CN113240309A - Enterprise dynamic monitoring method and device - Google Patents
Enterprise dynamic monitoring method and device Download PDFInfo
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- CN113240309A CN113240309A CN202110567443.9A CN202110567443A CN113240309A CN 113240309 A CN113240309 A CN 113240309A CN 202110567443 A CN202110567443 A CN 202110567443A CN 113240309 A CN113240309 A CN 113240309A
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Abstract
The present disclosure provides a method and a device for enterprise dynamic monitoring, including: acquiring enterprise operation data, and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data; judging whether the enterprise operation data is valid or not according to the enterprise change type corresponding to the enterprise operation data and preset historical data, and if the enterprise operation data is valid, recording the enterprise operation data as a change event; and judging whether the change event is a risk event, if so, grading the risk event according to a preset risk grade classification condition, and pushing the grade of the risk event and the risk event to a target user. The method can solve the problems that the information query is incomplete and the efficiency is low due to the fact that the related information of the target enterprise is queried manually in the prior art.
Description
Technical Field
The disclosure relates to the technical field of internet, in particular to a method and a device for dynamically monitoring an enterprise.
Background
During the operation process of the enterprise, some enterprise data reflecting the operation condition of the enterprise is generated. The related data items of an enterprise have dozens of dimensions, and along with the updating of data, a large amount of important information is to be extracted every day.
Disclosure of Invention
The embodiment of the disclosure provides an enterprise dynamic monitoring method and device, which can solve the problems of incomplete information query and low efficiency in the conventional method for manually querying risk information of a target enterprise.
In a first aspect of the embodiments of the present disclosure, a method for dynamically monitoring an enterprise is provided, including:
acquiring enterprise operation data, and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data;
judging whether the enterprise operation data is valid or not according to the enterprise variation type corresponding to the enterprise operation data and preset historical data,
if the enterprise operation data is valid, recording the enterprise operation data as a change event;
classifying the change event according to preset event classification conditions, judging whether the change event is a risk event, and if so, pushing the category of the risk event and the risk event to a target user.
In an optional implementation manner, the method for obtaining the enterprise business data and determining the enterprise change type corresponding to the enterprise business data according to the type of the enterprise business data includes:
determining the type of the enterprise operation data according to the content of the enterprise operation data;
determining the enterprise change type corresponding to the enterprise operation data based on the enterprise identification corresponding to the enterprise operation data,
wherein the enterprise change type comprises any one of a good event, a neutral event and a risk event.
In an optional implementation manner, the method for determining whether the enterprise operation data is valid according to the enterprise change type corresponding to the enterprise operation data and preset historical data includes:
determining the change time of the enterprise operation data according to the enterprise change type corresponding to the enterprise operation data;
judging whether the enterprise operation data is the latest data or not according to the change time of the enterprise operation data;
judging whether the enterprise operation data is matched with the preset historical data or not on the basis that the enterprise operation data is the latest data,
and if not, judging that the enterprise operation data is valid.
In an optional implementation manner, the method for classifying the change event according to the preset event classification condition includes:
classifying the change events according to the enterprise change types corresponding to the change events, wherein the categories corresponding to the change events comprise a first category, a second category and a third category,
the first category is used for indicating that the change which is beneficial to enterprise operation of the enterprise operation data occurs;
the second category is used for indicating that the enterprise operation data changes, and the changes are neutral changes;
the third category is used for indicating that the change which is unfavorable for the enterprise operation of the enterprise operation data occurs.
In an optional embodiment, the preset classification condition includes:
and classifying the risk level by the target user according to any one of the time when the enterprise operation data changes, the number of times when the enterprise operation data changes, the event property and the amount of money when the enterprise operation data changes.
In a second aspect of the embodiments of the present disclosure, an enterprise dynamic monitoring apparatus is provided, which includes:
the type determining unit is used for acquiring enterprise operation data and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data;
a first judging unit, configured to judge whether the enterprise operation data is valid according to an enterprise change type corresponding to the enterprise operation data and preset historical data,
the change event unit is used for recording the enterprise operation data as a change event if the enterprise operation data is valid;
a second judging unit, configured to classify the change event according to a preset event classification condition, judge whether the change event is a risk event,
and the grading pushing unit is used for pushing the category to which the risk event belongs and the risk event to a target user when the second judging unit judges that the risk event belongs is yes.
In an optional embodiment, the type determining unit is further configured to:
determining the type of the enterprise operation data according to the content of the enterprise operation data;
determining the enterprise change type corresponding to the enterprise operation data based on the enterprise identification corresponding to the enterprise operation data,
wherein the enterprise change type comprises any one of a good event, a neutral event and a risk event.
In an optional implementation manner, the first determining unit is further configured to:
determining the change time of the enterprise operation data according to the enterprise change type corresponding to the enterprise operation data;
judging whether the enterprise operation data is the latest data or not according to the change time of the enterprise operation data;
judging whether the enterprise operation data is matched with the preset historical data or not on the basis that the enterprise operation data is the latest data,
and if not, judging that the enterprise operation data is valid.
In an optional embodiment, the hierarchical pushing unit is further configured to:
classifying the change events according to the enterprise change types corresponding to the change events, wherein the categories corresponding to the change events comprise a first category, a second category and a third category,
the first category is used for indicating that the change which is beneficial to enterprise operation of the enterprise operation data occurs;
the second category is used for indicating that the enterprise operation data changes, and the changes are neutral changes;
the third category is used for indicating that the change which is unfavorable for the enterprise operation of the enterprise operation data occurs.
In an optional embodiment, the preset classification condition includes:
and classifying the risk level by the target user according to any one of the time when the enterprise operation data changes, the number of times when the enterprise operation data changes, the event property and the amount of money when the enterprise operation data changes.
The enterprise dynamic monitoring method comprises the steps of obtaining enterprise operation data, and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data;
judging whether the enterprise operation data is valid or not according to the enterprise variation type corresponding to the enterprise operation data and preset historical data,
if the enterprise operation data is valid, recording the enterprise operation data as a change event;
classifying the change event according to preset event classification conditions, judging whether the change event is a risk event or not,
if yes, pushing the category of the risk event and the risk event to a target user.
The enterprise dynamic monitoring method provided by the embodiment of the disclosure is wholly divided into changes and early warning events, the changes mainly comprise interest events, neutral events and risk events, and monitoring information is more comprehensive. Repeated data information caused by repeated data acquisition needs to be compared with historical events, the latest event which changes is extracted, the changed events are classified, and information push is carried out.
Meanwhile, the change events of the associated enterprises are pushed, the change events can be classified by the user according to the self-defined early warning rules of the time, the times, the amount and the like of the change events and the experience of the user, and the risk events formed by frequent occurrence or accumulation of a plurality of neutral events in a short period can also be pushed. Furthermore, the risk type of the enterprise incremental data corresponding to the enterprise identification is determined according to the preset enterprise monitoring dimension, so that the user can receive the risk message of each dimension data in a self-defined mode, and the enterprise risk monitoring message can be accurately pushed to enable the user to receive the risk message of the concerned data dimension.
Drawings
FIG. 1 is a schematic flow chart of a method for enterprise dynamic monitoring according to an embodiment of the present disclosure;
FIG. 2 is a logic diagram of a method for enterprise dynamic monitoring according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an enterprise dynamic monitoring apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present disclosure and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein.
It should be understood that, in various embodiments of the present disclosure, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It should be understood that in the present disclosure, "including" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present disclosure, "plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that all three of A, B, C comprise, "comprises A, B or C" means that one of A, B, C comprises, "comprises A, B and/or C" means that any 1 or any 2 or 3 of A, B, C comprises.
It should be understood that in this disclosure, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, from which B can be determined. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present disclosure is explained in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a schematic flowchart illustrating an enterprise dynamic monitoring method according to an embodiment of the present disclosure, where as shown in fig. 1, the method includes:
step S101, acquiring enterprise operation data, and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data;
illustratively, the enterprise business data may include business information, court information, loss of credit information, brand patents, participation in bids, enterprise recruitment information, and the like. There are multiple dimensions for data items involved in a business and as data is updated, there is a large amount of information to be extracted each day. The risk grade of the enterprise incremental data corresponding to the enterprise identification is determined by presetting a risk grading rule for the enterprise incremental data, and corresponding enterprise risk monitoring information is generated, so that the problems of incomplete information and low efficiency existing in the process of manually inquiring the risk information of a target enterprise in the conventional technology can be solved.
Specifically, the enterprise change type corresponding to the enterprise operation data may be determined according to the type of the enterprise operation data.
In an optional implementation manner, the method for obtaining the enterprise business data and determining the enterprise change type corresponding to the enterprise business data according to the type of the enterprise business data includes:
determining the type of the enterprise operation data according to the content of the enterprise operation data;
determining the enterprise change type corresponding to the enterprise operation data based on the enterprise identification corresponding to the enterprise operation data,
wherein the enterprise change type comprises any one of a good event, a neutral event and a risk event.
For example, the enterprise identification can be used for representing a unique identification of the enterprise, the corresponding enterprise can be uniquely determined through the enterprise identification, data errors are prevented, and the enterprise business data can be accurately corresponding to the enterprise change type.
Optionally, the enterprise operation data may be acquired from a third party platform or a preset database, and the acquisition mode of the enterprise operation data is not limited in the embodiment of the present disclosure. The type of the enterprise operation data, such as daily operation of the enterprise, administrative penalty corresponding to the enterprise, and the like, is determined according to the content of the enterprise operation data, such as the information of the business and business registration of the enterprise, the specific registration date, the location of the registered business management department, and the like.
For example, in the embodiment of the present disclosure, the type of the enterprise operation data may include enterprise supervision risk, enterprise judicial risk, enterprise operation risk, personnel judicial risk, personnel operation risk, and the like. It should be noted that the embodiment of the present disclosure does not limit the type of the enterprise operation data. Taking enterprise regulatory risk as an example, the enterprise regulatory risk may include:
when the enterprise main body is listed in one of serious violation, administrative penalty, environmental penalty, newly-added tax notice, violation processing, spot check and inspection and the like, the type of the enterprise operation data can be determined as enterprise supervision risk.
Based on the type of the enterprise business data, the enterprise identification corresponding to the enterprise can be determined, so that the enterprise change type corresponding to the enterprise business data is further determined.
For example, the business change types corresponding to the business administration data may include any one of a good event, a neutral event, and a risk event.
It is understood that a good event may be an event that is beneficial to enterprise operation, a neutral event may be a normal flow event during enterprise operation, and a risk event may be an event that is not beneficial to enterprise operation. The type of the enterprise operation data can determine whether the enterprise data is the enterprise operation data which is beneficial to enterprise operation, unfavorable to enterprise operation or normal.
Step S102, judging whether the enterprise operation data is effective or not according to the enterprise change type corresponding to the enterprise operation data and preset historical data,
in practical application, a plurality of dimensions are provided for data items related to an enterprise, a large amount of information is to be extracted every day along with data updating, and in the information, part of information which is invalid for dynamic monitoring of the enterprise exists, and in order to improve the validity of the data and reduce the data processing pressure, whether the enterprise operation data is valid or not can be judged according to the enterprise change type corresponding to the enterprise operation data and preset historical data. And judging whether the enterprise operation data is effective or not by matching with preset historical data.
In an optional implementation manner, the method for judging whether the enterprise operation data is valid according to the enterprise change type corresponding to the enterprise operation data and preset historical data includes:
determining the change time of the enterprise operation data according to the enterprise change type corresponding to the enterprise operation data;
judging whether the enterprise operation data is the latest data or not according to the change time of the enterprise operation data;
judging whether the enterprise operation data is matched with the preset historical data or not on the basis that the enterprise operation data is the latest data,
and if not, judging that the enterprise operation data is valid.
For example, the change time of the enterprise operation data may be determined according to an enterprise change type corresponding to the enterprise operation data, where, taking the enterprise change type as a risk event as an example, when the risk event occurs, the time of the risk event occurring is recorded as the change time of the enterprise operation data;
further, whether the enterprise operation data is the latest data or not may be determined according to the change time of the enterprise operation data, where the method for determining whether the enterprise operation data is the latest data may include whether the interval time between the current query time and the current query time is within a preset time threshold range or not, if so, the enterprise operation data may be determined as the latest data, and if not, the enterprise operation data may not be determined as the latest data.
And on the basis that the enterprise operation data is the latest data, judging whether the enterprise operation data is matched with the preset historical data or not, and if not, judging that the enterprise operation data is valid.
For example, based on the date of the previous change, if the difference between the date of the current change and the date of the previous change is greater than the preset date threshold, the data may be determined to be invalid, otherwise, the data may be determined to be valid. It should be noted that the preset date threshold may be adjusted and set according to user requirements, and the preset date threshold is not limited in the embodiment of the present disclosure.
Step S103, if the enterprise operation data is valid, recording the enterprise operation data as a change event;
for example, if the enterprise operation data is valid, the enterprise operation data can be recorded as a change event, so that the change condition of the enterprise operation data can be accurately acquired, and dynamic monitoring of the enterprise is facilitated.
Step S104, classifying the change events according to preset event classification conditions, judging whether the change events are risk events,
if yes, pushing the category of the risk event and the risk event to a target user.
Risk events can be formed according to the change events, classified and risk pushed, meanwhile, risk events of associated enterprises are pushed, hierarchical risk events are formed on the basis of dynamic monitoring of the enterprises, different situations can be accurately corresponded, corresponding risk events and categories corresponding to the risk events are pushed to target users, the target users can know the corresponding situations in time, and corresponding preparation and processing are made.
In an optional implementation manner, the method for classifying the change event according to the preset classification condition includes:
classifying the change events according to the enterprise change types corresponding to the change events, wherein the categories corresponding to the change events comprise a first category, a second category and a third category,
the first category is used for indicating that the change which is beneficial to enterprise operation of the enterprise operation data occurs;
the second category is used for indicating that the enterprise operation data changes, and the changes are neutral changes;
the third category is used for indicating that the change which is unfavorable for the enterprise operation of the enterprise operation data occurs.
In an optional embodiment, the preset classification condition includes:
and classifying the risk level by the target user according to any one of the time when the enterprise operation data changes, the number of times when the enterprise operation data changes, the event property and the amount of money when the enterprise operation data changes.
In addition, in an optional implementation, the user may also classify events according to the self-defined early warning rules of time, frequency, amount of money of the event change, and according to the experience of the user, and may set and push corresponding risk categories for risk events that are frequently generated or accumulated to a certain extent in a short period of time of a plurality of neutral events, for example, legal representatives change many times in half a year.
In the embodiment of the disclosure, the user can customize the early warning rule, so that the problems that the preset risk category rule and the preset risk event classification cannot flexibly adapt to the actual situation are avoided.
The enterprise dynamic monitoring method provided by the embodiment of the disclosure is wholly divided into changes and early warning events, the changes mainly comprise interest events, neutral events and risk events, and monitoring information is more comprehensive. Repeated data information caused by repeated data acquisition needs to be compared with historical events, the latest event which changes is extracted, risk events can be formed according to the change events, and the risk events are classified and risk push is carried out.
Meanwhile, the risk events of the associated enterprises are pushed, the risk events can be classified according to self-defined early warning rules of time, times, amount and the like of the change events by users according to own experiences of the users, and the risk level can be set for pushing due to the fact that a plurality of neutral events frequently occur or are accumulated to a certain degree in a short period. Furthermore, the risk type of the enterprise incremental data corresponding to the enterprise identification is determined according to the preset enterprise monitoring dimension, so that the user can receive the risk message of each dimension data in a self-defined mode, and the enterprise risk monitoring message can be accurately pushed to enable the user to receive the risk message of the concerned data dimension.
Fig. 2 is a logic diagram schematically illustrating an enterprise dynamic monitoring method according to an embodiment of the present disclosure, and as shown in fig. 2, the enterprise dynamic monitoring method according to an embodiment of the present disclosure may include:
acquiring enterprise operation data, and determining an enterprise change type corresponding to the enterprise operation data according to the enterprise operation data;
comparing the enterprise change type corresponding to the enterprise operation data with historical data, and judging whether the data is valid;
if the enterprise change type is determined to be valid, recording an event corresponding to the enterprise change type into a change event;
judging whether the change event is a risk event or a user-defined risk event;
and if the risk event is the risk event, pushing the risk event to a user mailbox or a client side of social software and the like according to the risk category.
Fig. 3 is a schematic structural diagram of an enterprise dynamic monitoring apparatus according to an embodiment of the present disclosure, and as shown in fig. 3, the enterprise dynamic monitoring apparatus according to an embodiment of the present disclosure may include:
a type determining unit 31, configured to obtain enterprise operation data, and determine an enterprise change type corresponding to the enterprise operation data according to a type of the enterprise operation data;
a first determining unit 32, configured to determine whether the enterprise operation data is valid according to the enterprise change type corresponding to the enterprise operation data and preset historical data,
a change event unit 33, configured to record the enterprise operation data as a change event if the enterprise operation data is valid;
a second determining unit 34, configured to classify the change event according to a preset event classification condition, determine whether the change event is a risk event,
a hierarchical pushing unit 35, configured to, if the second determining unit determines that the category to which the risk event belongs and the risk event are pushed to a target user.
In an alternative embodiment, the type determining unit 31 is further configured to:
determining the type of the enterprise operation data according to the content of the enterprise operation data;
determining the enterprise change type corresponding to the enterprise operation data based on the enterprise identification corresponding to the type of the enterprise operation data,
wherein the enterprise change type comprises any one of a good event, a neutral event and a risk event.
In an optional implementation manner, the first determining unit 32 is further configured to:
determining the change time of the enterprise operation data according to the enterprise change type corresponding to the enterprise operation data;
judging whether the enterprise operation data is the latest data or not according to the change time of the enterprise operation data;
judging whether the enterprise operation data is matched with the preset historical data or not on the basis that the enterprise operation data is the latest data,
and if not, judging that the enterprise operation data is valid.
In an optional embodiment, the hierarchical pushing unit 34 is further configured to:
classifying the change events according to the enterprise change types corresponding to the change events, wherein the categories corresponding to the change events comprise a first category, a second category and a third category,
the first category is used for indicating that the change which is beneficial to enterprise operation of the enterprise operation data occurs;
the second category is used for indicating that the enterprise operation data changes, and the changes are neutral changes;
the third category is used for indicating that the change which is unfavorable for the enterprise operation of the enterprise operation data occurs.
In an optional embodiment, the preset classification condition includes:
and classifying the risk level by the target user according to any one of the time when the enterprise operation data changes, the number of times when the enterprise operation data changes, the event property and the amount of money when the enterprise operation data changes.
The present disclosure also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be a read-only memory (ROM), a random-access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present disclosure may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (10)
1. An enterprise dynamic monitoring method is characterized by comprising the following steps:
acquiring enterprise operation data, and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data;
judging whether the enterprise operation data is valid or not according to the enterprise variation type corresponding to the enterprise operation data and preset historical data,
if the enterprise operation data is valid, recording the enterprise operation data as a change event;
classifying the change event according to preset event classification conditions, judging whether the change event is a risk event or not,
if yes, pushing the category of the risk event and the risk event to a target user.
2. The method of claim 1, wherein the step of obtaining business operation data and determining the business variation type corresponding to the business operation data according to the type of the business operation data comprises:
determining the type of the enterprise operation data according to the content of the enterprise operation data;
determining the enterprise change type corresponding to the enterprise operation data based on the enterprise identification corresponding to the type of the enterprise operation data,
wherein the enterprise change type comprises any one of a good event, a neutral event and a risk event.
3. The method of claim 1, wherein the determining whether the business operation data is valid according to the business variation type corresponding to the business operation data and preset historical data comprises:
determining the change time of the enterprise operation data according to the enterprise change type corresponding to the enterprise operation data;
judging whether the enterprise operation data is the latest data or not according to the change time of the enterprise operation data;
judging whether the enterprise operation data is matched with the preset historical data or not on the basis that the enterprise operation data is the latest data,
and if not, judging that the enterprise operation data is valid.
4. The method of claim 1, wherein the method for classifying the change event according to the preset event classification condition comprises:
classifying the change events according to the enterprise change types corresponding to the change events, wherein the categories corresponding to the change events comprise a first category, a second category and a third category,
the first category is used for indicating that the change which is beneficial to enterprise operation of the enterprise operation data occurs;
the second category is used for indicating that the enterprise operation data changes, and the changes are neutral changes;
the third category is used for indicating that the change which is unfavorable for the enterprise operation of the enterprise operation data occurs.
5. The method according to claim 1, wherein the preset classification condition comprises:
and classifying the risk level by the target user according to any one of the time when the enterprise operation data changes, the number of times when the enterprise operation data changes, the event property and the amount of money when the enterprise operation data changes.
6. An enterprise dynamic monitoring apparatus, comprising:
the type determining unit is used for acquiring enterprise operation data and determining an enterprise change type corresponding to the enterprise operation data according to the type of the enterprise operation data;
a first judging unit, configured to judge whether the enterprise operation data is valid according to an enterprise change type corresponding to the enterprise operation data and preset historical data,
the change event unit is used for recording the enterprise operation data as a change event if the enterprise operation data is valid;
a second judging unit, configured to classify the change event according to a preset event classification condition, judge whether the change event is a risk event,
and the grading pushing unit is used for pushing the category to which the risk event belongs and the risk event to a target user when the second judging unit judges that the risk event belongs is yes.
7. The apparatus of claim 6, wherein the type determination unit is further configured to:
determining the type of the enterprise operation data according to the content of the enterprise operation data;
determining the enterprise change type corresponding to the enterprise operation data based on the enterprise identification corresponding to the type of the enterprise operation data,
wherein the enterprise change type comprises any one of a good event, a neutral event and a risk event.
8. The apparatus of claim 6, wherein the first determining unit is further configured to:
determining the change time of the enterprise operation data according to the enterprise change type corresponding to the enterprise operation data;
judging whether the enterprise operation data is the latest data or not according to the change time of the enterprise operation data;
judging whether the enterprise operation data is matched with the preset historical data or not on the basis that the enterprise operation data is the latest data,
and if not, judging that the enterprise operation data is valid.
9. The apparatus of claim 6, wherein the staging push unit is further configured to:
classifying the change events according to the enterprise change types corresponding to the change events, wherein the categories corresponding to the change events comprise a first category, a second category and a third category,
the first category is used for indicating that the change which is beneficial to enterprise operation of the enterprise operation data occurs;
the second category is used for indicating that the enterprise operation data changes, and the changes are neutral changes;
the third category is used for indicating that the change which is unfavorable for the enterprise operation of the enterprise operation data occurs.
10. The apparatus of claim 6, wherein the preset classification condition comprises:
and classifying the risk level by the target user according to any one of the time when the enterprise operation data changes, the number of times when the enterprise operation data changes, the event property and the amount of money when the enterprise operation data changes.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114117539A (en) * | 2022-01-24 | 2022-03-01 | 北京交研智慧科技有限公司 | Data protection method and device |
| CN115687721A (en) * | 2022-10-28 | 2023-02-03 | 盐城金堤科技有限公司 | Enterprise dynamic information recommendation method and device, computing equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101773764B1 (en) * | 2017-02-21 | 2017-09-01 | 주식회사 지담랩 | Corporate doctorring system based on financial analysis |
| CN108876228A (en) * | 2018-09-28 | 2018-11-23 | 苏州朗动网络科技有限公司 | Monitoring method, device, computer equipment and the storage medium of business risk |
| CN112651660A (en) * | 2021-01-07 | 2021-04-13 | 量子数聚（北京）科技有限公司 | Enterprise risk early warning method and device |
-
2021
- 2021-05-24 CN CN202110567443.9A patent/CN113240309A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101773764B1 (en) * | 2017-02-21 | 2017-09-01 | 주식회사 지담랩 | Corporate doctorring system based on financial analysis |
| CN108876228A (en) * | 2018-09-28 | 2018-11-23 | 苏州朗动网络科技有限公司 | Monitoring method, device, computer equipment and the storage medium of business risk |
| CN112651660A (en) * | 2021-01-07 | 2021-04-13 | 量子数聚（北京）科技有限公司 | Enterprise risk early warning method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114117539A (en) * | 2022-01-24 | 2022-03-01 | 北京交研智慧科技有限公司 | Data protection method and device |
| CN115687721A (en) * | 2022-10-28 | 2023-02-03 | 盐城金堤科技有限公司 | Enterprise dynamic information recommendation method and device, computing equipment and storage medium |
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