CN114331097A - Object operation monitoring method, computer equipment and computer storage medium - Google Patents
Object operation monitoring method, computer equipment and computer storage medium Download PDFInfo
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Abstract
The embodiment of the application discloses an object operation monitoring method, computer equipment and a computer storage medium. The embodiment of the application comprises the following steps: the growth index value of each analysis object in the analysis object group is calculated, the growth index value is the weighted sum of index data of a plurality of primary indexes of the analysis objects, the index data of the primary indexes is the weighted sum of index data of a plurality of secondary indexes, the growth index value of the analysis object group can be determined according to the growth index values of the plurality of analysis objects in the analysis object group, and the growth index value of the analysis object group can indicate the development status of the large environment where the analysis objects are located. Therefore, data analysis is not limited to data of a single individual any more, the development status of the large environment can be comprehensively analyzed by combining data of a plurality of individuals, the data analysis result is more objective and comprehensive, the development status of the large environment can be reflected, and personnel can make behavior decisions by combining the development status of the large environment.
Description
Technical Field
The embodiment of the application relates to the field of data analysis, in particular to an object operation monitoring method, computer equipment and a computer storage medium.
Background
The data analysis means that the collected data are calculated by adopting a statistical method to obtain the change trend and index condition of the collected data. Through data analysis of a specific object, the development trend and the development condition of the object can be obtained, and then the behavior strategy can be adjusted according to the development trend and the development condition of the object. For example, the development trend and the change situation of the enterprise on the operation condition can be obtained by analyzing the operation data of the enterprise, and the development trend and the change situation of the operation condition of the enterprise can guide an enterprise manager to the operation strategy of the enterprise, so that the operation strategy of the enterprise can be ensured to promote the development of the enterprise.
However, when data analysis is performed, the related technical solutions perform data analysis only based on independent individual internal data, and the data source for analysis is single, so that the data analysis result is simple and isolated, the operation process of the object cannot be effectively monitored, the formulation of the behavior strategy cannot be objectively and comprehensively guided, and the data analysis effect is poor.
Disclosure of Invention
The embodiment of the application provides an object operation monitoring method, computer equipment and a computer storage medium, which are used for carrying out data analysis on an analysis object so as to enable a data analysis result to be more objective and comprehensive, thereby realizing effective monitoring.
A first aspect of an embodiment of the present application provides an object operation monitoring method, where the method includes:
acquiring data in the operation process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, wherein the secondary index group comprises index data of a plurality of secondary indexes;
calculating the weighted sum of the index data of a plurality of secondary indexes of the secondary index group to obtain the index data of the primary index;
calculating the weighted sum of index data of a plurality of primary indexes of the analysis object to obtain a growth index value of the analysis object;
determining a set of analysis objects, the set of analysis objects comprising a plurality of the analysis objects;
and determining the growth index value of the analysis object group according to the growth index values of a plurality of analysis objects in the analysis object group.
A second aspect of embodiments of the present application provides a computer device, including:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring data in the running process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, and the secondary index group comprises index data of a plurality of secondary indexes;
the first calculation unit is used for calculating the weighted sum of the index data of the secondary indexes of the secondary index group to obtain the index data of the primary index;
the second calculation unit is used for calculating the weighted sum of index data of the primary indexes of the analysis object to obtain a growth index value of the analysis object;
a first determination unit configured to determine an analysis object group including a plurality of the analysis objects;
a second determination unit configured to determine a growth index value of the analysis target group based on growth index values of a plurality of analysis targets of the analysis target group.
A third aspect of embodiments of the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the method of the foregoing first aspect when executing the computer program.
A fourth aspect of embodiments of the present application provides a computer storage medium having instructions stored therein, which when executed on a computer, cause the computer to perform the method of the first aspect.
A fifth aspect of embodiments of the present application provides a computer program product, which when run on a computer device, causes the computer device to perform the method of the first aspect.
According to the technical scheme, the embodiment of the application has the following advantages:
in the embodiment of the present application, a growth index value of each analysis object in the analysis object group is calculated, where the growth index value is a weighted sum of index data of a plurality of primary indexes of the analysis object, the index data of the primary indexes is a weighted sum of index data of a plurality of secondary indexes, the growth index value of the analysis object group can be determined according to the growth index values of the plurality of analysis objects in the analysis object group, and the growth index value of the analysis object group can indicate a current development situation of a large environment in which the analysis object is located. Therefore, data analysis is not limited to data of a single individual any more, the development status of the large environment can be comprehensively analyzed by combining data of a plurality of individuals, the data analysis result is more objective and comprehensive, and the development status of the large environment can be reflected, so that the operation process of an analysis object can be effectively monitored, and personnel can make behavior decisions by combining the development status of the large environment.
Drawings
Fig. 1 is a schematic flow chart of a method for monitoring object operation in an embodiment of the present application;
fig. 2 is another schematic flow chart of the object operation monitoring method in the embodiment of the present application;
FIG. 3 is a diagram illustrating a correspondence relationship between a random consistency index and a rank of a determination matrix according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram illustrating a trend of growth index value changes of analysis object groups corresponding to different industry ranges in the embodiment of the present application;
fig. 5 is a system diagram of a large-data-based operation monitoring system for a medium-sized and small enterprise, which implements the object operation monitoring method in the embodiment of the present application;
FIG. 6 is a schematic structural diagram of a computer device in an embodiment of the present application;
fig. 7 is a schematic structural diagram of another computer device in the embodiment of the present application.
Detailed Description
The embodiment of the application provides an object operation monitoring method, computer equipment and a computer storage medium, which are used for carrying out data analysis on an analysis object so as to enable a data analysis result to be more objective and comprehensive.
Referring to fig. 1, an embodiment of a method for monitoring object operation in the embodiment of the present application includes:
101. acquiring data in the operation process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, wherein the secondary index group comprises index data of a plurality of secondary indexes;
the method of the embodiment is applicable to a computer device, which may be a server or a terminal. When the computer device is a terminal, the computer device can be a Personal Computer (PC), a desktop computer, or other terminal device; when the computer device is a server, the computer device may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud database, cloud computing, a big data and artificial intelligence platform, and the like.
The computer equipment can analyze the data of the analysis object and monitor the operation process of the analysis object, so that personnel can know the condition and the development trend of the analysis object according to the data analysis result. The data of the analysis object may be data representing the development condition or development trend of the analysis object, and may be index data of an evaluation index of the analysis object. The object to be analyzed may be any type of object, such as an organization or an individual, or may be an object to be analyzed, and when the object to be analyzed is an object, such as an object requiring growth trend evaluation, for example, crops, livestock and poultry, the object to be analyzed may be an economic entity such as an enterprise, an individual manager, and the like.
When data analysis is needed, the computer equipment acquires data of the operation process of the analysis object so as to obtain a secondary index group corresponding to a primary index of the analysis object, the secondary index group comprises index data of a plurality of secondary indexes, namely the primary index corresponds to the plurality of secondary indexes, and the secondary index of the analysis object can be obtained to perform data analysis.
102. Calculating the weighted sum of the index data of a plurality of secondary indexes of the secondary index group to obtain the index data of the primary index;
the computer device may give a weight to the index data of each secondary index, and calculate a weighted sum of the index data of the plurality of secondary indexes of the secondary index group, the calculated weighted sum being the index data of the primary index. Therefore, the weight of the index data of the secondary index can be considered to represent the degree of influence on the index data of the primary index.
103. Calculating the weighted sum of the index data of a plurality of primary indexes of the analysis object to obtain a growth index value of the analysis object;
when the analysis object can adopt a plurality of primary indexes for comprehensive evaluation, the index data of each primary index can be respectively calculated according to the steps. Furthermore, the index data of each primary index may be weighted, a weighted sum of the index data of a plurality of primary indexes may be calculated, and the calculated weighted sum may be used as a growth index value of the analysis target, which may be used to evaluate the development tendency and change situation of the analysis target. Similarly, the weight of the index data of the primary index may be considered to represent the magnitude of the influence on the growth index value.
In some embodiments, the growth index value may be positively correlated with the development trend of the analysis object, i.e., the larger the growth index value, the better the development trend; the growth index value may also be negatively correlated with the trend of the analysis subject, i.e., the greater the growth index value, the worse the trend. Depending on the index system employed.
In some embodiments, the growth index value of the analysis object may be displayed in a human-computer interaction interface, which facilitates monitoring of the development trend of the analysis object.
104. Determining an analysis object group, wherein the analysis object group comprises a plurality of analysis objects;
when data analysis is carried out, the related technical scheme only carries out data analysis based on independent individual internal data, only collects one individual internal data for analysis, has a single data source for analysis, leads to simple and isolated data analysis results, cannot combine the development status and the change trend of the large environment where the individual is located for comprehensive analysis, cannot objectively and comprehensively guide the formulation of behavior strategies, and has poor data analysis effect. In order to solve this problem, the present embodiment determines an analysis object group where an analysis object is located, and further performs data analysis according to growth index values of a plurality of analysis objects in the analysis object group, thereby avoiding that the data analysis is limited to only data of a single individual.
105. Determining growth index values of the analysis object group according to the growth index values of the plurality of analysis objects of the analysis object group;
after determining the analysis object group, each analysis object in the group may be calculated to obtain a growth index value based on the above steps. Therefore, when the development trend and the current situation of the large environment where the analysis object is located need to be comprehensively evaluated, the index value capable of representing the development trend and the development situation of the analysis object group can be determined according to the growth index values of the plurality of analysis objects of the analysis object group based on a statistical method, and the index value can be used as the growth index value of the analysis object group, so that a data analyst can determine the development trend and the development situation of the analysis object group according to the indication of the growth index value of the analysis object group.
In this embodiment, a growth index value of each analysis object in the analysis object group is calculated, where the growth index value is a weighted sum of index data of a plurality of primary indexes of the analysis object, the index data of the primary indexes is a weighted sum of index data of a plurality of secondary indexes, the growth index value of the analysis object group can be determined according to the growth index values of the plurality of analysis objects in the analysis object group, and the growth index value of the analysis object group can indicate a current development situation of a large environment in which the analysis object is located. Therefore, data analysis is not limited to data of a single individual any more, the development status of the large environment can be comprehensively analyzed by combining data of a plurality of individuals, the data analysis result is more objective and comprehensive, the development status of the large environment can be reflected, and personnel can make behavior decisions by combining the development status of the large environment.
In some embodiments, the growth index value of the analysis object group may be displayed in a human-computer interaction interface, which facilitates monitoring of the development trend of the analysis object group.
The embodiments of the present application will be described in further detail below on the basis of the aforementioned embodiment shown in fig. 1. Referring to fig. 2, another embodiment of the method for monitoring the operation of an object in the embodiment of the present application includes:
201. performing data cleaning on index data of a secondary index of the analysis object;
the data used for data analysis may be obtained by external data obtaining and internal data obtaining, where the external data obtaining may be, for example, that the computer device is in butt joint with a data platform of an analysis object to receive data transmitted by the data platform, or that data disclosed by a website platform is obtained by data crawling, or that data is obtained by data purchasing. The internal data acquisition may be, for example, sharing data of the analysis target with an internal data bin, and further acquiring data shared by the analysis target from the internal data bin. The present embodiment does not limit the data acquisition path and manner.
In this embodiment, the computer device may perform data cleaning on the index data of the secondary index of the analysis object to remove erroneous data, invalid data, and the like.
In some embodiments, the data cleaning may be performed by performing data cleaning on the index data of the secondary index according to a data distribution and a data meaning of the index data of the secondary index. For example, the index data of the secondary index is subjected to data cleaning according to the data distribution condition of the index data of the secondary index, such as calculating the variance or standard deviation of the index data of the secondary index, determining the dispersion degree of the data according to the size of the variance or standard deviation, and if the dispersion degree of the data is too high (such as exceeding a dispersion degree threshold), rejecting the extreme value in the index data of the secondary index to ensure that the index data of the secondary index can reflect the overall change trend of the analysis object; or, the index data of the secondary indexes are arranged from large to small or from small to large, and the data smaller than 5% of the quantile value or the data larger than 95% of the quantile value are determined from the array obtained by arrangement and removed.
The data cleaning of the index data of the secondary index is performed according to the data meaning of the index data of the secondary index, for example, each financial data has a unique meaning for the financial data, the meaning of the financial data determines the specification (such as a numerical range) of the representation form of the financial data, the value cannot be less than 0 if the meaning of business income determines that the value is not less than 0, and if a certain business income data is less than 0, the data does not accord with the financial specification and should be determined as an abnormal value and removed.
In some embodiments, the data cleansing may be performed by removing the index data of the secondary index of the analysis object whose data collection period is less than the preset period from the plurality of analysis objects, and retaining the index data of the secondary index of the analysis object whose data collection period is not less than the preset period. Taking a financial scenario as an example, the data collection period number may be a billing period number of an account set, the preset period number may be 9 periods in consideration of continuous requirements of billing, and index data of a secondary index of an analysis object whose billing period number is less than 9 periods in the past 12 months may be removed from a plurality of analysis objects.
Therefore, invalid data, wrong data and data which do not meet the standard or requirement in the index data of the secondary index can be removed through the various data cleaning operations, and the analysis result obtained in the subsequent data analysis process is more accurate, objective and real.
202. Acquiring data in the operation process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, wherein the secondary index group comprises index data of a plurality of secondary indexes;
in this embodiment, the primary index of the analysis object may be commonly influenced by a plurality of secondary indexes, that is, the index data of the plurality of secondary indexes commonly influence the index data of the primary index. For example, the primary indicators used to evaluate the development level of the enterprise may be business scale, risk resistance, profitability, innovation resistance, cost effectiveness, business growth, etc., wherein the secondary indicators corresponding to the business scale indicators may include business income, total assets, cumulative add-on of fixed assets (original value), inventory, etc.; the secondary indexes corresponding to the risk resistance indexes can comprise asset liability rate, snap rate, accounts receivable turnover days, accounts payable turnover days and the like; the secondary indexes corresponding to the profitability indexes can comprise net profit rate, business profit rate, asset profit rate and the like; the secondary indexes corresponding to the innovation capability indexes can comprise a research and development cost input-business income ratio, a research and development cost increase rate, a business cost-business income ratio and the like; the secondary indexes corresponding to the cost-benefit indexes can comprise fixed cost (payroll cost + house renting water and electricity), total cost ratio, (sales cost + management cost + financial cost) revenue-income ratio and the like; the secondary indexes corresponding to the business growth indexes may include a total asset growth rate, a fixed asset growth rate, a net asset growth rate, a profit sum (pre-tax profit) growth rate, a main business revenue growth rate, and the like.
After determining a plurality of secondary indexes corresponding to the primary indexes, the computer device may obtain index data of the plurality of secondary indexes corresponding to the primary indexes, the index data of the plurality of secondary indexes constituting the secondary index group.
The step 201 and the step 201 are not necessarily distinguished in order of execution, and for example, the index data of the secondary index may be obtained first, and then the index data of the secondary index is cleaned; or the index data of the secondary indexes is firstly cleaned, then the index data of the secondary indexes which finish the cleaning of the data is stored in a data warehouse, and when the data analysis is needed, the index data of the secondary indexes is obtained from the data warehouse. Therefore, the present embodiment does not limit the execution order of these two steps.
203. Calculating the weighted sum of the index data of a plurality of secondary indexes of the secondary index group to obtain the index data of the primary index;
in this embodiment, the weight of the index data of each secondary index in the secondary index group corresponding to the primary index may be set in any manner, for example, a person may set the weight according to the importance degree of the secondary index. In some embodiments, the weight of the index data of each secondary index may also be automatically determined, that is, a determination matrix is constructed according to the relative importance between two secondary indexes of a secondary index group, wherein the relative importance between two secondary indexes may be determined according to the sauty 1-9 scaling method, the maximum characteristic root λ of the determination matrix is calculated, the maximum characteristic root λ of the determination matrix and the order n of the determination matrix are substituted into a consistency index calculation formula to calculate a consistency index CI, a check coefficient CR is calculated, the check coefficient CR is a ratio between the consistency index CI and a random consistency index RI, the random consistency index RI is determined according to the order n of the determination matrix, and when the check coefficient is smaller than a preset threshold, the weight of the index data of the two secondary indexes is determined according to a feature vector corresponding to the maximum characteristic root of the determination matrix, further, the weighted sum of the index data of the plurality of secondary indexes may be calculated based on the weights of the index data of the plurality of secondary indexes, for example, by adding the products of the index data of the plurality of secondary indexes of the secondary index group and the weights, the weighted sum of the index data of the plurality of secondary indexes of the secondary index group may be obtained.
Wherein, the consistency index calculation formula can be
The preset threshold may be 0.1, and when the checking coefficient CR is less than 0.1, the degree of inconsistency of the determination matrix may be considered to be within the allowable range, and satisfactory consistency may be obtained, and it may be confirmed that the determination matrix passes the consistency check.
The Saaty1-9 scale is used to determine relative importance as shown in Table 1:
TABLE 1
The random consistency index RI may be determined according to the corresponding relationship between the random consistency index RI and the rank n of the determination matrix shown in fig. 3, for example, when the rank n of the determination matrix is 4, the value of the corresponding random consistency index RI is 0.90.
For example, taking the two-level indexes corresponding to the business scale index as the first-level index as an example to describe the calculation process of the index data of the business scale index, a determination matrix can be constructed according to the business income, the cumulative increment of the total assets and the fixed assets (original values), and the relative importance of every two of the 4 two-level indexes of the stock, so as to obtain a determination matrix a shown as follows:
the column headings of the determination matrix are business income, total assets, the accumulated increment of fixed assets (original value) and inventory from left to right, and correspondingly, the column headings are business income, total assets, the accumulated increment of fixed assets (original value) and inventory from top to bottom.
After the judgment matrix A is obtained, the maximum characteristic root lambda of the judgment matrix A can be calculated, the maximum characteristic root lambda and the order number n (n is 4) of the judgment matrix are substituted into a consistency index calculation formula, a consistency index CI is calculated, the numerical value of a random consistency index RI is further determined to be 0.90, a check coefficient CR is calculated, the check coefficient CR is determined to be smaller than a preset threshold value, a characteristic vector corresponding to the maximum characteristic root of the judgment matrix is used as the weight of the index data of each secondary index, the index data of each secondary index is multiplied by the corresponding weight to obtain the product corresponding to each secondary index, the products corresponding to the 4 secondary indexes are added, and the obtained sum value is used as the index data of the operation scale index.
By analogy, index data of other first-level indexes can be calculated. When the index data of the plurality of first-level indexes is obtained, the index data of the plurality of first-level indexes may be ranked, for example, the index data of the plurality of first-level indexes are arranged in an increasing or decreasing order of numerical value, 25% quantiles, 50% quantiles and 75% quantiles are determined according to the numerical value queue obtained by the arrangement, the first-level index having a numerical value of less than 25% quantile of the index data may be rated as a first-level index, the first-level index having a numerical value of between 25% quantile and 50% quantile of the index data may be rated as a second-level index, the first-level index having a numerical value of between 50% quantile and 75% quantile of the index data may be rated as a third-level index, and the first-level index having a numerical value of more than 75% quantile of the index data may be rated as a fourth-level index. Therefore, by rating the primary indexes according to the index data, the advantages and the disadvantages of the development status of the analysis object can be determined, and personnel can conveniently adjust the behavior strategy according to the advantages and the disadvantages of the development status of the analysis object.
204. Calculating the weighted sum of the index data of a plurality of primary indexes of the analysis object to obtain a growth index value of the analysis object;
in the present embodiment, the weight of the index data of the primary index may be set in any manner, and for example, a person may set the weight according to the importance level of the primary index, or the weight of the index data of the primary index may be automatically determined by an operation similar to the above-described manner of automatically determining the weight of the index data of the secondary index.
For example, the primary indexes used for evaluating the development level of the enterprise may be business scale, risk resistance, profitability, innovation capability, cost effectiveness, business growth, and the like, and the corresponding index data is obtained by calculating each primary index, and the corresponding index data is multiplied by the respective weight to obtain the product corresponding to each primary index, the products corresponding to the 6 primary indexes are added to obtain a sum, which is used as a growth index value of the enterprise, and the growth index value may be used for evaluating the current development situation and trend of the enterprise.
In this embodiment, the number of levels of the index used for evaluating the development condition of the analysis object is not limited, and may be, for example, two-level indexes, that is, a primary index and a secondary index; the index may be an index of more than two levels, that is, a third-level index, a fourth-level index, and the like may be below the second-level index. Taking an analysis object as an enterprise example, when the indexes are only 2 levels, the secondary indexes can be indexes actually generated in the operation process of the enterprise, such as original indexes of business cost, asset liability rate, net profit and the like, and the index data of the primary indexes can be calculated according to the index data of the secondary indexes; when the indexes have 3 levels, the three-level indexes are actually generated indexes in the operation process of the enterprise, such as original indexes of business cost, asset liability rate, net profit and the like, the index data of the second-level indexes can be obtained by calculation according to the index data of the third-level indexes, the index data of the same first-level indexes can be obtained by calculation according to the index data of the second-level indexes, and the like. Therefore, no matter how many levels of indexes are set, the index data of the index at the lowest level can be the original index data of the index actually generated in the operation process of the enterprise, the original index data can be used as a data base for evaluating the development condition of the enterprise, the index data of the index at the higher level is calculated based on the data base, and finally the growth index value of the enterprise can be calculated.
205. Determining an analysis object group, wherein the analysis object group comprises a plurality of analysis objects;
in this embodiment, there are various ways of determining the analysis object group. In an embodiment, a computer device may determine N candidate analysis objects satisfying a preset range from among a plurality of candidate analysis objects, where the preset range is obtained by dividing based on a preset dimension, N is an integer greater than 1, and may determine a plurality of hierarchies by layering the N candidate analysis objects, where each hierarchy includes a plurality of candidate analysis objects, further extract a target number of candidate analysis objects from the plurality of candidate analysis objects of a target hierarchy, and form an analysis object group according to the candidate analysis objects extracted from each hierarchy of the plurality of hierarchies, where the target hierarchy represents any one hierarchy of the plurality of hierarchies.
The preset dimension may be any dimension that can be used as a basis for dividing the candidate analysis object, for example, when the candidate analysis object is an enterprise, the preset dimension may be a region dimension, an industry dimension, or the like, and when the candidate analysis object is divided according to the region dimension, different region ranges may be determined; when dividing according to industry dimension, different industry ranges can be determined.
For example, when dividing according to industry dimensions, a plurality of different industry ranges such as wholesale retail industry, business service industry, science and technology service industry, manufacturing industry and the like can be determined, and further, candidate analysis objects meeting the industry ranges can be determined from a plurality of candidate analysis objects respectively.
The N candidate analysis objects may be layered, and this manner may be that target index data of each candidate analysis object in the N candidate analysis objects is used as a sample point, K centroids are determined, where K is an integer greater than 1, a distance between each sample point and each centroid is respectively calculated, each sample point is determined as a sample point corresponding to the centroid closest to the sample point, a new centroid is determined according to a plurality of sample points corresponding to each centroid, the step of calculating the distance between each sample point and each centroid is performed until a termination condition is satisfied, K clusters are obtained, a candidate analysis object corresponding to a sample point in any one cluster is used as a candidate analysis object of a hierarchy corresponding to the any one cluster, and different clusters correspond to different hierarchies, that is, K hierarchies are separated from the N candidate analysis objects.
The target index data as the sample point is data related to an index of the candidate analysis object, and may be any index data. For example, when the analysis target is an enterprise, since the logarithm of the revenue follows a normal distribution, the target index data as the sample point may be the logarithm of the revenue.
The termination condition may be arbitrarily set, for example, no or a minimum number of sample points may be reassigned to different centroids; alternatively, no or a minimal number of centroids are changed; it is also possible to reach the number of iterations, etc.
After the hierarchical layers are completed, a certain number of candidate analysis objects may be extracted for each hierarchical layer, and an analysis object group may be formed according to all the extracted candidate analysis objects. For example, sample unit allocation is performed according to the total number of candidate analysis objects of the target hierarchy, the standard deviation of the target hierarchy, and N to obtain the target number corresponding to the target hierarchy, and the target number is extracted from a plurality of candidate analysis objects of the target hierarchyAnd outputting the target number of the alternative analysis objects. For example, for a target hierarchy in the plurality of hierarchies (assuming that the target hierarchy is the nth layer in the plurality of hierarchies), the total number N of candidate analysis objects of the target hierarchy may be setnStandard deviation S of the target levelnAnd substituting N into the distribution formula to calculate the target number corresponding to the target level.
Wherein, the allocation formula is as follows:
therefore, the target number required to be extracted for each level can be calculated through the distribution formula, the target number of candidate analysis objects are extracted from the plurality of candidate analysis objects of each level, and an analysis object group is formed according to all the extracted candidate analysis objects.
206. Determining growth index values of the analysis object group according to the growth index values of the plurality of analysis objects of the analysis object group;
the computer device may calculate a growth index value corresponding to each analysis object in the analysis object group, and determine the growth index value of the analysis object group according to the growth index values of the plurality of analysis objects in the analysis object group. In one embodiment, the growth index values of the plurality of analysis objects in the analysis object group may be arranged in an increasing or decreasing order of numerical values, a median value may be determined from the numerical value queue obtained by the arrangement, and the median value may be determined as the growth index value of the analysis object group.
For example, following the above example of dividing the analysis object according to the industry dimension, each industry range corresponds to one enterprise group, the growth index value of each enterprise in each enterprise group can be calculated, and then a plurality of growth index values corresponding to each enterprise group are arranged according to the ascending or descending order of the numerical value, a median value is determined from the numerical value queue obtained by arrangement, and the median value is determined as the growth index value of the enterprise group. Therefore, the growth index values of the enterprise groups corresponding to the industry ranges can be respectively determined and can be displayed in a chart so that people can observe the change trend of the development status of different industries.
As shown in fig. 4, the median of the growth index values during 12 accountants in 2020 year can be calculated for 4 industries, such as wholesale retail industry, business service industry, science and technology service industry, and manufacturing industry, respectively, and displayed in the form of a graph, so that a person can obtain the variation trend of the current development situation of each industry by observing the graph, and further make a behavior decision according to the variation trend of the current development situation of the industry.
In one embodiment, an average value of growth index values of a plurality of analysis subjects in the analysis subject group may be calculated, and the average value may be determined as the growth index value of the analysis subject group.
Fig. 5 shows a big data-based middle-sized and small-sized enterprise operation monitoring system capable of implementing the object operation monitoring method of the present embodiment, which includes a data acquisition platform, a financial intelligent algorithm platform, a growth index evaluation system, and a data analysis monitoring platform, where the data acquisition platform can perform metadata management, master data management, data quality management, and data security management, that is, after obtaining metadata and corresponding master data and other related data, the data acquisition platform puts the data in a warehouse, where data management work is performed, and washes out invalid data and data with errors, marks and sorts the data, and has security management measures, so that the data can be safely and reliably stored therein.
The financial intelligent algorithm platform can perform accounting subject management, atomic index calculation and algorithm index setting library and analysis method library, for example, the financial intelligent algorithm platform can adopt other indexes to evaluate the analysis object besides the indexes corresponding to the growth indexes. For example, the analysis target may be evaluated using different indexes such as a work and credit department monitoring index, a mutual credit investigation report index, a general purchase-sale-stock analysis index, and a general enterprise analysis index. Meanwhile, the financial intelligent algorithm platform can also provide various algorithms for operating the index data of the different indexes, such as a DuPont analysis method, a Wall scoring method, a balance score card or a Harvard analysis method and the like.
The growth index evaluation system provides a systematic statistical scoring system and a data processing method, such as the aforementioned clustering analysis method, analytic hierarchy method, data standardization (e.g., analysis based on logarithm of business income of an enterprise), consistency check, account set authenticity test construction (e.g., screening account set data according to account making period of the account set), and other data processing methods, and can desensitize data to ensure security of privacy information of the data.
The data analysis monitoring platform can be used as a platform for monitoring the operation process of a small and micro enterprise, through the object operation monitoring method, when an analysis object is the enterprise, an enterprise user can intuitively find the defects met by the development of the enterprise and the places needing to be promoted through the data analysis monitoring platform, meanwhile, a health condition can be set, whether index data of one or more indexes and a growth index value of the enterprise meet the health condition or not is determined by combining the health condition, and when the growth index value of the enterprise does not meet the health condition, alarm information can be immediately output, for example, the alarm information is fed back to computer equipment of the enterprise to prompt the enterprise user to pay attention to the index data of the enterprise. The health condition may be embodied in the form of a health threshold, and when the growth index value exceeds the health threshold, the health condition is considered to be not satisfied.
In addition, in order to facilitate the user to monitor the development status of the enterprise, the method of the embodiment can be deployed in an applet or a web page of the terminal, so that the user can conveniently monitor and observe at any time, and the use experience of the user on the object operation monitoring method of the embodiment is improved.
In the above description of the object operation monitoring method in the embodiment of the present application, referring to fig. 6, computer equipment in the embodiment of the present application is described below, and an embodiment of the computer equipment in the embodiment of the present application includes:
an obtaining unit 601, configured to perform data acquisition on an operation process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, where the secondary index group includes index data of a plurality of secondary indexes;
a first calculating unit 602, configured to calculate a weighted sum of index data of a plurality of secondary indexes of the secondary index group, to obtain index data of a primary index;
a second calculating unit 603, configured to calculate a weighted sum of index data of a plurality of primary indexes of the analysis object, so as to obtain a growth index value of the analysis object;
a first determination unit 604 for determining an analysis object group, the analysis object group including a plurality of analysis objects;
a second determining unit 605, configured to determine a growth index value of the analysis target group according to growth index values of a plurality of analysis targets of the analysis target group.
In an implementation manner of this embodiment, the first determining unit 604 is specifically configured to determine, from a plurality of candidate analysis objects, N candidate analysis objects that meet a preset range, where the preset range is obtained by dividing based on a preset dimension, where N is an integer greater than 1; layering the N candidate analysis objects; extracting a target number of alternative analysis objects from a plurality of alternative analysis objects of a target level; wherein the target hierarchy represents any one of the plurality of hierarchies; and forming an analysis object group according to the alternative analysis objects extracted from each hierarchy of the plurality of hierarchies.
In an implementation manner of this embodiment, the first determining unit 604 is specifically configured to obtain a target number corresponding to a target level according to the total number of candidate analysis objects of the target level, a standard deviation of the target level, and N sample unit allocation; a target number of candidate analysis objects are extracted from a plurality of candidate analysis objects of a target hierarchy.
In an implementation manner of this embodiment, the first determining unit 604 is specifically configured to use target index data of each candidate analysis object in the N candidate analysis objects as a sample point, where the target index data is data related to an index of the candidate analysis object; determining K centroids, wherein K is an integer greater than 1; respectively calculating the distance between each sample point and each centroid, and determining each sample point as the sample point corresponding to the centroid closest to the sample point; determining a new centroid according to a plurality of sample points corresponding to each centroid, returning to the step of calculating the distance between each sample point and each centroid until a termination condition is met, obtaining K clusters, and taking the alternative analysis object corresponding to the sample point in any one cluster as the alternative analysis object of the corresponding level of the any one cluster; wherein different clusters correspond to different levels.
In an implementation manner of this embodiment, the first calculating unit 602 is specifically configured to construct a judgment matrix according to relative importance between each two of a plurality of secondary indexes in the secondary index group, and calculate a maximum feature root of the judgment matrix; substituting the maximum characteristic root of the judgment matrix and the order of the judgment matrix into a consistency index calculation formula, and calculating to obtain a consistency index; calculating a detection coefficient, wherein the detection coefficient is a ratio of the consistency index and the random consistency index, and the random consistency index is determined according to the order of the judgment matrix; when the inspection coefficient is smaller than a preset threshold value, determining the weight of the index data of the plurality of secondary indexes according to the feature vector corresponding to the maximum feature root of the judgment matrix; and calculating the weighted sum of the index data of the plurality of secondary indexes according to the weights of the index data of the plurality of secondary indexes.
In one embodiment of this embodiment, the second determining unit 605 is further configured to monitor a change of the growth index value of the analysis object according to the health condition; and when the growth index value of the analysis object does not meet the health degree condition, outputting alarm information, wherein the alarm information is used for prompting that the growth index value of the analysis object does not meet the health degree condition.
In an implementation manner of this embodiment, the second determining unit 605 is specifically configured to arrange the growth index values of the multiple analysis objects in the analysis object group in an order of increasing or decreasing numerical values; and determining a median value from the numerical value queue obtained by arrangement, and determining the median value as a growth index value of the analysis object group.
In an embodiment of this embodiment, the computer device further includes:
the data cleaning unit 606 is configured to perform data cleaning on the index data of the secondary index according to the data distribution condition and the data meaning of the index data of the secondary index; and/or eliminating the index data of the secondary indexes of the analysis objects of which the data acquisition period number is less than the preset period number from the plurality of analysis objects, and reserving the index data of the secondary indexes of the analysis objects of which the data acquisition period number is not less than the preset period number.
In this embodiment, operations performed by each unit in the computer device are similar to those described in the embodiments shown in fig. 1 to fig. 2, and are not described again here.
In this embodiment, a growth index value of each analysis object in the analysis object group is calculated, where the growth index value is a weighted sum of index data of a plurality of primary indexes of the analysis object, the index data of the primary indexes is a weighted sum of index data of a plurality of secondary indexes, the growth index value of the analysis object group can be determined according to the growth index values of the plurality of analysis objects in the analysis object group, and the growth index value of the analysis object group can indicate a current development situation of a large environment in which the analysis object is located. Therefore, data analysis is not limited to data of a single individual any more, the development status of the large environment can be comprehensively analyzed by combining data of a plurality of individuals, the data analysis result is more objective and comprehensive, the development status of the large environment can be reflected, and personnel can make behavior decisions by combining the development status of the large environment.
Referring to fig. 7, a computer device in an embodiment of the present application is described below, where an embodiment of the computer device in the embodiment of the present application includes:
the computer device 700 may include one or more Central Processing Units (CPUs) 701 and a memory 705, where the memory 705 stores one or more applications or data.
The memory 705 may be volatile storage or persistent storage, among others. The program stored in the memory 705 may include one or more modules, each of which may include a sequence of instructions operating on a computer device. Still further, the central processor 701 may be configured to communicate with the memory 705 and to execute a sequence of instruction operations in the memory 705 on the computer device 700.
The computer apparatus 700 may also include one or more power supplies 702, one or more wired or wireless network interfaces 703, one or more input-output interfaces 704, and/or one or more operating systems, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.
The central processing unit 701 may perform operations performed by the computer device in the embodiments shown in fig. 1 to fig. 2, which are not described herein again.
An embodiment of the present application further provides a computer storage medium, where one embodiment includes: the computer storage medium has stored therein instructions that, when executed on a computer, cause the computer to perform the operations described above as being performed by the computer device in the embodiments of fig. 1-2.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
Claims (10)
1. A method for monitoring operation of an object, the method comprising:
acquiring data in the operation process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, wherein the secondary index group comprises index data of a plurality of secondary indexes;
calculating the weighted sum of the index data of a plurality of secondary indexes of the secondary index group to obtain the index data of the primary index;
calculating the weighted sum of index data of a plurality of primary indexes of the analysis object to obtain a growth index value of the analysis object;
determining a set of analysis objects, the set of analysis objects comprising a plurality of the analysis objects;
and determining the growth index value of the analysis object group according to the growth index values of a plurality of analysis objects in the analysis object group.
2. The method of claim 1, wherein the determining a set of analysis objects comprises:
determining N candidate analysis objects meeting a preset range from the multiple candidate analysis objects, wherein the preset range is obtained by dividing based on a preset dimension, and N is an integer greater than 1;
layering the N candidate analysis objects to obtain a plurality of layers;
extracting a target number of alternative analysis objects from a plurality of alternative analysis objects of a target hierarchy; wherein the target hierarchy represents any one of the plurality of hierarchies;
and forming the analysis object group according to the alternative analysis objects extracted from each hierarchy of the plurality of hierarchies.
3. The method of claim 2, wherein the extracting a target number of candidate analysis objects from a plurality of the candidate analysis objects of a target hierarchy comprises:
distributing sampling units according to the total number of the alternative analysis objects of the target level, the standard deviation of the target level and N to obtain the target number corresponding to the target level;
extracting the target number of candidate analysis objects from the plurality of candidate analysis objects of the target hierarchy.
4. The method of claim 2, wherein said layering the N candidate analysis objects comprises:
target index data of each candidate analysis object in the N candidate analysis objects is used as a sample point, wherein the target index data is data related to indexes of the candidate analysis objects;
determining K centroids, wherein K is an integer greater than 1;
respectively calculating the distance between each sample point and each centroid, and determining each sample point as the sample point corresponding to the centroid closest to the sample point;
determining a new centroid according to the plurality of sample points corresponding to each centroid respectively, and returning to the step of calculating the distance between each sample point and each centroid respectively until a termination condition is met to obtain K clusters;
taking the candidate analysis object corresponding to the sample point in any one cluster as the candidate analysis object of the corresponding hierarchy of the any one cluster; wherein different clusters correspond to different levels.
5. The method of claim 1, wherein calculating a weighted sum of metric data for a plurality of secondary metrics of the set of secondary metrics comprises:
constructing a judgment matrix according to the relative importance of the plurality of secondary indexes of the secondary index group, and calculating the maximum characteristic root of the judgment matrix;
substituting the maximum characteristic root of the judgment matrix and the order of the judgment matrix into a consistency index calculation formula, and calculating to obtain a consistency index;
calculating a check coefficient, wherein the check coefficient is a ratio of the consistency index to a random consistency index, and the random consistency index is determined according to the order of the judgment matrix;
when the inspection coefficient is smaller than a preset threshold value, determining the weight of the index data of the plurality of secondary indexes according to the feature vector corresponding to the maximum feature root of the judgment matrix;
and calculating the weighted sum of the index data of the plurality of secondary indexes according to the weights of the index data of the plurality of secondary indexes.
6. The method of claim 1, further comprising:
monitoring the change of the growth index value of the analysis object according to the health condition;
and when the growth index value of the analysis object does not meet the health degree condition, outputting alarm information, wherein the alarm information is used for prompting that the growth index value of the analysis object does not meet the health degree condition.
7. The method of any of claims 1 to 6, wherein prior to calculating the weighted sum of the metric data for the plurality of secondary metrics of the set of secondary metrics, the method further comprises:
according to the data distribution and the data meaning of the index data of the secondary index, performing data cleaning on the index data of the secondary index; and/or the presence of a gas in the gas,
and removing the index data of the secondary indexes of the analysis objects of which the data acquisition period number is less than the preset period number from the plurality of analysis objects, and reserving the index data of the secondary indexes of the analysis objects of which the data acquisition period number is not less than the preset period number.
8. A computer device, characterized in that the computer device comprises:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring data in the running process of an analysis object to obtain a secondary index group corresponding to a primary index of the analysis object, and the secondary index group comprises index data of a plurality of secondary indexes;
the first calculation unit is used for calculating the weighted sum of the index data of the secondary indexes of the secondary index group to obtain the index data of the primary index;
the second calculation unit is used for calculating the weighted sum of index data of the primary indexes of the analysis object to obtain a growth index value of the analysis object;
a first determination unit configured to determine an analysis object group including a plurality of the analysis objects;
a second determination unit configured to determine a growth index value of the analysis target group based on growth index values of a plurality of analysis targets of the analysis target group.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 7.
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