 for a data warehouse includes: a determination module 1002, the determination module 1002 is used to determine at least one target indicator item of the data warehouse, each target indicator item includes at least one secondary indicator item; a hierarchical analysis method is used to determine the weight value of at least one secondary indicator item; a weight value of each target indicator item is determined according to the weight value of at least one secondary indicator item; an indicator value of each target indicator item is determined according to the indicator value, weight value of at least one secondary indicator item and the weight value of each target indicator item; an evaluation module 1004, the evaluation module 1004 is used to generate a quality inspection report for the data warehouse based on the indicator value and weight value of at least one target indicator item.

[0101] In this embodiment, the index value and weight value of at least one secondary index item in the target index item to be evaluated are used to comprehensively evaluate the index value and weight value of each target index item of the data warehouse, and the overall performance of the data warehouse is evaluated through the index value and weight value of each target index item. Thus, a measurement system for evaluating the quality of the data warehouse model is established, and a quality inspection report is generated by combing the applicable quantitative indicators. On the one hand, a quality inspection mechanism for the data warehouse is realized, and the data warehouse can be evaluated during the construction of the data warehouse. There is no need to conduct artificial observation and testing on the performance of the data warehouse after the data warehouse is put into use, which not only saves manpower, but also improves the performance evaluation efficiency of the data warehouse and effectively reduces the construction cost of the data warehouse. On the other hand, the evaluation standards and specifications of the data warehouse can be unified, so that the evaluation results of the data warehouse are more objective and accurate, and reliable data support is provided for the establishment and maintenance of the data warehouse. On the other hand, since there is no need for artificial subjective judgment, the user does not need to fully master the principles of data warehouse construction, data processing and other skills, which is conducive to reducing the difficulty of data warehouse quality inspection and expanding the application scope of the product.

[0102] Optionally, the data warehouse quality inspection device 1000 also includes: a processing module (not shown in the figure), which is used to normalize the indicator data of at least one secondary indicator item; the determination module 1002 is also used to perform weighted calculation on the normalized indicator value of at least one secondary indicator item and the weight value of at least one secondary indicator item, and then divide them by the weight value of each target indicator item to obtain the indicator value of each target indicator item.

[0103] Optionally, the determination module 1002 is also used to determine the indicator category of each secondary indicator item, and the indicator categories include positive indicators, negative indicators and moderate indicators; the processing module is also used to perform positive processing on the indicator data of the secondary indicator item when the indicator category of the secondary indicator item is a negative indicator or a moderate indicator; and normalize the indicator data corresponding to the positive indicator and the indicator data after the positive processing.

[0104] Optionally, the processing module is further used to perform convergence processing on the normalized indicator value of at least one secondary indicator item.

[0105] Optionally, the determination module 1002 is also used to perform weighted calculation on the indicator value of at least one target indicator item and the weight value of at least one target indicator item to obtain the target value; determine the evaluation result based on the product of the target value and a preset evaluation coefficient; the evaluation module 1004 is also used to analyze and process the data warehouse based on the evaluation result to generate a quality inspection report.

[0106] Optionally, the data warehouse quality inspection device 1000 further includes: an optimization module (not shown in the figure), which is used to optimize the data warehouse according to the quality inspection report.

[0107] Optionally, the data warehouse quality detection device 1000 further includes: an updating module (not shown in the figure), the updating module being configured to update a weight value of at least one secondary indicator item according to a quality detection report.

[0108] Optionally, the data warehouse quality inspection device 1000 also includes: a first acquisition module (not shown in the figure), the first acquisition module is used to obtain business attribute information of the data warehouse; a determination module 1002, which is also used to determine at least one target indicator item and at least one secondary indicator item contained in each target indicator item based on the business attribute information.

[0109] Optionally, the data warehouse quality inspection device 1000 also includes: a second acquisition module (not shown in the figure), the second acquisition module is used to obtain sample data of the data warehouse; the processing module is also used to adopt a hierarchical analysis method to perform weight processing on the weight values ​​of secondary indicator items of multiple sample databases, and establish a comparison matrix of weight values; the determination module 1002 is also used to determine the weight value of at least one secondary indicator item based on the comparison matrix.

[0110] In this embodiment, each module of the data warehouse quality inspection device 1000 implements the steps of the data warehouse quality inspection method in any embodiment of the first aspect when performing its respective functions. Therefore, the data warehouse quality inspection device 1000 also includes all the beneficial effects of the data warehouse quality inspection method in any embodiment of the first aspect, which will not be repeated here.

[0111] The quality detection device of the data warehouse in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a palm computer, an in-vehicle electronic device, an intelligent camera device, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and the non-mobile electronic device can be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV) or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0112] The data warehouse quality detection device in the embodiment of the present application can be a device with an operating system. The operating system can be an Android operating system, an iOS operating system, or other operating systems, which are not specifically limited in the embodiment of the present application.

[0113] In one embodiment of the present application, Fig.11 As shown, an electronic device 1100 is provided, including: a processor 1104, a memory 1102, and a program or instruction stored in the memory 1102 and running on the processor 1104. When the program or instruction is executed by the processor 1104, the steps of the data warehouse quality detection method provided in any of the above embodiments are implemented. Therefore, the electronic device 1100 includes all the beneficial effects of the data warehouse quality detection method provided in any of the above embodiments, which will not be repeated here.

[0114] It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

[0115] Fig.12 The hardware structure diagram of an electronic device 1200 for implementing an embodiment of the present application is shown. The electronic device 1200 includes, but is not limited to, a radio frequency unit 1201, a network module 1202, an audio output unit 1203, an input unit 1204, a sensor 1205, a display unit 1206, a user input unit 1207, an interface unit 1208, a memory 1209, and a processor 1210.

[0116] Those skilled in the art will appreciate that the electronic device 1200 may also include a power source (such as a battery) for supplying power to each component, and the power source may be logically connected to the processor 1210 through a power quality detection system, thereby realizing functions such as quality detection of charging, discharging, and power consumption quality detection through the power quality detection system. Fig.12 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0117] Among them, the processor 1210 is used to determine at least one target indicator item of the data warehouse, each target indicator item includes at least one secondary indicator item; use the hierarchical analysis method to determine the weight value of at least one secondary indicator item; determine the weight value of each target indicator item based on the weight value of at least one secondary indicator item; determine the indicator value of each target indicator item based on the indicator value, weight value of at least one secondary indicator item and the weight value of each target indicator item; and generate a quality inspection report for the data warehouse based on the indicator value and weight value of at least one target indicator item.

[0118] In this embodiment, the index value and weight value of at least one secondary index item in the target index item to be evaluated are used to comprehensively evaluate the index value and weight value of each target index item of the data warehouse, and the overall performance of the data warehouse is evaluated through the index value and weight value of each target index item. Thus, a measurement system for evaluating the quality of the data warehouse model is established, and a quality inspection report is generated by combing the applicable quantitative indicators. On the one hand, a quality inspection mechanism for the data warehouse is realized, and the data warehouse can be evaluated during the construction of the data warehouse. There is no need to conduct artificial observation and testing on the performance of the data warehouse after the data warehouse is put into use, which not only saves manpower, but also improves the performance evaluation efficiency of the data warehouse and effectively reduces the construction cost of the data warehouse. On the other hand, the evaluation standards and specifications of the data warehouse can be unified, so that the evaluation results of the data warehouse are more objective and accurate, and reliable data support is provided for the establishment and maintenance of the data warehouse. On the other hand, since there is no need for artificial subjective judgment, the user does not need to fully master the principles of data warehouse construction, data processing and other skills, which is conducive to reducing the difficulty of data warehouse quality inspection and expanding the application scope of the product.

[0119] Furthermore, the processor 1210 is also used to normalize the indicator data of at least one secondary indicator item; after weighted calculation, the normalized indicator value of at least one secondary indicator item and the weight value of at least one secondary indicator item are divided by the weight value of each target indicator item respectively to obtain the indicator value of each target indicator item.

[0120] Furthermore, the processor 1210 is also used to determine the indicator category of each secondary indicator item, which indicator categories include positive indicators, negative indicators and moderate indicators; when the indicator category of the secondary indicator item is a negative indicator or a moderate indicator, the indicator data of the secondary indicator item is positively processed; and the indicator data corresponding to the positive indicator and the indicator data after the positive processing are normalized.

[0121] Furthermore, the processor 1210 is further configured to perform convergence processing on the normalized index value of at least one secondary index item.

[0122] Furthermore, the processor 1210 is also used to perform weighted calculation on the indicator value of at least one target indicator item and the weight value of at least one target indicator item to obtain a target value; determine an evaluation result based on the product of the target value and a preset evaluation coefficient; and analyze and process the data warehouse based on the evaluation result to generate a quality inspection report.

[0123] Furthermore, the processor 1210 is further configured to optimize the data warehouse according to the quality detection report.

[0124] Furthermore, the processor 1210 is further configured to update a weight value of at least one secondary indicator item according to the quality detection report.

[0125] Furthermore, the processor 1210 is also used to obtain business attribute information of the data warehouse; and determine at least one target indicator item and at least one secondary indicator item included in each target indicator item according to the business attribute information.

[0126] Furthermore, the processor 1210 is also used to obtain sample data from the data warehouse; use hierarchical analysis method to perform weight processing on the weight values ​​of secondary indicator items of multiple sample databases to establish a comparison matrix of weight values; and determine the weight value of at least one secondary indicator item based on the comparison matrix.

[0127] It should be understood that in the embodiment of the present application, the input unit 1204 may include a graphics processor (Graphics Processing Unit, GPU) 1241 and a microphone 1242, and the graphics processor 1241 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 1206 may include a display panel 1261, and the display panel 1261 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1207 includes a touch panel 1271 and other input devices 1272. The touch panel 1271 is also called a touch screen. The touch panel 1271 may include two parts: a touch detection device and a touch controller. Other input devices 1272 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here. The memory 1209 can be used to store software programs and various data, including but not limited to applications and operating systems. The processor 1210 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 1210.

[0128] In one embodiment of the present application, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the data warehouse quality detection method provided in any of the above embodiments are implemented.

[0129] In this embodiment, reading the storage medium can implement each process of the data warehouse quality detection method provided in the embodiment of the present application, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0130] The processor is the processor in the communication device in the above embodiment. The read storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0131] An embodiment of the present application also provides a chip, which includes a processor and a communication interface, wherein the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned data warehouse quality inspection method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0132] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-level chip, a system chip, a chip system, or a system-on-chip chip, etc.

[0133] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0135] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A data warehouse quality inspection method, characterized in that: include: Determine at least one target indicator item of the data warehouse, each target indicator item including at least one secondary indicator item; Using the hierarchical analysis method, determine the weight value of the at least one secondary indicator item; Determining a weight value of each target indicator item according to a weight value of the at least one secondary indicator item; The data warehouse is simulated and operated with preset parameters, and the operating data is converted into an indicator value of each secondary indicator item according to the operating data of the data warehouse and the preset performance conversion rules corresponding to each secondary indicator item; Normalizing the index value of the at least one secondary index item; Performing convergence processing on the normalized index value of the at least one secondary index item; Determine the index value of each target index item according to the index value and weight value of the at least one secondary index item and the weight value of each target index item; A quality inspection report of the data warehouse is generated according to the indicator value and weight value of the at least one target indicator item.

2. The data warehouse quality detection method according to claim 1, characterized in that: The step of determining the index value of each target index item according to the index value and weight value of the at least one secondary index item and the weight value of each target index item comprises: After weighted calculation is performed on the normalized index value of the at least one secondary index item and the weight value of the at least one secondary index item, the index value of each target index item is obtained by dividing the weight value of each target index item respectively.

3. The data warehouse quality inspection method according to claim 2, characterized in that: The normalizing the index value of the at least one secondary index item includes: Determine the indicator category of each of the secondary indicator items, wherein the indicator categories include positive indicators, negative indicators and moderate indicators; When the indicator category of the secondary indicator item is the negative indicator or the moderate indicator, the indicator value of the secondary indicator item is positively processed; The index value corresponding to the positive index and the index value after the positive processing are normalized.

4. The data warehouse quality detection method according to any one of claims 1 to 3, characterized in that: Generating a quality inspection report of the data warehouse according to the indicator value and weight value of the at least one target indicator item includes: Performing weighted calculation on the index value of the at least one target index item and the weight value of the at least one target index item to obtain a target value; Determining an evaluation result according to the product of the target value and a preset evaluation coefficient; According to the evaluation results, the data warehouse is analyzed and processed to generate the quality inspection report.

5. The data warehouse quality detection method according to any one of claims 1 to 3, characterized in that: The determining of at least one target indicator item of the data warehouse includes: Acquiring business attribute information of the data warehouse; According to the business attribute information, the at least one target indicator item and the at least one secondary indicator item included in each target indicator item are determined.

6. A data warehouse quality inspection device, characterized in that: include: A determination module, used to determine at least one target indicator item of the data warehouse, each target indicator item including at least one secondary indicator item; Using the hierarchical analysis method, determine the weight value of the at least one secondary indicator item; Determining a weight value of each target indicator item according to a weight value of the at least one secondary indicator item; The data warehouse is simulated and operated with preset parameters, and the operating data is converted into an indicator value of each secondary indicator item according to the operating data of the data warehouse and the preset performance conversion rules corresponding to each secondary indicator item; A processing module, used for normalizing the index value of the at least one secondary index item; Performing convergence processing on the normalized index value of the at least one secondary index item; The determination module is further configured to determine the index value of each target index item according to the index value and weight value of the at least one secondary index item and the weight value of each target index item; The evaluation module is used to generate a quality inspection report of the data warehouse according to the indicator value and weight value of the at least one target indicator item.

7. The data warehouse quality inspection device according to claim 6, characterized in that: The determination module is further used to perform weighted calculation on the normalized index value of the at least one secondary index item and the weight value of the at least one secondary index item, and then divide them by the weight value of each target index item to obtain the index value of each target index item.

8. The data warehouse quality inspection device according to claim 7, characterized in that: The determination module is further used to determine the indicator category of each secondary indicator item, wherein the indicator category includes positive indicators, negative indicators and moderate indicators; The processing module is further configured to, when the indicator category of the secondary indicator item is the negative indicator or the moderate indicator, perform positive processing on the indicator value of the secondary indicator item; The index value corresponding to the positive index and the index value after the positive processing are normalized.

9. The data warehouse quality detection device according to any one of claims 6 to 8, characterized in that: Also includes: The determination module is further configured to perform weighted calculation on the index value of the at least one target index item and the weight value of the at least one target index item to obtain a target value; Determining an evaluation result according to the product of the target value and a preset evaluation coefficient; The evaluation module is also used to analyze and process the data warehouse according to the evaluation results to generate the quality inspection report.

10. The data warehouse quality detection device according to any one of claims 6 to 8, characterized in that: Also includes: An acquisition module, used to acquire business attribute information of the data warehouse; The determination module is used to determine the at least one target indicator item and the at least one secondary indicator item included in each target indicator item according to the business attribute information.

11. An electronic device, characterized in that: It includes a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the data warehouse quality detection method as described in any one of claims 1 to 5.

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