CN115147029A - Enterprise activity monitoring method and system based on big data - Google Patents
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Abstract
The invention belongs to the technical field of big data, and discloses an enterprise activity monitoring method and system based on big data, which are used for acquiring activity evaluation resource distribution of an enterprise and basic data of a market supervision department; determining business logic and calculation logic, and constructing an enterprise activity index model; processing abnormal values in the indexes, and calculating an industry adjusting parameter and a scale adjusting parameter; normalizing the adjusted index value by a min-max standardization method; respectively carrying out single enterprise activity measurement and calculation and total activity measurement and calculation; and calculating the activity of the industry and the activity of other dimensions, and determining the activity level. The invention optimizes and reconstructs the enterprise activity analysis index system and the algorithm model, adjusts and optimizes the enterprise operation state and the enterprise operation activity index, adjusts and optimizes the calculation weight of the enterprise operation state and the enterprise operation activity index, enriches the enterprise supervision means and realizes the improvement of the monitoring efficiency of the enterprise operation condition.
Description
Technical Field
The invention belongs to the technical field of big data, and particularly relates to an enterprise activity monitoring method and system based on big data.
Background
In recent years, with the rapid development of the economic society of China, new technologies, new modes and new industries are continuously emerging, and new challenges are brought to market supervision. The existing enterprise supervision mode cannot form multi-azimuth linkage with enterprises, cannot establish full-flow management and control, is low in efficiency, low in data utilization rate and incapable of forming accurate indexes and results, needs to utilize a model for assisting quantification, forms accurate monitoring and analyzing results, and helps to improve enterprise supervision efficiency and quality. The enterprise liveness is an important dimension for measuring the development status of the enterprise. At present, no unified standard evaluation system related to the activity of the enterprise exists in China temporarily, so that most of evaluation methods for the activity of the enterprise in the prior art are to establish a set of index system to evaluate the activity of the enterprise, and the evaluation method has certain limitations and incompleteness, insufficient data range coverage, low quality, complex calculation process, and used parameter indexes which are incompletely fit with the current market situation, has a low data support effect for evaluating or making decision basis, and is difficult to adapt to the requirement of monitoring the activity of the enterprise in a new situation.
In the prior art, the comparative advantages of a big data technology and the important function of an enterprise credit information public system are fully exerted, economic operation monitoring and prediction and risk early warning are improved, market expectation is reasonably guided, the service level and the supervision efficiency are improved, and an enterprise activity model is constructed. The model mainly covers three types of enterprise attribute information, operation state information and operation activity information, and 18 indexes are counted. With the reform of the organization, the development of new situation and new state and the change of the operation environment of the whole enterprise, the model can not reflect the real situation of the activity of the enterprise, so that the optimization reconstruction is carried out on the activity model of the enterprise according to the current real situation.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) The existing enterprise supervision mode cannot form multi-direction linkage with an enterprise, cannot establish full-flow management and control, is low in efficiency and poor in data utilization rate, and cannot form accurate indexes and results.
(2) In the prior art, most of evaluation methods for enterprise activity are to establish a set of index system to evaluate the activity value of an enterprise, and have certain limitations and incompleteness.
(3) The existing enterprise activity degree model can only obtain data conclusion, the result is not further classified and analyzed, and the real situation of the activity degree of the whole enterprise cannot be objectively and accurately reflected.
(4) The existing enterprise liveness analysis system is used for making a blueprint by using a given index system, actual data and market conditions are not combined, and the enterprise liveness analysis system cannot reflect the enterprise liveness conditions which are more comprehensive, more accord with the operation state of an enterprise and more fit with the current market conditions.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an enterprise activity monitoring method and system based on big data.
The invention is realized in such a way that an enterprise activity monitoring method based on big data is applied to a client, and the enterprise activity monitoring method based on big data comprises the following steps:
the client side improves the credibility of the enterprise activity value by optimizing the enterprise operation state, the enterprise operation activity index and the calculation weight; and optimizing the industry adjusting parameters and the scale adjusting parameters by combining the economic environment, the current market operating situation, the current industrial industry situation, the regional difference, the enterprise age and the influence factors of the enterprise scale, and calculating and monitoring the enterprise activity data by using the optimized enterprise activity model.
Further, the enterprise activity monitoring method based on the big data comprises the following steps:
acquiring enterprise activity evaluation resource distribution and market supervision department basic data;
determining business logic and computational logic, and constructing an enterprise activity index data model;
processing abnormal values in the indexes, and calculating industry adjusting parameters and scale adjusting parameters;
step four, normalizing the adjusted index value by adopting a min-max standardization method;
step five, respectively carrying out single enterprise activity measurement and calculation and total activity measurement and calculation;
and step six, calculating the activity of the industry and the activity of other dimensions, and determining the activity level.
Further, the basic attribute data of the enterprise in the first step comprises enterprise state, industry gate class, registered capital, annual report data of the enterprise, annual report state, tax payment, net profit, social insurance, enterprise change record data, enterprise migration data, enterprise injection and cancellation data, enterprise investor information data, enterprise branch organization data, movable property mortgage data, penalty data and enterprise abnormal directory data; tax payment data, social security payment data, accumulation fund payment data and internet data which are introduced into office data of the foreign hall as dimension supplements.
Furthermore, the business logic in the second step is the application logic of the business data, and the social security data needs to make sure the social security payment state and the payment date; if the payment record exists, the payment state is positive; if the payment date is interrupted in the month, the payment is determined as debowed, and the debowed month is calculated according to the time. The tax data needs to determine whether tax payment exists or not, and if yes, the tax data is scored; the annual report data need to clarify annual report time of enterprises, and annual report scoring is carried out within a specified time.
The calculation judgment in the calculation logic is embodied in the judgment of the injection and the suspension sale, the new registration judgment, the processing of the actual enterprise number at the end of the period and the activity degree calculation time period, wherein the activity degree time period calculation runs through the whole calculation logic.
When the expense injection and expenditure judgment is carried out, whether the enterprise has records in the calculation time period or not is judged; if yes, acquiring the recorded time, constructing a time sequence, transmitting the recorded time, and deleting the enterprise in the calculation time period; if the data is not recorded, after the data is updated in full quantity, a time sequence is constructed to set a start-stop time, and meanwhile, the suspension injection time is greater than the start time and less than or equal to the end time.
When new registration is judged, whether the enterprise has records in the calculation time period is judged; if yes, acquiring the recorded time, constructing a time sequence, transmitting the recorded time, and deleting the enterprise in the calculation time period; if no record is recorded, after the data is updated in full quantity, a time series is constructed to set a start-stop time, and a registration time is made to be greater than a start time and equal to or less than an end time.
When the end of the period is judged, judging whether the enterprise has records in the calculation time period; if yes, acquiring the recorded time, constructing a time sequence, transmitting the recorded time, and deleting the enterprise in the calculation time period; if no record is recorded, after the data is updated in full quantity, a time series is constructed to set a start-stop time, and a registration time is made to be greater than a start time and equal to or less than an end time.
Further, the enterprise liveness index model in the second step evaluates the real production and operation conditions of the enterprise through a series of index items. The index distribution comprises three categories of enterprise attribute indexes, operation state indexes and operation activity indexes, and the construction of an enterprise activity index model comprises the following steps:
1) Data resource combing: and collecting relevant activity data including market supervision, tax, social security, statistics, public accumulation fund, electric power, water affair and natural gas data from an official platform and an internet platform according to the evaluation target.
2) Data access: after the targets are obtained, the targets are preliminarily classified into enterprise registration information, enterprise permission information, enterprise operation behavior information, enterprise credit information, enterprise daily supervision information and other enterprise information; and extracting a target value of the collected data, and constructing target big data with the characteristic label.
3) Data processing: after the data are transmitted, carrying out logical relation processing on the data, and extracting, cleaning, mining and standardizing the various data by utilizing a data extraction and cleaning tool according to the characteristics of the various data; the method comprises the steps that market registration data need to extract enterprise credit codes, enterprise market registration subject names and registration time; the license information needs to extract the license category and the validity period; the effective time of the credit information needs to be extracted from the credit information of market main bodies, and the supervision records need to be extracted from the daily supervision information of enterprises.
4) Data storage: dividing the processed data into three types, respectively constructing a starting database, a basic database and a subject database, and storing the corresponding data according to data classification; the original database is used for storing the most original data and reserving the data form of the original acquisition mode, including yearly newspaper data and market subject registration original data; the basic library is data which is processed to a certain degree, and original book data is converted into an information set of an enterprise and a corresponding field thereof after the data is cleaned and processed; the theme library is used for storing result data calculated by the liveness model, has different dimensions, and displays different calculation level results in each dimension.
5) Activity model: the liveness model is a calculation unit of liveness, and liveness results appear in different dimensions after calculation; and compiling data measurement and calculation and data analysis programs according to the established enterprise activity model algorithm, performing data access and data measurement and calculation on the basic database to obtain measurement and calculation results, and storing the measurement and calculation results into corresponding theme libraries according to different display dimensions.
Further, the abnormal value processing in the index in the third step comprises:
the value of some indexes of part of enterprises is particularly high, the interference to other enterprises is caused, and in order to reduce the interference of abnormal values, the following processing is carried out on data: the data in the index is higher than the average value by +1.5 times of standard deviation, and the data is equal to the average value by +1.5 times of standard deviation; data in the index were below-1.5 standard deviations from the mean, making it equal to-1.5 standard deviations from the mean.
The calculation of the industry adjustment parameters comprises:
the operation state index and social security payment of an enterprise are not influenced by the industry, the adjustment parameter in the index is 1, and the calculation mode of the industry adjustment parameter on other indexes is as follows:
wherein the content of the first and second substances,
is taken as the mean value of the indexes,
is the industry mean of the indexes. For example, taking the index of the number of times of enterprise change records as an example,
the total number of change records of the enterprises in operation is divided by the total number of the enterprises in operation to obtain an index mean value,
the total number of change records of the enterprises in each industry is divided by the total number of the enterprises in the same industry, so as to obtain the average value of each industry, and the average value of each industry is obtained
Results and
and substituting the result into a formula for calculation to finally obtain the industry adjusting parameter of the enterprise change record frequency index.
The calculation of the scale adjustment parameter comprises:
the operation state index and social security payment of an enterprise are not influenced by scale, the adjusting parameter in the index is 1, and the scale adjusting parameter on other indexes is calculated in the following mode:
wherein the content of the first and second substances,
is taken as the mean value of the indexes,
is the mean value of indexes under different enterprise scales. For example, taking the index of the number of times of enterprise change records as an example,
the total number of change records of the enterprises in operation is divided by the total number of the enterprises in operation to obtain an index mean value,
dividing the total number of change records of the enterprises in each scale by the total number of the enterprises in the same scale to obtain the average value of each scale
Results and
and substituting the result into a formula for calculation to finally obtain the scale adjustment parameter of the enterprise change record frequency index.
The index value calculation method of the single enterprise after adjusting the parameters comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
the adjusted index value of the k index of a certain enterprise,
the parameters are adjusted for the business industry of the enterprise,
the parameters are adjusted for the size of the enterprise,
the index value of the k index of the enterprise. For example, taking the index of the number of times of change records of a single enterprise as an example,
is a regulating parameter of the industry to which the enterprise belongs,
is an adjustment parameter of the scale to which the enterprise belongs,
and substituting the enterprise change and record frequency into a formula for calculation to finally obtain the index value of the enterprise after the adjustment of the change and record frequency index.
The normalization processing in the fourth step includes:
normalizing the adjusted index value by a min-max standardization method, converting the index value into a value from 0 to 100, and calculating the activity of a single enterprise according to an index system by the processed index value;
wherein the content of the first and second substances,
for each enterprise
The minimum value of the number of the first and second,
for each enterprise
The highest value of (d). For example, taking the index of the number of change records of a single enterprise as an example,
is the index value adjusted by the index of the number of times of change and record of the enterprise,
is the minimum value of the index values adjusted by the change and record frequency index of each enterprise,
the index value is the maximum value in the index values after the change and record frequency indexes of each enterprise are adjusted, and the maximum value is substituted into a formula for calculation to finally obtain the value of the change and record frequency indexes of the enterprise.
The single enterprise activity measuring and calculating in the step five comprises the following steps:
the method for calculating the individual activity of the enterprise comprises the following steps:
wherein EA is the activity of a single enterprise,
for the enterprise, at the value of the K-th index, K =1,2, \ 8230;, 17,
is the k index
The weight of (c). For example, taking the index of the change filing coefficient of a single enterprise as an example,
the weight of the index of the number of times of filing is changed,
the value of the index of the number of times of changing and recording the enterprise is substituted into a formula for calculation, and finally the activity value of the enterprise is obtained.
The measurement and calculation of the total activity degree comprises the following steps:
the operation state of the enterprise reflects whether the enterprise is active or not, the enterprise carries out normal production operation behaviors, and tax payment, social insurance and medical insurance in the operation state indexes are all normal; under the condition of no other additional activities, the activity of the enterprise is 60, so that the enterprise with the activity greater than or equal to 60 is defined as being normally active, the overall activity is defined as the proportion of the part of the enterprise to the whole part participating in the analysis, and the model is as follows:
wherein GEA is the total activity of the enterprise, EA is the activity of a single enterprise, and N is the number of enterprises.
The method for calculating the industry activity in the sixth step comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
i =1,2, \ 8230for liveness of the ith industry, n, EA is the liveness of a single enterprise,
the number of enterprises in the ith industry.
The calculation method of the activity of other dimensions comprises the following steps: and calculating the enterprise liveness within a certain range and dimension according to the enterprise liveness calculation method, wherein the enterprise liveness comprises the region liveness and the industry liveness.
The determination of the activity level in the sixth step comprises the following steps: five levels of very active, comparative activity, general active, less active and inactive are set according to the activity score of a single enterprise,
and step six, calculating the activity of the industry, wherein the calculating process comprises data gathering and integrating, data cleaning and processing, and algorithm model building and realizing.
1) Data aggregation and integration: integrating data of an original database and data of a basic database in a data extraction, shared exchange platform data exchange and internet data dynamic acquisition mode; and gathering tax, social security, public deposit, market supervision business and network news data related to the enterprise liveness analysis, realizing full, dynamic and uniform collection of the data, and providing basic data resources for model processing and analysis.
2) And (3) data cleaning and processing: the data quality optimization method is used for optimizing data quality and processing redundant information, missing information and abnormal values collected in the process; for redundant information, compiling a corresponding script, setting a redundant condition, and triggering condition cleaning; for missing information, establishing a relation between data and data by means of identity data of unified social credit codes, performing query on an original database and a basic database together, and returning a result to complement the missing information when the same type of results are screened; and for the abnormal data value, setting a data range, defining a standard, and processing the data beyond the standard range to meet the reasonable calculation requirement.
3) Constructing and realizing an algorithm model: the enterprise mass data information is the result of data aggregation integration and data cleaning processing, and an enterprise activity index model needs to construct indexes according to actual conditions; after the task of constructing the specific indexes is completed, an implementation stage is entered, and the problems of balancing industry attributes, scale attributes, index normalization, single activity measurement and calculation, total activity measurement and calculation and activity level division are solved.
Introducing an adjusting parameter, mapping the data into a certain range through normalization, and scaling a group of data according to a certain proportion; the single activity measurement is to calculate the activity condition of a single enterprise, and logically refer to the whole activity measurement index; the total activity measurement is to calculate the activity of each enterprise and then calculate the ratio of the number of the enterprises showing market behaviors and operation management behaviors in a specific time period to the total number of the enterprises; the overall activity measurement is the final basis of the activity result. And (4) dividing the activity level, analyzing the result of the activity calculation of the enterprise by a quantitative method, and endowing different activity levels according to different scores.
Another object of the present invention is to provide a big data based enterprise activity monitoring system applying the big data based enterprise activity monitoring method, the big data based enterprise activity monitoring system comprising:
the data acquisition module is used for acquiring enterprise activity evaluation resource distribution and market regulatory department basic data;
the model construction module is used for determining business logic and calculation logic and constructing an enterprise activity index model;
the abnormal value processing module is used for processing the abnormal value in the index and calculating an industry adjusting parameter and a scale adjusting parameter;
the index normalization processing module is used for normalizing the adjusted index value by adopting a min-max standardization method;
the activity measuring and calculating module is used for respectively measuring and calculating the activity of a single enterprise and measuring and calculating the total activity;
and the activity level determining module is used for calculating the activity of the industry and the activity of other dimensions and determining the activity level.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the big data based enterprise activity monitoring method.
It is another object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of the big data based enterprise activity monitoring method.
Another object of the present invention is to provide an information data processing terminal, which is used for implementing the enterprise activity monitoring system based on big data.
In combination with the technical solutions and the technical problems to be solved, please analyze the advantages and positive effects of the technical solutions to be protected in the present invention from the following aspects:
1. improve monitoring efficiency and monitoring timeliness, form one set of complete enterprise monitoring system, the supervision means of enterprise has been richened, the realization is to the promotion of enterprise operation condition monitoring efficiency, help forms the quantified means of enterprise operation condition monitoring informationization, rely on this index system can realize the real time monitoring to the enterprise operation condition, through the continuation of liveness or the comparability change condition, reveal the change trend of enterprise's production and operation activity, promote the timeliness of enterprise operation condition monitoring.
2. The method has the advantages that the monitoring reliability is improved, and the supervision and monitoring dimension is increased, the enterprise activity algorithm model is constructed in an optimized mode, and meanwhile, the data are processed and cleaned, so that the authenticity and accuracy of the result are guaranteed from two aspects, and the supervision and monitoring reliability is improved; the method is not only used for evaluating and analyzing the history, trend and current situation of a single market subject, but also can be used for summarizing and calculating the activity of all enterprises in a specific area and industry on a macroscopic level to obtain the activity condition of the enterprises in the range, and meanwhile, the method can be classified and applied according to the age, registered capital, industry, type and other aspects of the enterprises, so that the monitoring dimension is enriched.
3. Compared with the prior art, the method has the advantages that the data redundancy is reduced, part of index changes caused by mechanism adjustment are combined, part of index changes caused by incomplete real data due to service change are deleted, and indexes which reflect the real conditions and activities of enterprises and are increased due to enterprise operation activities and market economic environment development are increased; meanwhile, the negative dynamic blank is filled, and negative value weighting is carried out on the negative indexes, so that on one hand, under the condition that the enterprise achievement statistical result is distorted or the operation of the enterprise does not obtain an active result, the activity condition of the enterprise can still be seen through the negative index information; on the other hand, illegal behaviors in the enterprise operation process can be found by means of negative indexes, and the purpose of monitoring the market is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a big data-based enterprise activity monitoring method according to an embodiment of the present invention;
FIG. 2 is a flowchart of an activity level construction method provided by an embodiment of the present invention;
fig. 3 is a schematic diagram of an enterprise activity monitoring method based on big data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a method and a system for monitoring enterprise liveness based on big data, and the invention is described in detail with reference to the attached drawings.
1. Illustrative embodiments are explained. This section is an explanatory embodiment expanding on the claims so as to fully understand how the present invention is embodied by those skilled in the art.
As shown in fig. 1, the method for monitoring enterprise activity based on big data according to the embodiment of the present invention includes the following steps:
s101, acquiring enterprise activity evaluation resource distribution and market supervision department basic data;
s102, determining business logic and computational logic, and constructing an enterprise activity index model;
s103, processing abnormal values in the indexes, and calculating industry adjusting parameters and scale adjusting parameters;
s104, normalizing the adjusted index value by adopting a min-max standardization method;
s105, respectively carrying out single enterprise activity measurement and calculation and total activity measurement and calculation;
and S106, calculating the activity of the industry and the activity of other dimensions, and determining the activity level.
In an embodiment of the invention, fig. 2 is a schematic diagram of a big data-based enterprise activity monitoring method.
Example 1
As shown in FIG. 2, determining business logic and computational logic in step S102, and constructing an enterprise activity index model includes the following steps:
(1) Data resource combing: and collecting related activity data from an official platform and an internet platform according to the evaluation target, wherein the main data comprises data such as market supervision, tax, social security, statistics, public deposit, electric power, water affairs, natural gas and the like.
TABLE 1 Enterprise Activity assessment data Source
TABLE 2 Enterprise Activity assessment supplemental data reference
| Data sources | Data content |
| Tax bureau | Tax data |
| Society and medical insurance offices | Social security payment data |
| Accumulation fund management center | Accumulation fund payment data |
| Internet data | Network news, recruitment and the like |
(2) Data access: after the targets are obtained, the targets are preliminarily classified into enterprise registration information, enterprise permission information, enterprise operation behavior information, enterprise credit information, enterprise daily supervision information and other information. And extracting a target value of the collected data, and constructing target big data with the characteristic label.
(3) Data processing: after the data are transmitted, the data are processed in a logical relation, and various data are extracted, cleaned, excavated, standardized and the like by using tools such as data extraction, cleaning and the like according to the characteristics of various data. For example, the marketspace registration data requires the extraction of a business credit code, a business marketspace registration subject name, a time of registration; the license information needs to extract the license category and the validity period; the effective time of the credit information needs to be extracted from the credit information of market main bodies, and the supervision records need to be extracted from the daily supervision information of enterprises.
(4) Data storage: dividing the processed data into three types, respectively constructing an initial database, a basic database and a subject database, and storing the corresponding data according to data classification. The original database is used for storing the most original data and reserving the data form of the original acquisition mode, such as yearly newspaper data, market subject registration original data and the like. The basic library is data which is processed to a certain degree, and original book data is converted into information sets of enterprises and corresponding fields thereof after the data is cleaned and processed. The theme library is used for storing result data after the activity model calculation, has different dimensions, and displays different calculation level results in each dimension.
(5) Activity model: the liveness model is a calculation unit of liveness, and liveness results appear in different dimensions after calculation. And compiling data measurement and calculation and data analysis programs according to the established enterprise activity model algorithm, performing data access and data measurement and calculation on the basic database to obtain measurement and calculation results, and storing the measurement and calculation results into corresponding theme libraries according to different display dimensions.
Example 2
Calculating the industry liveness in step S106 includes:
the liveness model calculation is an important step for constructing the model, and the core of the liveness model calculation comprises three blocks of data aggregation integration, data cleaning processing, algorithm model construction and realization.
1) Data aggregation and integration: and integrating the data of the original database and the basic database by adopting the modes of data extraction, data exchange of a shared exchange platform, dynamic collection of internet data and the like. And gathering data such as tax, social security, public deposit, market supervision service, network news and the like related to the activity analysis of the enterprise, realizing full, dynamic and uniform collection of the data, and providing basic data resources for model processing and analysis.
2) And (3) data cleaning and processing: the method aims to optimize data quality and process redundant information, missing information and abnormal values collected in the process. And for redundant information, compiling a corresponding script, setting a redundant condition and triggering condition cleaning. And for the missing information, establishing a relation between the data and the data by means of the identity data of the unified social credit codes, executing query on the original database and the basic database together, and returning the result to complement the missing information when the same type of result is screened. And for the abnormal data value, setting a data range, defining a standard, and processing the data beyond the standard range in order to meet the reasonable calculation requirement.
3) Constructing and realizing an algorithm model: the enterprise mass data information is the result of data aggregation integration and data cleaning processing, and an enterprise activity index model needs to construct indexes according to actual conditions. After the task of constructing the specific indexes is completed, the implementation stage is entered, and the problems of balancing the industry attributes, the scale attributes, the index normalization, the single activity measurement and calculation, the total activity measurement and calculation and the activity level division are required to be processed.
The business attribute is that the liveness of the enterprises is different due to different types of the enterprises. To eliminate this difference, adjustment parameters need to be introduced. For example, retail businesses have introduced a tuning parameter of 0.95, which is multiplied when calculating liveness.
The scale attribute is that the liveness varies due to the different sizes of the enterprises. To eliminate this difference, adjustment parameters need to be introduced. For example, under 100 ten thousand registered capital enterprises may introduce a tuning parameter of 1.15 when calculating liveness, which is multiplied when calculating liveness.
The normalization problem is to map data into a range that facilitates computation, i.e., scaling a set of data to a certain scale, e.g., converting (75, 100) to (0.75, 1).
The single activity measurement is used for calculating the activity condition of a single enterprise, and the logic of the single activity measurement refers to the whole activity measurement index.
The total activity degree measurement is to calculate the activity degree of each enterprise and then calculate the ratio of the number of the enterprises showing the market behavior and the operation management behavior in a specific time period to the total number of the enterprises. The overall activity measurement is the final basis of the activity result.
And (4) dividing the activity level, researching the result of the activity calculation of the enterprise by using a quantitative method, and endowing different activity levels according to different scores.
Example 3
As a preferred embodiment, as shown in fig. 3, the method for monitoring enterprise activity based on big data provided in the embodiment of the present invention specifically includes the following steps:
1. enterprise activity evaluation resource distribution, market supervision department basic data
The basic attribute data of the enterprise, such as enterprise state, industry door class, registered capital, enterprise annual report data, annual report state, tax payment, net profit, social insurance, enterprise change filing data, enterprise migration data, enterprise injection and expense data, enterprise investor information data, enterprise branch office data, movable property mortgage data, penalty data and enterprise abnormal directory data. Tax payment data, social security payment data, accumulation fund payment data, internet data and the like are introduced into the data of the foreign office as the supplement of the dimension.
2. Business logic
The business logic is the application logic of business data, and social security data needs to make sure the social security payment state and the payment date. If the payment record exists, the payment state is positive, if the month of the payment date is interrupted, the payment is regarded as owed, and the owed month is calculated according to the time. The tax data needs to determine whether tax payment exists, and if yes, the index is scored. The annual report data need to clarify annual report time of enterprises, and annual reports are scored within a specified time.
3. Computational logic
The calculation determination is mainly embodied in determination of overhead investment, determination of new registration, processing of the number of enterprises in the end of the term, activity calculation time period, and the like. Wherein the liveness period calculation runs through the entire computational logic.
When the overhead expense is judged, whether the enterprise has the record in the calculation time period or not is judged, if yes, the record time is obtained, a time sequence is constructed, the record time is transmitted, and meanwhile, the enterprise is deleted in the calculation time period. If the data are not recorded, after the data are updated in a full amount, a time sequence is constructed to set the starting time and the ending time, and meanwhile, the hoisting expense injection time is larger than the starting time and smaller than or equal to the ending time.
When new registration is judged, whether the enterprise has records in the calculation time period is judged, if yes, the time of the records is obtained, a time sequence is constructed, the time of the records is transmitted, and meanwhile, the enterprise is deleted in the calculation time period. If no record is recorded, after the data is updated in full, a time sequence is constructed to set the start time and the end time, and meanwhile, the registration time is larger than the start time and smaller than or equal to the end time.
When the enterprise is judged at the end, whether the enterprise has the record in the calculation time period or not is judged, if yes, the record time is obtained, a time sequence is constructed, the record time is transmitted, and meanwhile, the enterprise is deleted in the calculation time period. If no record is recorded, after the data is updated in full, a time sequence is constructed to set the start time and the end time, and meanwhile, the registration time is larger than the start time and smaller than or equal to the end time.
4. Construction and application
The activity index system of the enterprise evaluates the real production and operation conditions of the enterprise through a series of index items. The index distribution has three main categories, namely an enterprise attribute index, an operation state index and an operation activity index. The specific indexes are shown in Table 3.
TABLE 3 Enterprise Activity model index System
5. Outlier handling in metrics
The value of some indexes of part of enterprises is particularly high, the interference to other enterprises is caused, and in order to reduce the interference of abnormal values, the following processing is carried out on data: the data in the index is higher than the average value by +1.5 times of standard deviation, and the data is equal to the average value by +1.5 times of standard deviation; data in the index were below-1.5 standard deviations from the mean, making it equal to-1.5 standard deviations from the mean.
6. Industry regulating parameters
The operation state index and social security payment of an enterprise are not influenced by the industry, the adjustment parameter in the index is 1, and the calculation mode of the industry adjustment parameter on other indexes is as follows:
wherein the content of the first and second substances,
is used as the mean value of the indexes,
is the industry mean of the indexes. For example, taking the index of the number of times of enterprise change and record as an example,
the total times of change and record of the enterprises in operation are divided by the total number of the enterprises in operation to obtain an index mean value,
the total times of change and record of the enterprises in each industry are divided by the total number of the enterprises in the same industry, so as to obtain the average value of each industry
Results and
and substituting the result into a formula for calculation to finally obtain the industry adjusting parameter of the enterprise change record frequency index.
7. Scale control parameters
The operation state index and social security payment of an enterprise are not influenced by scale, the adjusting parameter in the index is 1, and the scale adjusting parameter on other indexes is calculated in the following way:
wherein, the first and the second end of the pipe are connected with each other,
is taken as the mean value of the indexes,
is the mean value of indexes under different enterprise scales. For example, taking the index of the number of times of enterprise change records as an example,
the total times of change and record of the enterprises in operation are divided by the total number of the enterprises in operation to obtain an index mean value,
dividing the total number of change records of the enterprises in each scale by the total number of the enterprises in the same scale to obtain the average value of each scale
Results and
and substituting the result into a formula for calculation to finally obtain the scale adjustment parameter of the enterprise change record frequency index.
The method for calculating the index value of the single enterprise after adjusting the parameters comprises the following steps:
wherein the content of the first and second substances,
the adjusted index value of the k index of a certain enterprise,
the parameters are adjusted for the business of the enterprise,
the parameters are adjusted for the size of the enterprise,
the index value of the k index of the enterprise. For example, taking the index of the number of times of change records of a single enterprise as an example,
is a regulating parameter of the industry to which the enterprise belongs,
is an adjustment parameter of the scale to which the enterprise belongs,
and substituting the change and record times of the enterprise into a formula for calculation to finally obtain the index value of the enterprise after the change and record times index is adjusted.
8. Normalization process
In order to eliminate the difference of the magnitude, the adjusted index value is normalized by a min-max standardization method, and the index value is converted into a value from 0 to 100. And calculating the activity of the single enterprise according to the index system after the processing.
Wherein the content of the first and second substances,
for each enterprise
The smallest value of the sum of the values,
for each enterprise
The highest value in the range. For example, taking the index of the number of times of change records of a single enterprise as an example,
is the index value adjusted by the index of the number of times of change and record of the enterprise,
is the minimum value of the index values adjusted by the change and record frequency index of each enterprise,
the index value is the maximum value in the index values after the change and record frequency indexes of each enterprise are adjusted, and the maximum value is substituted into a formula for calculation to finally obtain the value of the change and record frequency indexes of the enterprise.
9. Measuring and calculating activity of single enterprise
And calculating the individual activity of the enterprise. The calculation method is as follows:
wherein EA is the activity of a single enterprise,
for the enterprise, at the value of the K-th index, K =1,2, \ 8230;, 17,
is the k index
The weight of (c). For example, taking the index of the change filing coefficient of a single enterprise as an example,
the weight of the index of the number of times of filing is changed,
the value of the index of the number of times of changing and recording the enterprise is substituted into a formula for calculation, and finally the activity value of the enterprise is obtained.
10. Measurement and calculation of total activity
The operation state of the enterprise reflects whether the enterprise is active, the enterprise carries out normal production operation behaviors (namely tax payment, social security and medical security in the operation state index are normal), and the activity of the enterprise is 60 under the condition that no other additional activities exist, so that the enterprise with the activity greater than or equal to 60 is defined to be normally active, the whole activity is defined as the proportion of the part of the enterprise in all participating in the analysis, and the model is as follows:
wherein, GEA is the total activity of the enterprise, EA is the activity of a single enterprise, and N is the number of the enterprises.
11. Industry activity level (IEA) calculation method
Wherein the content of the first and second substances,
i =1,2, \ 8230for liveness of the ith industry, n, EA is the liveness of a single enterprise,
the number of enterprises in the ith industry.
12. Method for calculating activity of other dimensions
According to the enterprise liveness calculation method, the enterprise liveness in a certain range and dimension can be calculated, for example: regional liveness, industrial liveness, etc.
13. Grade of liveness
In order to further embody and analyze the difference of the activity degree of the enterprises, five levels of very active, comparative activity degree, general active, less active and inactive are set according to the activity degree score of a single enterprise, and the specific grades are divided into a table 4.
TABLE 4 Individual Enterprise Activity level Classification
| Activity of a Single Enterprise (EA) | Grade of liveness |
| EA≥75 | Is very active |
| 60＜EA＜75 | Is relatively active |
| EA=60 | General activity of |
| 0＜EA＜60 | Under-activation |
| EA=0 | Inactive |
The enterprise-scale attribute adjustment parameters provided by the embodiment of the invention are shown in table 5, and the industry attribute adjustment parameters are shown in table 6.
TABLE 5 Enterprise Scale Attribute tuning parameters
| X 5 | X 6 | X 7 | X 8 | X 9 | X 10 | X 11 | X 12 | X 13 | X 14 | X 15 | X 17 | |
| Within 100 ten thousand | 1.08 | 1.08 | 1.14 | 1.24 | 1.98 | 1.00 | 33.01 | 1.00 | 1.01 | 0.92 | 1.30 | 2.80 |
| 100-500 ten thousand | 0.97 | 0.97 | 1.02 | 1.13 | 4.47 | 1.00 | 5.66 | 1.03 | 1.04 | 0.98 | 0.91 | 0.56 |
| 500-1000 ten thousand | 0.92 | 0.93 | 1.01 | 1.14 | 4.18 | 1.00 | 1.87 | 0.96 | 0.96 | 1.02 | 1.00 | 13.35 |
| 1000-2000 ten thousand | 0.89 | 0.89 | 0.85 | 1.10 | 2.40 | 1.00 | 0.97 | 1.02 | 1.02 | 1.02 | 0.53 | 11.33 |
| 2000-5000 ten thousand | 0.84 | 0.85 | 0.57 | 0.96 | 1.60 | 1.00 | 0.41 | 0.95 | 0.93 | 0.97 | 1.06 | 14.70 |
| 5000-10000 ten thousand | 0.84 | 0.86 | 1.29 | 0.91 | 0.85 | 1.00 | 0.18 | 0.95 | 0.93 | 0.99 | 1.08 | 10.68 |
| Over 10000 ten thousand | 0.84 | 0.85 | 0.82 | 0.69 | 0.23 | 1.00 | 0.03 | 0.91 | 0.87 | 1.21 | 1.22 | 0.16 |
| Others are | 1.41 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.15 | 0.00 | 0.00 | 1.13 | 1.58 | 1.48 |
TABLE 6 trade Attribute tuning parameters
Example 4
The enterprise activity monitoring system based on big data provided by the embodiment of the invention comprises:
the data acquisition module is used for acquiring enterprise activity evaluation resource distribution and market regulatory department basic data;
the model construction module is used for determining business logic and calculation logic and constructing an enterprise activity index model;
the abnormal value processing module is used for processing the abnormal value in the index and calculating an industry adjusting parameter and a scale adjusting parameter;
the index normalization processing module is used for normalizing the adjusted index value by adopting a min-max standardization method;
the activity measuring and calculating module is used for respectively measuring and calculating the activity of a single enterprise and measuring and calculating the total activity;
and the activity level determining module is used for calculating the activity of the industry and the activity of other dimensions and determining the activity level.
2. Application examples. In order to prove the creativity and the technical value of the technical scheme of the invention, the part is the application example of the technical scheme of the claims on specific products or related technologies.
The invention establishes a big data model for monitoring the activity of the enterprise, and in the embodiment, an enterprise activity monitoring system is established for realizing the detection function. The application examples are described below:
step S101, obtaining enterprise activity evaluation resource distribution and market supervision department basic data, and the concrete process is as follows:
collecting the data of market supervision departments and carrying out resource exchange work on other departments. And carrying out extraction work from the data center to the created monitoring original library, and building a clickhouse database (including the original library and the theme library) for storing corresponding data, wherein the writing speed reaches 50-200M/s. And after the data extraction and exchange are finished, the cleaning and warehousing work of the original data is carried out, 8 data bases, 26 types of special subject libraries and theme libraries are built, and 511678578 pieces of data are aggregated.
The basic data of the market supervision department comprises basic attribute data of an enterprise (such as enterprise state, industry types and registered capital), enterprise change filing data, enterprise migration data, enterprise injection and expense data, enterprise data of opening an enterprise, enterprise investor information data and enterprise branch organization data; introducing other department enterprise-related data serving as the supplement of the dimension, wherein the enterprise-related data comprises tax payment state data, social security payment data, accumulation fund payment data, external trade data and water, electricity and gas use data; and supplements internet data (e.g., web news, recruiting).
Step S102, determining business logic and computational logic, and constructing an enterprise activity index model, wherein the specific process is as follows:
in an embodiment, the obtained basic data is preliminarily classified into enterprise registration information, enterprise permission information, enterprise operation behavior information, enterprise credit information, enterprise daily supervision information and other enterprise information; and extracting key fields of the database table by using a program to construct target big data with the feature tags. After data extraction and exchange are completed, cleaning and warehousing are carried out on the data, and a targeted script is compiled according to actual requirements, so that basic data acquisition of enterprise activity analysis is realized in the aspects of migration and migration, investment, invested cost, small and micro enterprises, enterprise activity, market main body total amount and the like.
The construction of the enterprise activity index model requires determining the application logic of the business logic and defining the conditions of calculation and judgment. Specifically, in the embodiment, the business logic of the social security data determines the emphasis on the social security payment state and the payment date according to the actual data. If the payment record exists, the payment state is positive; if the payment date and month are interrupted, the payment date and month are regarded as debowed, the debowed month is calculated according to the time, and the like, so that a targeted enterprise activity model algorithm is established for data measurement and analysis.
Step S103, processing abnormal values in the indexes, and calculating industry adjusting parameters and scale adjusting parameters, wherein the specific process is as follows:
in the embodiment, a rule is introduced for abnormal value conditions, the upper limit and the lower limit both take 1.5 times of standard deviation as a reference, and data in the index are higher than the average value by +1.5 times of standard deviation and are equal to the average value by +1.5 times of standard deviation; data in the indicator are lower than the average value by-1.5 times the standard deviation, and are made equal to the average value by-1.5 times the standard deviation.
In this embodiment, the basic situation of activity is different according to different actual industry situations, and industry adjustment parameters are obtained by substituting actual data into an industry adjustment formula, for example, retail industry data is introduced to obtain adjustment parameters of 0.95. The scale adjustment parameters are the same as the scale adjustment parameters, in the embodiment, the data of less than 100 ten thousand registered capital enterprises are substituted into a scale adjustment parameter formula to obtain 1.15 adjustment parameters, all industries and scales are respectively substituted into the formula to obtain the industry adjustment parameters and the scale adjustment parameters, and the calculation result shows that the results obtained by carrying out weighted adjustment calculation on the two parameters meet the actual conditions.
Step S104, normalization processing is carried out on the adjusted index value by adopting a min-max standardization method, and the specific process is as follows:
the adjustment parameters determined in the above steps are introduced, and in this embodiment, the adjusted index value is normalized by a min-max normalization method, and the value (75, 100) is converted into (0.75, 1) by the min-max normalization method.
And S105 and S106, respectively carrying out single enterprise activity measurement and calculation and total activity measurement, calculating the activity of the industry and the activity of other dimensions, and determining the activity level. The specific process is as follows:
in the embodiment, the single liveness is calculated by referring to the overall liveness measuring and calculating index, the liveness condition of each enterprise can be obtained by the system after all data are calculated by the enterprise liveness monitoring system, the overall liveness measuring and calculating in a specific time period are carried out on the basis, and the result is used as the final basis. In the embodiment, the single activity and the whole activity of all enterprises are obtained after the data are processed by the system, and based on the fact that the combined embodiment data obtained by calculation relate to the actual conditions of the enterprises, the enterprise is determined to be very active when EA is larger than 75, more active when EA is larger than 60 and smaller than 75, general active when EA is equal to 60, under active when EA is larger than 0 and smaller than 60, and inactive activity level when EA = 0. Finally, an enterprise activity analysis result is formed, and the embodiment successfully realizes the functions of constructing and calculating the enterprise activity model and supports the market supervision work on the basis.
3. Evidence of the relevant effects of the examples. The embodiment of the invention achieves some positive effects in the process of research and development or use, and has great advantages compared with the prior art, and the following contents are described by combining data, diagrams and the like in the test process.
In this embodiment, for realizing the basic data acquisition function, 7 types of data extraction and cleaning programs (scripts) are formed, covering: immigration and emigration, investment, invested, small and micro enterprises, enterprise liveness and market main amount. Compared with the prior art, the script is more targeted, and the data extraction and cleaning speed of the application embodiment is improved.
For realizing the market supervision function, this embodiment is after the big data model of market liveness that is used for the supervision is found, found market supervision monitoring system, it is visual to contain the visual large-screen of 6 monitoring analysis dimensions such as market subject development profile, the new registration condition of market subject, the market subject condition of reimbursing, market subject industry development condition, market subject regional development condition, market subject condition of emigrating in, the export function of having realized the automatic reimbursement of data simultaneously, with this support market supervision work, more audio-visual realization is to the show of enterprise liveness result.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. It will be appreciated by those skilled in the art that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, for example such code provided on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware) or a data carrier such as an optical or electronic signal carrier. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the embodiments of the present invention, and the scope of the present invention should not be limited thereto, and any modifications, equivalents and improvements made by those skilled in the art within the technical scope of the present invention as disclosed in the present invention should be covered by the scope of the present invention.
Claims (12)
1. The enterprise activity monitoring method based on the big data is characterized by comprising the following steps of:
acquiring enterprise activity evaluation resource distribution and market supervision department basic data;
determining business logic and computational logic, and constructing an enterprise activity index data model;
processing abnormal values in the indexes, and calculating industry adjusting parameters and scale adjusting parameters;
step four, normalization processing is carried out on the adjusted index value by adopting a min-max standardization method;
step five, respectively carrying out single enterprise activity measurement and calculation and total activity measurement and calculation;
and step six, calculating the activity of the industry and the activity of other dimensions, and determining the activity level.
2. The big-data based enterprise activeness monitoring method according to claim 1, wherein the enterprise basic attribute data in the first step includes enterprise status, industry gate class, registered capital, enterprise yearbook data, yearbook status, tax payment, net profit, social insurance, enterprise change filing data, enterprise migration data, enterprise investment expense data, enterprise investor information data, enterprise branch office data, mortgage data, penalty data, and enterprise abnormal entry data; meanwhile, tax payment data, social security payment data, accumulation fund payment data and internet data in the office data of the external hall are supplemented.
3. The enterprise activity monitoring method based on big data as claimed in claim 1, wherein the business logic in the second step is an application logic of business data, social security data needs to define social security payment state and payment date, tax data needs to define whether tax payment exists, annual report data needs to define annual report time of enterprises, and annual report scoring is performed within a specified time;
the calculation in the calculation logic is implemented by the judgment of the injection and the cancellation, the new registration judgment, the processing of the actual number of enterprises at the end of the period and the activity degree calculation time period, wherein the activity degree time period calculation runs through the whole calculation logic.
4. The big data-based enterprise activity monitoring method according to claim 1, wherein the enterprise activity index model in the second step evaluates the real production and operation conditions of the enterprise through a series of index items; the index distribution comprises three categories of enterprise attribute indexes, operation state indexes and operation activity indexes, and the construction of an enterprise activity index model comprises the following steps:
1) Data resource combing;
2) Accessing data;
3) Processing data;
4) Storing data;
5) And (5) an activity model.
5. The big-data based enterprise activity monitoring method as claimed in claim 1, wherein the abnormal value processing in the indicators in the third step comprises:
making the data in the index higher than the average value by +1.5 times of standard deviation and making the data equal to the average value by +1.5 times of standard deviation; the data in the index is lower than the average value by-1.5 times of standard deviation, and the data is equal to the average value by-1.5 times of standard deviation;
the calculation of the industry adjustment parameters comprises:
the operation state index and social security payment of an enterprise are not influenced by the industry, the adjustment parameter in the index is 1, and the calculation mode of the industry adjustment parameter on other indexes is as follows:
wherein the content of the first and second substances,
is taken as the mean value of the indexes,
is the industry mean of the indexes.
6. The big-data based enterprise liveness monitoring method as claimed in claim 1, wherein the normalization in the fourth step comprises:
normalizing the adjusted index value by a min-max standardization method, converting the index value into a value from 0 to 100, and calculating the activity of a single enterprise according to an index system by the processed index value;
wherein the content of the first and second substances,
for each enterprise
The smallest value of the sum of the values,
for each enterprise
The highest value in the range.
7. The big-data based enterprise activity monitoring method as claimed in claim 1, wherein the single enterprise activity measure in the fifth step comprises:
the method for calculating the individual activity of the enterprise comprises the following steps:
wherein EA is the activity of a single enterprise,
for the enterprise, at the value of the kth index, K =1,2, \8230, 17,
is the k index
The weight of (c).
8. The big data-based enterprise activity monitoring method as claimed in claim 1, wherein the industry activity calculation method in the sixth step is:
wherein the content of the first and second substances,
i =1,2, \ 8230for liveness of the ith industry, n, EA is the liveness of a single enterprise,
the number of enterprises in the ith industry;
the calculation method of the activity of other dimensions comprises the following steps: according to the enterprise activity calculation method, calculating enterprise activity in a certain range and dimension, including regional activity and industrial activity;
the activity level determination in the sixth step comprises: setting five levels of very active, comparative activity, general active, less active and inactive according to the activity score of a single enterprise;
the industrial activity calculation process comprises data aggregation and integration, data cleaning and processing, and algorithm model construction and realization.
9. An enterprise activity monitoring system based on big data applying the method according to any one of claims 1 to 8, wherein the enterprise activity monitoring system based on big data comprises:
the data acquisition module is used for acquiring enterprise activity evaluation resource distribution and market regulatory department basic data;
the model construction module is used for determining business logic and calculation logic and constructing an enterprise activity index model;
the abnormal value processing module is used for processing the abnormal value in the index and calculating an industry adjusting parameter and a scale adjusting parameter;
the index normalization processing module is used for normalizing the adjusted index value by adopting a min-max standardization method;
the activity measuring and calculating module is used for respectively measuring and calculating the activity of a single enterprise and measuring and calculating the total activity;
and the activity level determining module is used for calculating the activity of the industry and the activity of other dimensions and determining the activity level.
10. A computer device, characterized in that the computer device comprises a memory and a processor, the memory stores a computer program, and the computer program when executed by the processor causes the processor to execute the steps of the big data based enterprise activity monitoring method according to any one of claims 1 to 8.
11. A computer-readable storage medium, which is characterized by storing a computer program, and when the computer program is executed by a processor, the computer program causes the processor to execute the steps of the big data based enterprise activity monitoring method according to any one of claims 1 to 8.
12. An information data processing terminal, characterized in that the information data processing terminal is used for implementing the big data based enterprise activity monitoring system as claimed in claim 9.
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| CN117217568A (en) * | 2023-07-24 | 2023-12-12 | 广东省投资和信用中心(广东省发展和改革事务中心) | Economic monitoring method and system based on market subject information resource library |
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| CN110135667A (en) * | 2018-02-08 | 2019-08-16 | 吉林省锐迅信息技术股份有限公司 | It is a kind of for measuring the index system of enterprise's active degree |
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| CN113869642A (en) * | 2021-08-26 | 2021-12-31 | 中国环境科学研究院 | Enterprise activity determination method and device, electronic equipment and storage medium |
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| CN112015801A (en) * | 2020-08-14 | 2020-12-01 | 四川云恒数联科技有限公司 | Enterprise activity analysis method based on big data mining |
| CN113869642A (en) * | 2021-08-26 | 2021-12-31 | 中国环境科学研究院 | Enterprise activity determination method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117217568A (en) * | 2023-07-24 | 2023-12-12 | 广东省投资和信用中心(广东省发展和改革事务中心) | Economic monitoring method and system based on market subject information resource library |
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