CN113869623A - Enterprise risk level determination method and device and readable storage medium - Google Patents
Enterprise risk level determination method and device and readable storage medium Download PDFInfo
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Abstract
The application provides a method, a device and a readable storage medium for determining enterprise risk level, which are used for acquiring enterprise information filled by an enterprise to be detected; acquiring the number of employees of the enterprise to be detected under each preset abnormal attribute dimension from the verified enterprise information; determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk; and determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value. Therefore, the risk level of the enterprise to be detected is determined by background analysis directly according to the number of the staff under the preset abnormal attribute dimension in the enterprise information filled by the enterprise to be detected and the mapping relation between the preset abnormal attribute dimension and the enterprise risk, so that the processing time for inputting the enterprise information and analyzing the enterprise information by the staff can be saved, and the efficiency of enterprise risk detection is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for determining an enterprise risk level, and a readable storage medium.
Background
During the existence of abnormal conditions (such as epidemic situations), most enterprises can carry out ordered production along with the development situation of the abnormal conditions, at the moment, enterprise-related data need to be collected so that personnel at all levels can judge whether the enterprises are suitable for continuous production or shutdown production under the abnormal conditions, and after the enterprise-related data are collected, the prevention and control risks of the enterprises under the abnormal conditions are determined according to the collected data, so that whether the enterprises continue to produce or not is judged according to the prevention and control risks.
At present, the form is mainly issued to the administration enterprise, then the form submitted by the enterprise is input by related workers to complete the collection of the related data of the enterprise, and then the prevention and control risks of the enterprise are analyzed by the prevention and control workers, but the prevention and control workers can be subjected to larger workload, so that the detection efficiency of the prevention and control risks of the enterprise is lower.
Disclosure of Invention
In view of this, an object of the present application is to provide a method, an apparatus and a readable storage medium for determining an enterprise risk level, which are used for determining the risk level of an enterprise to be detected through background analysis directly according to the number of employees in a preset abnormal attribute dimension in enterprise information to be filled by the enterprise to be detected and a mapping relationship between the preset abnormal attribute dimension and an enterprise risk, so as to save processing time for entering enterprise information and analyzing the enterprise information by a worker, and help to improve efficiency of enterprise risk detection.
The embodiment of the application provides a method for determining enterprise risk level, which comprises the following steps:
acquiring enterprise information filled by an enterprise to be detected;
acquiring the number of employees of the enterprise to be detected under each preset abnormal attribute dimension from the verified enterprise information;
determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk;
and determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value.
Further, the enterprise information is verified as follows:
determining the enterprise name and the enterprise code of the enterprise to be detected from the enterprise information;
determining whether the business name exists and the business code is consistent with a docket business code associated with the business name;
if yes, determining that the enterprise information passes verification;
if not, determining that the enterprise information is abnormal.
Further, the preset abnormal attribute dimension comprises at least one of contact with abnormal personnel within a preset time period, an abnormal area from and to within a preset time period, and health conditions of the personnel.
Further, after determining the enterprise risk level of the enterprise to be detected, the method further includes:
and storing the enterprise information of the enterprise to be detected and the enterprise risk level of the enterprise to be detected in an enterprise storage area.
Further, the determining method further includes:
and in response to a query instruction of a user, determining at least one display dimension based on the query information indicated in the query instruction, and displaying the enterprise information in each display dimension.
The embodiment of the present application further provides a device for determining an enterprise risk level, where the device for determining an enterprise risk level includes:
the information acquisition module is used for acquiring enterprise information filled by the enterprise to be detected;
the quantity determining module is used for acquiring the quantity of the employees of the enterprise to be detected under each preset abnormal attribute dimension from the verified enterprise information;
the first determining module is used for determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk;
and the second determining module is used for determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value.
Further, the determining apparatus further includes an enterprise information checking module, where the enterprise information checking module is configured to:
determining the enterprise name and the enterprise code of the enterprise to be detected from the enterprise information;
detecting whether the business name exists and whether the business code is consistent with a docket business code associated with the business name;
if yes, determining that the enterprise information passes verification;
if not, determining that the enterprise information is abnormal.
Further, the preset abnormal attribute dimension comprises at least one of contact with abnormal personnel within a preset time period, an abnormal area from and to within a preset time period, and health conditions of the personnel.
Further, the determining apparatus further includes a data storage module, and the data storage module includes:
and storing the enterprise information of the enterprise to be detected and the enterprise risk level of the enterprise to be detected in an enterprise storage area.
Further, the determining apparatus further includes a display module, and the display module is configured to:
and in response to a query instruction of a user, determining at least one display dimension based on the query information indicated in the query instruction, and displaying the enterprise information in each display dimension.
An embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the enterprise risk level determination method as described above.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining an enterprise risk level are performed as described above.
According to the enterprise risk level determining method, the enterprise risk level determining device and the readable storage medium, the number of staff of the enterprise to be detected under each preset abnormal attribute dimension is obtained from the enterprise information which is filled in by the enterprise to be detected and passes the verification, the risk level corresponding to each preset abnormal attribute dimension is determined according to the mapping relation between each preset abnormal attribute dimension and the enterprise risk, the enterprise risk level of the enterprise to be detected is determined through the risk level of each preset abnormal attribute dimension, the number of staff of the enterprise to be detected is larger than the preset number, the risk level of the enterprise to be detected is determined directly according to the number of staff under the preset abnormal attribute dimension in the enterprise information which is filled in by the enterprise to be detected and the mapping relation between the preset abnormal attribute dimension and the enterprise risk, the risk level of the enterprise to be detected is determined through background analysis, and the enterprise information which is input by staff can be saved, And the processing time of enterprise information is analyzed, so that the efficiency of enterprise risk detection is improved.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a block diagram of a possible application scenario;
FIG. 2 is a flowchart of a method for determining an enterprise risk level according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of a method for determining a risk level of an enterprise according to another embodiment of the present application;
fig. 4 is a schematic structural diagram of an enterprise risk level determination apparatus according to an embodiment of the present disclosure;
fig. 5 is a second schematic structural diagram of an enterprise risk level determination apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.
Next, an application scenario to which the present application is applicable will be described. The application can be applied to the technical field of data processing, the number of employees of an enterprise to be detected under each preset abnormal attribute dimension is obtained from the enterprise information which passes the verification according to the obtained enterprise information filled by the enterprise to be detected, the risk grade corresponding to each preset abnormal attribute dimension is determined according to the mapping relation between each preset abnormal attribute dimension and the enterprise risk, the enterprise risk grade of the enterprise to be detected is determined according to the risk grade of each preset abnormal attribute dimension, the number of employees under the preset abnormal attribute dimension in the enterprise information filled by the enterprise to be detected is directly determined according to the mapping relation between the preset abnormal attribute dimension and the enterprise risk, the risk grade of the enterprise to be detected is determined by background analysis, the processing time for entering the enterprise information and analyzing the enterprise information by workers can be saved, the method is beneficial to improving the enterprise risk detection efficiency.
Referring to fig. 1, fig. 1 is a system structure diagram in a possible application scenario, as shown in fig. 1, the system includes a user terminal and a determining device, the user terminal submits enterprise information filled by an enterprise to be detected, the determining device checks the enterprise information after receiving the enterprise information, determines an enterprise risk level of the enterprise to be detected after the enterprise information passes the check, stores the enterprise information and the enterprise risk level, determines at least one display dimension according to query information in the query instruction after the user terminal sends the query instruction, and displays corresponding enterprise information in each display dimension.
Research shows that at present, forms are mainly issued to a domination enterprise, then related workers enter the forms according to the forms submitted by the enterprise to complete the collection of related data of the enterprise, and then prevention and control workers analyze the prevention and control risks of the enterprise, but the prevention and control workers bring large workload, and the detection efficiency of the prevention and control risks of the enterprise is low.
Based on this, the embodiment of the application provides a method for determining enterprise risk levels, which directly determines the risk levels of the to-be-detected enterprises through background analysis according to the number of employees under the preset abnormal attribute dimension in the enterprise information filled by the to-be-detected enterprises and the mapping relationship between the preset abnormal attribute dimension and the enterprise risk, so that the processing time for entering the enterprise information and analyzing the enterprise information by the staff can be saved, and the efficiency of enterprise risk detection can be improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for determining an enterprise risk level according to an embodiment of the present disclosure. As shown in fig. 2, a method for determining an enterprise risk level according to an embodiment of the present application includes:
s201, acquiring enterprise information filled by the enterprise to be detected.
In the step, the enterprise information of the enterprise to be detected is obtained according to the filled information submitted by the enterprise to be detected.
The enterprise information filled by the enterprise to be detected can be divided into a plurality of filling directions, taking the enterprise to be detected in the epidemic situation prevention and control period as an example, the first filling direction is the filling of the basic information of the enterprise, the first filling direction can include enterprise information such as enterprise names (other enterprise names), filling persons, contact phones, unified social credit codes of enterprise organizations, contacts, jobs, the number of enterprise workers, the number of workers in the area where the enterprise is located, enterprise types and the like, and in the first filling direction, the basic organizational structure information of the enterprise to be detected is mainly filled, so that the enterprise can be conveniently researched and controlled in the follow-up process; the second filling direction is the filling of the planned reworking condition of the enterprise, and the filling direction can comprise related information of the reworking condition of the enterprise, such as the current production condition of the enterprise, the number of workers in the area where the enterprise is located, the enterprise planned reworking time, the number of planned reworking workers of the enterprise, the overall production time recovery of the enterprise plan, the overall production number of the enterprise recovery and the like; the third filling direction is the filling of the employee information of the enterprise to be replied, and can comprise the total number of inspectors, the number of contacts with abnormal personnel in a preset time interval, the number of contacts with the abnormal personnel in the preset time interval, the number of people going to and from the abnormal area in the preset time interval, the number of people going back to the area where the enterprise is located from the abnormal area in the preset time interval, the name, the telephone number and the identity card number of the employee who specifically contacts the abnormal personnel; the fourth filling direction is the abnormal personnel of the enterprise and the measures taken, and the filling direction can comprise the number of abnormal symptoms, the number of isolated observers, suspected cases, confirmed cases, the names, telephone numbers, identity card numbers, the measures taken and the like of the confirmed cases.
The abnormal person may be a person who has already developed an epidemic-related disease, a person who has arrived at an abnormal area, a person who has come into close contact with a person who has arrived at an abnormal area, or the like.
The abnormal area may be an area where an epidemic situation occurs at the earliest or an area where a number of people with symptoms related to the epidemic situation is large and continuously increases, and the abnormal area may be a city, or a certain region, a certain county, and the like in the city.
S202, acquiring the number of the employees of the enterprise to be detected in each preset abnormal attribute dimension from the verified enterprise information.
In this step, after the filled enterprise information submitted by the enterprise to be detected is acquired in step S201, the enterprise information of the enterprise to be detected is verified, and after the enterprise information verification is passed, the number of employees in each preset abnormal attribute dimension in the filled enterprise information is acquired.
The preset abnormal attribute dimension is set in advance according to requirements, and if the number of the employees in the preset abnormal attribute dimension exceeds a certain threshold value, the fact that the enterprise to be detected has certain risks is indicated.
Here, in the enterprise information filled by the enterprise to be detected, a certain number of employees may exist under each preset abnormal attribute dimension; there may also be a number of employees in several of the preset anomalous attribute dimensions.
For the above example, the preset abnormal attribute dimension is some dimensions related to the epidemic situation, for example, the number of people who contact with the abnormal people in the third reporting direction, the number of people who contact with the abnormal area in the preset time interval, the number of people who come in and go out of the abnormal area in the preset time interval, and the number of people who return to the area where the enterprise is located from the abnormal area in the preset time interval; the number of abnormal symptoms, the number of isolated observers, suspected cases, confirmed cases, etc. in the fourth direction of filling.
S203, determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk.
In the step, a risk level corresponding to each preset abnormal attribute dimension is determined according to a preset mapping relation between each preset abnormal attribute dimension and the enterprise risk. Specifically, according to a mapping relation between pre-stored preset abnormal attribute dimensions and enterprise risks, a risk level corresponding to each preset abnormal attribute dimension is determined.
Here, the enterprise risk mainly refers to a risk that if an enterprise performs business during an abnormal situation, the abnormal situation is more tense; for the above example, during epidemic situation prevention and control, when the enterprise carries out production and operation, there will be a certain degree of enterprise employee aggregation, and at this moment, the enterprise risk mainly is: whether the epidemic situation is more serious and the risk of infection among the employees exists.
Here, the setting of each preset abnormal attribute dimension is set according to the attribute of the abnormal condition, and for the above example, during the epidemic situation prevention and control, whether the employee reaches the region with a serious epidemic situation, whether the employee has symptoms related to the epidemic situation, and the like, is the dimension which needs to be paid attention in the epidemic situation prevention and control and can influence the epidemic situation prevention and control, and the dimensions are all preset as the preset abnormal attribute dimensions.
The corresponding risk level may be adjectives such as "high", "low", "lower", and the like visually described by using characters, or may be defined by manually set numbers, for example, the risk level is divided into 10 levels in total, 10 levels of 0 to 9 may be set, and the larger the number is, the higher the enterprise risk level is, and the level corresponding to each preset abnormal attribute dimension is correspondingly increased in response to the difference between the preset abnormal attribute dimension and the influence of the enterprise risk.
For the above example, during epidemic situation prevention and control, for the dimension of the true diagnosis disease case in the survey dimension, as long as the number of the enterprises to be detected filled in the dimension is not 0, the detection enterprises have a huge enterprise risk, and thus it can be known that the risk level corresponding to the confirmed cases is very high, and assuming that ten levels of 0 to 9 are used as the judgment of the risk level, the risk level corresponding to the preset abnormal attribute dimension of the confirmed cases should be set to 9.
And S204, determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value.
In the step, the number of the staff under each preset abnormal attribute dimension in the enterprise information filled by the enterprise to be detected is determined, and weighted calculation is performed according to the risk level corresponding to each preset abnormal attribute dimension with the number of the staff larger than the preset value, so that the enterprise risk level of the enterprise to be detected is determined.
Here, for different preset abnormal attribute dimensions, because each preset abnormal attribute dimension has different influence on the risk of the enterprise, the preset values of the corresponding number of employees are different.
For the above example, the preset abnormal attribute dimensions of the abnormal symptom number, the isolation observation number, the suspected case, and the confirmed case are provided, as long as there is an employee in any dimension of the enterprise to be detected, there is a certain enterprise risk in the enterprise to be detected, and therefore the preset value of the number of employees in the preset abnormal attribute dimensions is set to 0.
Here, the weight coefficient corresponding to each preset abnormal attribute dimension reflects that a certain preset abnormal attribute dimension is determining the enterprise risk level, and the larger the influence is on the enterprise risk level, the larger the weight coefficient is.
For the above example, during epidemic situation prevention and control, as long as the number of employees of the enterprise to be detected in the preset abnormal attribute dimensions of abnormal symptom number, suspected case and confirmed case is not zero, the enterprise risk of the enterprise to be detected in the process of repeating the work will be very high, and the weighting coefficients corresponding to the preset abnormal attribute dimensions need to be set to be higher, so as to better determine the enterprise risk level of the enterprise to be detected.
The enterprise risk level determining method provided by the embodiment of the application obtains the number of employees of an enterprise to be detected under each preset abnormal attribute dimension from enterprise information which passes verification according to the obtained enterprise information filled by the enterprise to be detected, determines a risk level corresponding to each preset abnormal attribute dimension according to the mapping relation between each preset abnormal attribute dimension and enterprise risks, determines the enterprise risk level of the enterprise to be detected through the risk level of each preset abnormal attribute dimension, the number of employees of which is greater than the preset value, directly determines the risk level of the enterprise to be detected according to the number of employees under the preset abnormal attribute dimension in the enterprise information filled by the enterprise to be detected and the mapping relation between the preset abnormal attribute dimension and enterprise risks, analyzes and determines the risk level of the enterprise to be detected in a background manner, so that the enterprise information input by workers can be saved, And the processing time of enterprise information is analyzed, so that the efficiency of enterprise risk detection is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for determining an enterprise risk level according to another embodiment of the present application. As shown in fig. 3, a method for determining an enterprise risk level according to an embodiment of the present application includes:
s301, acquiring enterprise information filled by the enterprise to be detected.
S302, acquiring the number of the employees of the enterprise to be detected in each preset abnormal attribute dimension from the verified enterprise information.
S303, determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk.
S304, determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value.
S305, storing the enterprise information of the enterprise to be detected and the enterprise risk level of the enterprise to be detected in an enterprise storage area.
In the step, the enterprise information filled by the enterprise to be detected and the enterprise risk level of the enterprise to be detected are correspondingly stored in an enterprise storage area.
Here, for the storage of the enterprise information of the enterprise to be detected, the storage may be performed according to different enterprises, all enterprise information of one enterprise is stored together, or when multiple enterprises are managed, information of multiple enterprises with the same dimensionality is stored together, and for the above example, classified storage may be performed according to the filling direction during filling, and basic information of the enterprise, the simulation and re-work information of the enterprise, prevention and control measures of the enterprise, and the like; the enterprise risk level of the enterprise can be stored, and enterprises with the same enterprise risk level are stored together.
The enterprise risk level of an enterprise is dynamically changed, and after the enterprise risk level corresponding to the enterprise is changed, the storage area of the enterprise needs to be correspondingly adjusted.
For example, when enterprise a starts to perform statistics, the enterprise risk level of enterprise a is high, and when the statistics is performed again as the preventive measures of enterprise a improve, the enterprise risk level of enterprise a drops to low, and at this time, the storage location of enterprise a needs to be changed from a high-level area to a low-level area.
The descriptions of S301 to S304 may refer to the descriptions of S201 to S204, and the same technical effects can be achieved, which are not described in detail herein.
Further, the enterprise information is verified as follows: determining the enterprise name and the enterprise code of the enterprise to be detected from the enterprise information; determining whether the business name exists and the business code is consistent with a docket business code associated with the business name; if yes, determining that the enterprise information passes verification; if not, determining that the enterprise information is abnormal.
After enterprise information filled by an enterprise to be detected is received, determining an enterprise name and an enterprise code of the enterprise to be detected from the enterprise information, detecting whether the enterprise name exists and whether the submitted enterprise code is consistent with a record-keeping enterprise code of the enterprise name during recording, and if the judgment condition is met, determining that the enterprise information is verified to be passed; and if the conditions are not completely met, determining that the enterprise information filled by the enterprise to be detected is abnormal.
Here, the determination of whether the enterprise name exists is a determination of whether the enterprise exists, and after the enterprise is determined to exist, it is determined whether the enterprise name and the enterprise code are consistent, so as to avoid the situation that the filling error occurs when the enterprise information is filled.
When the enterprise information is determined to be abnormal, prompt information needs to be sent to remind the enterprise to be detected to repopulate and submit the modified enterprise information until the enterprise information submitted by the enterprise to be detected passes verification.
Here, in the process of verifying the enterprise information, in addition to verifying whether the basic information of the enterprise is correct, a mandatory filling option may be set in the setting process of filling information, and when the enterprise information submitted by the enterprise to be detected is also abnormal, in the setting process, under the condition that some mandatory filling options are not filled, the enterprise information cannot be submitted.
Further, the determining method further includes: and in response to a query instruction of a user, determining at least one display dimension based on the query information indicated in the query instruction, and displaying the enterprise information in each display dimension.
In the step, at least one display dimension which needs to be displayed is determined according to a query instruction of a user, and the enterprise information is displayed under each determined display dimension.
Here, the display dimension refers to a plurality of regions determined to be displayed according to a query instruction of a user, and may be consistent with a division dimension when storing enterprise information, or may be displayed according to a region where an enterprise is located.
For example, after the information of a plurality of enterprises is collected, the enterprise information of the plurality of enterprises included in a certain management area (a park, a science and technology park, etc.) can be displayed; the method can also be displayed according to the control measures of the enterprise for the epidemic situation, such as the implementation condition of the control measures of the enterprise (no measures are made, the measures are drafted, the measures are made, the measure scheme is reported, and the like).
Here, when information display is performed according to different display dimensions, display may be performed through a bar graph, a pie graph, or the like, so that a worker can better know the prevention and control progress of an enterprise and can better take an assistance measure.
The enterprise risk level determining method provided by the embodiment of the application obtains the number of employees of an enterprise to be detected in each preset abnormal attribute dimension from enterprise information which is obtained and filled in by the enterprise to be detected, determines the risk level corresponding to each preset abnormal attribute dimension according to the mapping relation between each preset abnormal attribute dimension and enterprise risk, determines the enterprise risk level of the enterprise to be detected by the risk level of each preset abnormal attribute dimension, wherein the number of employees is greater than the preset value, directly determines the risk level of the enterprise to be detected according to the number of employees in the preset abnormal attribute dimension in the enterprise information filled in by the enterprise to be detected and the mapping relation between the preset abnormal attribute dimension and enterprise risk, analyzes and determines the risk level of the enterprise to be detected in background, and stores the enterprise information and the corresponding enterprise risk level, the processing time for entering enterprise information and analyzing the enterprise information by the staff can be saved, and the efficiency of enterprise risk detection is improved.
Referring to fig. 4 and 5, fig. 4 is a schematic structural diagram of an enterprise risk level determination apparatus according to an embodiment of the present disclosure, and fig. 5 is a second schematic structural diagram of an enterprise risk level determination apparatus according to an embodiment of the present disclosure. As shown in fig. 4, the determining means 400 includes:
the information obtaining module 410 is configured to obtain enterprise information filled by the enterprise to be detected.
And the quantity determining module 420 is configured to obtain the quantity of the employees of the enterprise to be detected in each preset abnormal attribute dimension from the verified enterprise information.
The first determining module 430 is configured to determine a risk level corresponding to each preset abnormal attribute dimension based on a mapping relationship between each preset abnormal attribute dimension and an enterprise risk.
And a second determining module 440, configured to determine an enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of employees being greater than the preset value.
Further, as shown in fig. 5, the determining apparatus 400 further includes an enterprise information checking module 450, where the enterprise information checking module 450 is configured to:
determining the enterprise name and the enterprise code of the enterprise to be detected from the enterprise information;
detecting whether the business name exists and whether the business code is consistent with a docket business code associated with the business name;
if yes, determining that the enterprise information passes verification;
if not, determining that the enterprise information is abnormal.
Further, the preset abnormal attribute dimension comprises at least one of contact with abnormal personnel within a preset time period, an abnormal area from and to within a preset time period, and health conditions of the personnel.
Further, as shown in fig. 5, the determining apparatus 400 further includes a data storing module 460, where the data storing module 460 includes:
and storing the enterprise information of the enterprise to be detected and the enterprise risk level of the enterprise to be detected in an enterprise storage area.
Further, as shown in fig. 5, the determining apparatus 400 further includes a display module 470, where the display module 470 is configured to:
and in response to a query instruction of a user, determining at least one display dimension based on the query information indicated in the query instruction, and displaying the enterprise information in each display dimension.
The determining device provided by the embodiment of the application obtains the number of employees of the enterprise to be detected in each preset abnormal attribute dimension from the enterprise information which passes the verification according to the obtained enterprise information which is filled by the enterprise to be detected, determines the risk level corresponding to each preset abnormal attribute dimension according to the mapping relation between each preset abnormal attribute dimension and the enterprise risk, determines the enterprise risk level of the enterprise to be detected by analyzing the risk level of each preset abnormal attribute dimension, which is larger than the preset value, directly according to the number of employees in the preset abnormal attribute dimension in the enterprise information which is filled by the enterprise to be detected and the mapping relation between the preset abnormal attribute dimension and the enterprise risk in the background, determines the risk level of the enterprise to be detected, and can save the processing time for the staff to enter the enterprise information and analyze the enterprise information, the method is beneficial to improving the enterprise risk detection efficiency.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 6, the electronic device 600 includes a processor 610, a memory 620, and a bus 630.
The memory 620 stores machine-readable instructions executable by the processor 610, when the electronic device 600 runs, the processor 610 communicates with the memory 620 through the bus 630, and when the machine-readable instructions are executed by the processor 610, the steps of the method for determining the enterprise risk level in the embodiment of the method shown in fig. 2 and fig. 3 may be executed.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining an enterprise risk level in the method embodiments shown in fig. 2 and fig. 3 may be executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for determining a risk level of an enterprise, the method comprising:
acquiring enterprise information filled by an enterprise to be detected;
acquiring the number of employees of the enterprise to be detected under each preset abnormal attribute dimension from the verified enterprise information;
determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk;
and determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value.
2. The method of claim 1, wherein the business information is verified as follows:
determining the enterprise name and the enterprise code of the enterprise to be detected from the enterprise information;
determining whether the business name exists and the business code is consistent with a docket business code associated with the business name;
if yes, determining that the enterprise information passes verification;
if not, determining that the enterprise information is abnormal.
3. The determination method according to claim 1, wherein the preset abnormal attribute dimension comprises at least one of contact with abnormal personnel within a preset time period, traffic to and from abnormal areas within a preset time period, and health conditions of staff.
4. The method for determining according to claim 1, wherein after determining the enterprise risk level of the enterprise to be detected, further comprising:
and storing the enterprise information of the enterprise to be detected and the enterprise risk level of the enterprise to be detected in an enterprise storage area.
5. The determination method according to any one of claims 1 to 4, characterized in that the determination method further comprises:
and in response to a query instruction of a user, determining at least one display dimension based on the query information indicated in the query instruction, and displaying the enterprise information in each display dimension.
6. An apparatus for determining a risk level of an enterprise, the apparatus comprising:
the information acquisition module is used for acquiring enterprise information filled by the enterprise to be detected;
the quantity determining module is used for acquiring the quantity of the employees of the enterprise to be detected under each preset abnormal attribute dimension from the verified enterprise information;
the first determining module is used for determining a risk level corresponding to each preset abnormal attribute dimension based on the mapping relation between each preset abnormal attribute dimension and the enterprise risk;
and the second determining module is used for determining the enterprise risk level of the enterprise to be detected according to the risk level corresponding to each preset abnormal attribute dimension with the number of the employees larger than the preset value.
7. The apparatus of claim 6, further comprising an enterprise information verification module configured to:
determining the enterprise name and the enterprise code of the enterprise to be detected from the enterprise information;
detecting whether the business name exists and whether the business code is consistent with a docket business code associated with the business name;
if yes, determining that the enterprise information passes verification;
if not, determining that the enterprise information is abnormal.
8. The determination apparatus according to claim 6, wherein the preset abnormality attribute dimension includes at least one of contact with abnormal personnel within a preset time period, traffic to and from abnormal areas within a preset time period, and health conditions of staff.
9. An electronic device, comprising: processor, memory and bus, said memory storing machine-readable instructions executable by said processor, said processor and said memory communicating over said bus when an electronic device is running, said machine-readable instructions when executed by said processor performing the steps of the method of determining a risk level of an enterprise according to any of claims 1 to 5.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for determining a risk level of an enterprise according to any one of claims 1 to 5.
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