CN112381407A - Credit weighting double-random supervision method based on random algorithm - Google Patents
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Abstract
The invention discloses a credit weighting double-random supervision method based on a random algorithm, which belongs to the field of public credit, and aims to solve the technical problem that how to solve the problems that a government organization has more enterprises in the supervision process and cannot perform key supervision on the enterprises with lower credit rating, and the adopted technical scheme is as follows: the method adopts a mode of basic weight and temporary weight to carry out random spot check, ensures that enterprises with low credit can be supervised in a key way, and realizes more efficient supervision; the method comprises the following specific steps: s1, establishing a public credit information platform; s2, establishing a weight rule: formulating a weight index; s3, randomly extracting the enterprise to be inspected; s4, randomly extracting inspectors and followers: according to the inspection requirement, uniformly and randomly inspecting personnel and follower personnel; s5, generating a random task list; and S6, carrying out the completed supervision, uploading the supervision result, and updating the random probability of the next enterprise to the step S2 according to the corresponding increase or decrease of the temporary weight of the supervision result.
Description
Technical Field
The invention relates to the field of public credit, in particular to a credit weighting double-random supervision method based on a random algorithm.
Background
The random method includes random sampling and random grouping, and the method has simple randomization and grouping randomization.
In the prior art, government supervision units adopt an equal random method to realize enterprise supervision, and enterprises needing regular inspection are randomly extracted with equal probability in the supervised enterprises. The prior art can not really meet the actual requirements of government regulatory units and can not realize key supervision on key attention enterprises. The current government supervises the randomness that government personnel can not accomplish the supervisory personnel, has the risk of communicating privately, influences the accuracy nature of enterprise's supervision, influences fair and impartial simultaneously.
Therefore, how to solve the problem that the government organization has a plurality of enterprises in the supervision process and cannot perform key supervision on the enterprises with low credit rating is an urgent need to be solved at present.
Disclosure of Invention
The invention provides a credit weighting double-random supervision method based on a random algorithm, and aims to solve the problems that a government organization has more enterprises in the supervision process and cannot supervise the enterprises with lower credit rating.
The technical task of the invention is realized in the following way, namely a method for carrying out credit weighting double random supervision based on a random algorithm, which adopts a mode of basic weight and temporary weight to carry out random spot check, ensures that enterprises with low credit can be intensively supervised, and realizes more efficient supervision; the method comprises the following specific steps:
s1, establishing a public credit information platform: establishing a public credit information platform, maintaining government personnel needing to participate in inspection and enterprise information needing to be inspected, and forming a supervision enterprise database;
s2, establishing a weight rule: formulating a weight index;
s3, randomly extracting the enterprise to be inspected;
s4, randomly extracting inspectors and followers: according to the inspection requirement, uniformly and randomly inspecting personnel and follower personnel;
s5, generating a random task list: generating an inspection task list according to the enterprises and the inspectors which are randomly extracted, and executing the supervision result and recording the supervision result into the next double random weight calculation to complete the supervision work of the enterprises;
and S6, carrying out the completed supervision, uploading the supervision result, and updating the random probability of the next enterprise to the step S2 according to the corresponding increase or decrease of the temporary weight of the supervision result.
Preferably, the basic weight refers to a basic random weight value of the supervised enterprise, the default is 100 points, and when a newly added supervised enterprise is supervised by other government units, the basic weight inherits the original value.
Preferably, the temporary weight is a value which is calculated according to the weight index every day and is updated regularly, so that the accuracy of the random probability is ensured.
Preferably, the weighting rule set in step S2 is enterprise credit information collected through a public credit information platform, and the specific conditions are as follows:
if the credit information of the enterprise has the information of losing credit, correspondingly increasing random weight according to the degree of losing credit;
and secondly, if the credit information of the enterprise has credit keeping information, correspondingly reducing the random weight according to the continuity and the criticality of the credit keeping information.
Preferably, the weight index is flexibly configured according to the credit information collected by the user.
More preferably, the weight index includes an index for decreasing weight and an index for increasing weight;
the value of the increased or decreased weight is determined according to the degree of distrust and conviction, such as that the administrative penalty influence is lighter +1 score, and the distrust executant influence is heavier +5 score.
More preferably, the weight-reduction indicators include administrative approval and a class a taxpayer; the weighting-added indexes include administrative penalties and loss-of-trust executives.
Preferably, in step S3, the randomly extracted enterprises to be checked are as follows:
s301, accumulating the weight of each enterprise participating in random, and acquiring the weight jurisdiction of each enterprise;
s302, randomly generating a random number in the weight interval, wherein the random number falls in which weight interval, and the enterprise in the weight interval is the randomly acquired enterprise.
The credit weighting double-random supervision method based on the random algorithm has the following advantages:
the invention belongs to a part of double random supervision functions of government units in the field of public credit, weights are distributed according to the credit level of enterprises, and the processing method of the enterprises is supervised randomly according to the weights, so that the credit weighted randomness is beneficial to improving the working efficiency of the government units and optimizing the operator environment; the credit weighted double random supervision is served for government units, and a complete government unit organization and personnel thereof are required to be maintained on a public credit information platform based on the organization structure of a user to form a supervision personnel database for random supervision personnel; random enterprises are enterprises which need to be supervised by each government unit according to the functions of the enterprises, and need to be maintained on a public credit information platform to form a supervised enterprise database, and the same enterprise can be supervised by different units at the same time;
the invention solves the problems that a government organization has more enterprises in the supervision process and cannot aim at the key supervision of the enterprises with lower credit rating, adopts a random spot check method of basic weight and temporary weight to ensure that the probability of spot check of the enterprises with lower credit rating is high and the probability of spot check of the enterprises with higher credit rating is low, thereby realizing more efficient supervision;
the invention can help government units to realize more distressed monitoring and more released crediting when monitoring enterprises with large data volume, thereby improving the working efficiency of monitoring units; the illegal and distrusted enterprises can be better supervised, potential safety hazards are reduced, the enterprises are prevented from being in the bud, and the enterprise information management system is favorable for improving the operator environment of the whole society;
in the process of enterprise supervision of government units, different random weights are made according to the credit rating of the enterprise, and the enterprise is randomly checked and supervised according to the weights;
in the government supervision process, randomly spot-checking enterprises according to weights, randomly acquiring inspectors after randomly spot-checking the enterprises, generating a task list, and carrying out supervision work on the enterprises; in the supervision after execution, the temporary weight is correspondingly increased or decreased according to the supervision result, the random probability of the next enterprise is updated, and the supervision accuracy is improved;
the invention avoids the risk of private communication of the government supervision personnel of the prior government, not only can ensure the accuracy of enterprise supervision, but also ensures the fairness and justice.
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The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for credit weighted double random policing based on a random algorithm.
Detailed Description
The method of credit weighted double random policing based on random algorithm of the present invention is described in detail below with reference to the drawings and the specific embodiments of the specification.
Example (b):
as shown in the attached figure 1, the credit weighting double random supervision method based on the random algorithm adopts a mode of basic weight and temporary weight to carry out random spot check, ensures that enterprises with low credit can be intensively supervised, and realizes more efficient supervision; the method comprises the following specific steps:
s1, establishing a public credit information platform: establishing a public credit information platform, maintaining government personnel needing to participate in inspection and enterprise information needing to be inspected, and forming a supervision enterprise database;
s2, establishing a weight rule: formulating a weight index;
s3, randomly extracting the enterprise to be inspected;
s4, randomly extracting inspectors and followers: according to the inspection requirement, uniformly and randomly inspecting personnel and follower personnel;
s5, generating a random task list: generating an inspection task list according to the enterprises and the inspectors which are randomly extracted, and executing the supervision result and recording the supervision result into the next double random weight calculation to complete the supervision work of the enterprises;
and S6, carrying out the completed supervision, uploading the supervision result, and updating the random probability of the next enterprise to the step S2 according to the corresponding increase or decrease of the temporary weight of the supervision result.
The basic weight in this embodiment refers to a basic random weight value of a supervised enterprise, the default is 100 points, and when a newly added supervising enterprise is supervised by other government units, the basic weight inherits the original value.
The temporary weight in the embodiment refers to a value which is calculated every day according to the weight index and is updated regularly, so that the accuracy of the random probability is ensured. The weight index is flexibly configured according to different credit information collected by the user. The weight indexes comprise indexes for reducing the weight and indexes for increasing the weight; the value of the increased or decreased weight is determined according to the degree of distrust and conviction, such as that the administrative penalty influence is lighter +1 score, and the distrust executant influence is heavier +5 score. The indexes for reducing the weight comprise administrative examination and approval and A-level taxpayers; the weighting-added indexes include administrative penalties and loss-of-trust executives.
The weighting rule formulated in step S2 in this embodiment is enterprise credit information collected by a public credit information platform, and the specific conditions are as follows:
if the credit information of the enterprise has the information of losing credit, correspondingly increasing random weight according to the degree of losing credit;
and secondly, if the credit information of the enterprise has credit keeping information, correspondingly reducing the random weight according to the continuity and the criticality of the credit keeping information.
In this embodiment, the step S3 randomly extracts the inspected enterprise as follows:
s301, accumulating the weight of each enterprise participating in random, and acquiring the weight jurisdiction of each enterprise;
s302, randomly generating a random number in the weight interval, wherein the random number falls in which weight interval, and the enterprise in the weight interval is the randomly acquired enterprise.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art 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 the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A credit weighting double random supervision method based on a random algorithm is characterized in that random spot check is carried out by adopting a mode of basic weight and temporary weight, so that enterprises with low key supervision credit can be ensured, and more efficient supervision is realized; the method comprises the following specific steps:
s1, establishing a public credit information platform: establishing a public credit information platform, maintaining government personnel needing to participate in inspection and enterprise information needing to be inspected, and forming a supervision enterprise database;
s2, establishing a weight rule: formulating a weight index;
s3, randomly extracting the enterprise to be inspected;
s4, randomly extracting inspectors and followers: according to the inspection requirement, uniformly and randomly inspecting personnel and follower personnel;
s5, generating a random task list: generating an inspection task list according to the enterprises and the inspectors which are randomly extracted, and executing the supervision result and recording the supervision result into the next double random weight calculation to complete the supervision work of the enterprises;
and S6, carrying out the completed supervision, uploading the supervision result, and updating the random probability of the next enterprise to the step S2 according to the corresponding increase or decrease of the temporary weight of the supervision result.
2. The method of claim 1, wherein the basic weight is a basic random weight value of a supervised enterprise, the default is 100 points, and when a newly added supervised enterprise is supervised by other government units, the basic weight inherits the original value.
3. A method for weighted double random supervision of credit based on stochastic algorithms according to claim 1, wherein the temporary weights are calculated daily based on weight index and updated value.
4. The method for credit weighted double random administration based on stochastic algorithm according to claim 1, wherein the weighting rules established in step S2 are enterprise credit collected through public credit platform as follows:
if the credit information of the enterprise has the information of losing credit, correspondingly increasing random weight according to the degree of losing credit;
and secondly, if the credit information of the enterprise has credit keeping information, correspondingly reducing the random weight according to the continuity and the criticality of the credit keeping information.
5. Method for credit weighted double random supervision based on a random algorithm according to any of the claims 1-4 characterized in that the weight index is flexibly configured depending on the credit information collected by the user.
6. A method for weighted double random policing of credit based on stochastic algorithms according to claim 5 wherein the weight metrics comprise a decreasing weight metric and an increasing weight metric;
the value of the weight to be increased or decreased is determined according to the degree of loss of confidence and the degree of conservation of confidence.
7. The method for credit weighted double random policing based on stochastic algorithms of claim 6, wherein the indicators of reduced weight comprise administrative approval and class A taxpayers; the weighting-added indexes include administrative penalties and loss-of-trust executives.
8. The method for credit weighted double random administration based on stochastic algorithm of claim 1, wherein the randomly drawn enterprises in step S3 are as follows:
s301, accumulating the weight of each enterprise participating in random, and acquiring the weight jurisdiction of each enterprise;
s302, randomly generating a random number in the weight interval, wherein the random number falls in which weight interval, and the enterprise in the weight interval is the randomly acquired enterprise.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113743796A (en) * | 2021-09-08 | 2021-12-03 | 交通运输部公路科学研究所 | Multi-constraint condition double random spot check method based on weight |
| CN115796636A (en) * | 2022-10-19 | 2023-03-14 | 江苏领悟信息技术有限公司 | Double random extraction method for detection and inspection |
| WO2023123079A1 (en) * | 2021-12-28 | 2023-07-06 | 中国电子技术标准化研究院华东分院 | Multi-agent double-random distribution method and system based on review task |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113743796A (en) * | 2021-09-08 | 2021-12-03 | 交通运输部公路科学研究所 | Multi-constraint condition double random spot check method based on weight |
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| WO2023123079A1 (en) * | 2021-12-28 | 2023-07-06 | 中国电子技术标准化研究院华东分院 | Multi-agent double-random distribution method and system based on review task |
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