CN116579838A - Intelligent supervision method and system based on big data - Google Patents

Abstract

The invention provides an intelligent supervision method and system based on big data. Whether the abnormality exists or not is analyzed by automatically acquiring the related data of the enterprise, and when the abnormality exists, the credit data sniffer is utilized to acquire the credit data so as to judge the credit abnormality risk, so that the credit data sniffer flow can be automatically triggered to timely know the credit condition of the enterprise, the corresponding sniffer can be configured according to the characteristics of the enterprise, and the accuracy and the safety of data sniffer are ensured.

Description

Intelligent supervision method and system based on big data

Technical Field

The invention relates to the technical field of big data, in particular to an intelligent supervision method and system based on big data.

Background

As known, credit supervision is an important measure for ensuring the health of economic activities, and along with the development of computer technology, network technology and the like, a credit supervision system also enters a digitizing stage, but the existing credit supervision system is not intelligent enough, has hysteresis in acquiring information, and is not accurate and comprehensive in acquiring data.

What is needed is an intelligent supervision method and system based on big data.

Disclosure of Invention

Based on the problems, the invention provides the intelligent supervision method and the intelligent supervision system based on the big data, and the scheme of the invention can not only automatically trigger the credit data sniffing flow and timely know the credit condition of an enterprise, but also configure the corresponding sniffer according to the characteristics of the enterprise, thereby ensuring the accuracy and the safety of data sniffing.

In view of the foregoing, an aspect of the present invention proposes a big data-based intelligent supervision method for performing credit supervision on enterprises in an intelligent campus, including:

registering a first enterprise on a credit server when registering the first enterprise;

the credit investigation server configures a credit data sniffer for the first enterprise according to the submitted first enterprise information;

the credit investigation server generates first credit investigation index information of the first enterprise and sends the first credit investigation index information to a central cloud server;

the credit investigation server acquires first credit data of the first enterprise and analyzes the first credit data to obtain a first credit analysis result;

judging whether a preset first abnormal condition exists in the first credit analysis result;

If the first abnormal condition exists, sending a credit data sniffing instruction to a corresponding first credit data sniffer in the credit data sniffers;

connecting the first credit data sniffer to sniffer nodes in a corresponding network;

the first credit data sniffer acquires second credit data;

judging whether the first enterprise has credit abnormal risks according to the second credit data analysis;

and if the credit abnormal risk exists, sending out an early warning prompt and a rectifying instruction.

Optionally, the step of configuring the credit data sniffer for the first enterprise by the credit server according to the submitted first enterprise information includes:

extracting first enterprise feature data of the first enterprise from the first enterprise information;

determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model;

an associated enterprise of the first enterprise is determined, a second sniffer of the associated enterprise is associated with a first sniffer of the first enterprise, and the first sniffer and the second sniffer are used as the credit data sniffer of the first enterprise.

Optionally, the first credit data sniffer is an intelligent mobile sniffer; the method also comprises the steps of:

respectively establishing a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located;

determining a reference data acquisition point and a reference data acquisition model of the first physical space and the second physical space according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model;

after the intelligent mobile sniffer acquires the second credit data, controlling the intelligent mobile sniffer to acquire first reference data at the reference data acquisition point according to the reference data acquisition model;

the step of the first credit data sniffer obtaining second credit data and judging whether the first enterprise has credit abnormal risk according to the analysis of the second credit data comprises the following steps:

and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

Optionally, the step of analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data and performing credit prediction includes:

Preprocessing the second credit data and the first reference data, and extracting first characteristic data according to a preset rule;

inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report;

and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

Optionally, the intelligent mobile sniffer comprises a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a network connection assembly, a control processor, a data acquisition unit, a positioning unit, an optical communication assembly, a mechanical foot, a power assembly and a transmission assembly; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body;

the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer;

The control processor is deployed with a data sniffing model and a data acquisition model which are trained by utilizing a neural network;

the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit;

the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit;

the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer

A cavity is formed between the bottom end of the heat dissipation shell and the bottom end of the sniffer body, a dust removal component and a fan component are arranged in the cavity, and the dust removal component and the fan component are respectively and electrically connected with the power component.

Another aspect of the present invention provides a big data based intelligent supervision system for performing credit supervision on an enterprise in an intelligent campus, including a registration terminal, a credit data sniffer, a credit investigation server and a central cloud server, wherein,

the registration terminal is configured to: when first enterprise registration is processed, first enterprise information of the first enterprise is sent to the credit investigation server to register;

the credit server is configured to:

Configuring the credit data sniffer for the first enterprise according to the received first enterprise information;

generating first credit index information of the first enterprise, and sending the first credit index information to the central cloud server;

acquiring first credit data of the first enterprise, and analyzing the first credit data to obtain a first credit analysis result;

judging whether a preset first abnormal condition exists in the first credit analysis result;

if the first abnormal condition exists, sending a credit data sniffing instruction to a corresponding first credit data sniffer in the credit data sniffers;

connecting the first credit data sniffer to sniffer nodes in a corresponding network;

controlling the first credit data sniffer to acquire second credit data, and analyzing and judging whether the first enterprise has credit abnormal risks according to the second credit data;

and if the credit abnormal risk exists, sending out an early warning prompt and a rectifying instruction.

Optionally, the step of configuring, by the credit server, a credit data sniffer for the first enterprise according to the submitted first enterprise information is specifically configured to:

Extracting first enterprise feature data of the first enterprise from the first enterprise information;

determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model;

an associated enterprise of the first enterprise is determined, a second sniffer of the associated enterprise is associated with a first sniffer of the first enterprise, and the first sniffer and the second sniffer are used as the credit data sniffer of the first enterprise.

Optionally, the first credit data sniffer is an intelligent mobile sniffer; the central cloud server is configured to:

respectively establishing a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located;

determining a reference data acquisition point and a reference data acquisition model of the first physical space and the second physical space according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model;

after controlling the first credit data sniffer to acquire the second credit data, the credit server is configured to: controlling the intelligent mobile sniffer to acquire first reference data at the reference data acquisition point according to the reference data acquisition model;

The step of controlling the first credit data sniffer to acquire second credit data and judging whether the first enterprise has credit abnormal risk according to the second credit data analysis, wherein the credit investigation server is configured to:

and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

Optionally, in the step of analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data and performing credit prediction, the credit investigation server is configured to:

preprocessing the second credit data and the first reference data, and extracting first characteristic data according to a preset rule;

inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report;

and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

Optionally, the intelligent mobile sniffer comprises a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a network connection assembly, a control processor, a data acquisition unit, a positioning unit, an optical communication assembly, a mechanical foot, a power assembly and a transmission assembly; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body;

The mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer;

the control processor is deployed with a data sniffing model and a data acquisition model which are trained by utilizing a neural network;

the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit;

the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit;

the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer

A cavity is formed between the bottom end of the heat dissipation shell and the bottom end of the sniffer body, a dust removal component and a fan component are arranged in the cavity, and the dust removal component and the fan component are respectively and electrically connected with the power component.

By adopting the technical scheme of the invention, the intelligent supervision system based on big data is provided with the registration terminal, the credit data sniffer, the credit investigation server and the central cloud server. Whether the abnormality exists or not is analyzed by automatically acquiring the related data of the enterprise, and when the abnormality exists, the credit data sniffer is utilized to acquire the credit data so as to judge the credit abnormality risk, so that the credit data sniffer flow can be automatically triggered to timely know the credit condition of the enterprise, the corresponding sniffer can be configured according to the characteristics of the enterprise, and the accuracy and the safety of data sniffer are ensured.

Drawings

FIG. 1 is a flow chart of a big data based intelligent supervision method provided by an embodiment of the present application;

fig. 2 is a schematic block diagram of a big data based intelligent supervision system according to one embodiment of the present application.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced otherwise than as described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.

The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

An intelligent supervision method and system based on big data according to some embodiments of the present application are described below with reference to fig. 1 to 2.

As shown in fig. 1, one embodiment of the present application provides a big data-based intelligent supervision method for performing credit supervision on enterprises in an intelligent campus, including:

registering a first enterprise on a credit server when registering the first enterprise;

the credit investigation server configures a credit data sniffer for the first enterprise according to the submitted first enterprise information;

the credit investigation server generates first credit investigation index information of the first enterprise and sends the first credit investigation index information to a central cloud server;

The credit investigation server acquires first credit data of the first enterprise and analyzes the first credit data to obtain a first credit analysis result;

judging whether a preset first abnormal condition exists in the first credit analysis result;

if the first abnormal condition exists, sending a credit data sniffing instruction to a corresponding first credit data sniffer in the credit data sniffers;

connecting the first credit data sniffer to sniffer nodes in a corresponding network;

the first credit data sniffer acquires second credit data;

judging whether the first enterprise has credit abnormal risks according to the second credit data analysis;

and if the credit abnormal risk exists, sending out an early warning prompt and a rectifying instruction.

It can be understood that when an enterprise enters a smart park, the enterprise can be registered on a credit server, and the credit server can be an edge server or an internet of things server, and can be actually arranged in an office of a first line supervision department or a smart park; the credit server configures a credit data sniffer for the first enterprise according to the submitted first enterprise information. It should be noted that the credit data sniffer may be classified into a plurality of types according to functions/performances, and each enterprise may have different operation ranges, enterprise properties, architecture, operation modes, etc., so that the corresponding credit evaluation data is different, so that each enterprise may need to use different enterprise data sniffers, for example, the credit data sniffer may be classified into a software type credit data sniffer (i.e. sniffer) and a hardware sniffer, the hardware sniffer may have different structures, be configured with different hardware elements, etc., some enterprises may perform credit data sniffer through the sniffer, some enterprises may need to perform credit data sniffer through the hardware sniffer (such as, for example, the hardware sniffer with a high security level performs data sniffer on the enterprise with a high security level), and some enterprises with a relatively complex case need to obtain accurate data through a combination of the sniffer and the hardware sniffer.

The credit investigation server generates first credit investigation index information of the first enterprise and sends the first credit investigation index information to a central cloud server (the central cloud server manages data of a plurality of intelligent parks and credit investigation servers in the intelligent parks) so that other parties can acquire credit data of the first enterprise through the central cloud server. The credit investigation server obtains first credit data of the first enterprise (such as obtaining human resource data, operation management data, industry status data, development trend data, litigation related data, asset operation data, financial benefit data, innovation capability data and enterprise/stakeholder/high-level related news data of the first enterprise through public financial report, propaganda data and the like, and the first credit data is analyzed by using a trained first anomaly judgment model to obtain a first credit analysis result and judge whether a preset first anomaly condition exists in the first credit analysis result, wherein the first anomaly condition identified by the first anomaly judgment model is an anomaly (or an anomaly that a difference between public data related to the enterprise and a development trend of the enterprise exceeds a preset difference value) existing in comparison with public data related to the enterprise in the same period as the history, but not a credit anomaly; when the first abnormal condition exists, in order to further determine whether the first enterprise has a credit abnormality, sending a credit data sniffing instruction (including but not limited to sniffing/acquisition requirement, sniffing/acquisition security requirement, privacy protection requirement, sniffing/acquisition frequency, sniffing/acquisition data type and the like) to a corresponding first credit data sniffer in the credit data sniffers; connecting the first credit data sniffer to sniffer nodes in the corresponding network (the corresponding network and sniffer nodes can be determined according to a predetermined determination rule in advance or according to the first credit analysis result); the first credit data sniffer acquires second credit data and sends the second credit data to the credit investigation server; judging whether the first enterprise has credit abnormal risks according to the second credit data analysis; if the credit abnormal risk exists, an early warning prompt and a rectification instruction are sent out, and further the intelligent park management terminal controls a patrol terminal (such as an unmanned aerial vehicle) to patrol the area where the first enterprise is located.

By adopting the technical scheme of the embodiment, whether the abnormality exists or not is analyzed by automatically acquiring the related data of the enterprise, and when the abnormality exists, the credit data sniffer is utilized to acquire the credit data so as to judge the credit abnormal risk, so that the credit data sniffer flow can be automatically triggered, the credit condition of the enterprise can be timely known, the corresponding sniffer can be configured according to the characteristics of the enterprise, and the accuracy and the safety of data sniffer are ensured.

In some possible embodiments of the present invention, the step of configuring, by the credit server, a credit data sniffer for the first enterprise according to the submitted first enterprise information includes:

extracting first enterprise feature data of the first enterprise from the first enterprise information;

determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model;

an associated enterprise of the first enterprise is determined, a second sniffer of the associated enterprise is associated with a first sniffer of the first enterprise, and the first sniffer and the second sniffer are used as the credit data sniffer of the first enterprise.

It can be understood that, in this embodiment, in order to ensure the comprehensiveness and accuracy of the acquired credit data, first enterprise feature data (such as feature data in the aspects of enterprise operation scope, enterprise properties, architecture, operation mode, etc.) of the first enterprise is extracted from the first enterprise information; determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model (the sniffer determination model is obtained by training a neural network by utilizing a large amount of enterprise characteristic data and characteristic data in aspects of sniffer function, performance and the like); determining an associated enterprise of the first enterprise (for example, determining related companies such as a stakeholder enterprise, a subsidiary enterprise, a branch enterprise, a company with the same stakeholder and the like of the first enterprise, determining the associated enterprise according to data in terms of regional characteristics, business transaction, operation scope, enterprise property, architecture, operation mode and the like, such as an enterprise with the same stakeholder and the same business dependency relationship or the same operation scope), determining whether the associated enterprise is configured with a second sniffer through a central cloud server, further acquiring credit data of the associated enterprise when the associated enterprise is not configured with the second sniffer, judging whether the configuration is necessary according to the credit data of the associated enterprise, configuring the second sniffer if necessary (for example, configuring the corresponding second sniffer if the credit level of the associated enterprise is risky according to the credit data of the associated enterprise, and taking the second sniffer and the first sniffer as the first sniffer credit data of the first sniffer through unique identifiers of the two.

It should be noted that, in this embodiment, the first/second sniffer may be a sniffer, or may be a hardware sniffer, where the execution flow of the sniffer may be: the sniffing program captures data packets on an accessed network and analyzes and processes the data packets; analyzing the data packet, extracting various information therein, such as a source IP address, a target IP address, a protocol type, a port number and the like; recording the analyzed information, including source IP address, target IP address, protocol type, port number, data packet length, etc.; filtering the data packets according to preset rules, and only reserving the data packets conforming to the rules; analyzing the filtered data packet to obtain useful information such as the type of application, the protocol used, the requested data, etc.; and sending the analyzed information to a credit server for analysis by the credit server. Since the sniffing process in the network is dynamic, new data packets and rules may appear at any time, the sniffing program in this embodiment may be continuously learned and trained by the central cloud server according to the collected new data packets and rules, so as to improve the efficiency and accuracy thereof.

In some possible embodiments of the invention, the first credit data sniffer is an intelligent mobile sniffer; the method also comprises the steps of:

respectively establishing a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located;

determining a reference data acquisition point and a reference data acquisition model of the first physical space and the second physical space according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model;

after the intelligent mobile sniffer acquires the second credit data, controlling the intelligent mobile sniffer to acquire first reference data at the reference data acquisition point according to the reference data acquisition model;

the step of the first credit data sniffer obtaining second credit data and judging whether the first enterprise has credit abnormal risk according to the analysis of the second credit data comprises the following steps:

and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

It will be appreciated that in this embodiment, the first credit data sniffer (i.e. the first sniffer, or the second sniffer, or both) is an intelligent mobile sniffer.

In order to obtain more credit data to obtain more accurate credit evaluation results, in this embodiment, a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located are respectively established; determining reference data acquisition points of the first physical space and the second physical space (for example, determining credit data to be acquired according to abnormal conditions in a first credit analysis result, determining acquisition points of related reference data according to the credit data, for example, determining that the credit data is on-time delivery rate, determining that the reference data is inventory image data, and determining that the acquisition points are warehouses) and a reference data acquisition model (for example, acquiring angles, quantity, image quality standards and the like of the image data) according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model; after the first credit data sniffer (namely the intelligent mobile sniffer) acquires the second credit data, the intelligent mobile sniffer is controlled to acquire first reference data at the reference data acquisition point according to the reference data acquisition model by combining the states (such as the object arrangement state, the object working state and the like) of the objects in the first physical space and the second physical space; and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

In some possible embodiments of the present invention, the step of analyzing and determining whether the first enterprise has a credit abnormal risk according to the second credit data and the first reference data, and performing credit prediction includes:

preprocessing the second credit data and the first reference data, and extracting first characteristic data according to a preset rule;

inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report;

and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

It can be understood that, in order to accurately perform credit assessment and risk prediction, in this embodiment, the second credit data and the first reference data are preprocessed, and first feature data are extracted according to a preset rule; inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report; and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

In some possible embodiments of the present invention, the intelligent mobile sniffer comprises a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a network connection component, a control processor, a data acquisition unit, a positioning unit, an optical communication component, a mechanical foot, a power component and a transmission component; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body;

the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer;

the control processor is deployed with a data sniffing model and a data acquisition model which are trained by utilizing a neural network;

the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit;

the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit;

the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer

A cavity is formed between the bottom end of the heat dissipation shell and the bottom end of the sniffer body, a dust removal component and a fan component are arranged in the cavity, the dust removal component and the fan component are respectively and electrically connected with the power component, the intelligent movable sniffer is favorable for improving the heat dissipation efficiency of the heat dissipation shell, timely eliminates dust in the heat dissipation shell, and prevents low equipment working efficiency caused by dust blockage when the intelligent movable sniffer works for a long time.

It will be appreciated that, in order to make the intelligent mobile sniffer more intelligent and flexible to perform data sniffing more efficiently, in this embodiment, the intelligent mobile sniffer (the structure of the intelligent mobile sniffer is not shown in the figure) includes a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a control processor, a network connection component electrically connected to the control processor, a data acquisition unit, a positioning unit, an optical communication component, a mechanical foot, a power component and a transmission component; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body; the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer; the control processor is deployed with a data sniffing model and a data acquisition model trained by using a neural network (the data sniffing/acquisition model comprises, but is not limited to, a data sniffing/acquisition object, a data sniffing/acquisition address, accessed network connection point information, a data sniffing/acquisition data type or type, a data quality standard, a data security standard, a data precision standard, a data sniffing/acquisition operation flow, a data sniffing/acquisition triggering condition and the like) so as to control the intelligent mobile sniffer to perform data sniffing and acquisition; the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit; the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit; the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer the bottom of heat dissipation shell with form a cavity between the bottom of sniffer body, be provided with dust removal subassembly and fan subassembly in the cavity, dust removal subassembly with the fan subassembly respectively with the power subassembly electricity is connected, is favorable to improving the radiating efficiency of heat dissipation shell to carry out timely clearance with the dust in the heat dissipation shell, prevent the long-time during operation of intelligent mobile sniffer leads to equipment work efficiency low because of the dust jam.

Referring to fig. 2, another embodiment of the present invention provides a big data-based intelligent supervision system for performing credit supervision on enterprises in an intelligent campus, including a registration terminal, a credit data sniffer, a credit server and a central cloud server, wherein,

the registration terminal is configured to: when first enterprise registration is processed, first enterprise information of the first enterprise is sent to the credit investigation server to register;

the credit server is configured to:

configuring the credit data sniffer for the first enterprise according to the received first enterprise information;

generating first credit index information of the first enterprise, and sending the first credit index information to the central cloud server;

acquiring first credit data of the first enterprise, and analyzing the first credit data to obtain a first credit analysis result;

judging whether a preset first abnormal condition exists in the first credit analysis result;

if the first abnormal condition exists, sending a credit data sniffing instruction to a corresponding first credit data sniffer in the credit data sniffers;

connecting the first credit data sniffer to sniffer nodes in a corresponding network;

Controlling the first credit data sniffer to acquire second credit data, and analyzing and judging whether the first enterprise has credit abnormal risks according to the second credit data;

and if the credit abnormal risk exists, sending out an early warning prompt and a rectifying instruction.

It can be understood that when an enterprise enters a smart park, the registration terminal can register the enterprise on a credit server, and the credit server can be an edge server or an internet of things server, and can be actually arranged in an office of a first line supervision department or a smart park; the credit server configures a credit data sniffer for the first enterprise according to the submitted first enterprise information. It should be noted that the credit data sniffer may be classified into a plurality of types according to functions/performances, and each enterprise may have different operation ranges, enterprise properties, architecture, operation modes, etc., so that the corresponding credit evaluation data is different, so that each enterprise may need to use different enterprise data sniffers, for example, the credit data sniffer may be classified into a software type credit data sniffer (i.e. sniffer) and a hardware sniffer, the hardware sniffer may have different structures, be configured with different hardware elements, etc., some enterprises may perform credit data sniffer through the sniffer, some enterprises may need to perform credit data sniffer through the hardware sniffer (such as, for example, the hardware sniffer with a high security level performs data sniffer on the enterprise with a high security level), and some enterprises with a relatively complex case need to obtain accurate data through a combination of the sniffer and the hardware sniffer.

The credit investigation server generates first credit investigation index information of the first enterprise and sends the first credit investigation index information to a central cloud server (the central cloud server manages data of a plurality of intelligent parks and credit investigation servers in the intelligent parks) so that other parties can acquire credit data of the first enterprise through the central cloud server. The credit investigation server obtains first credit data of the first enterprise (such as obtaining human resource data, operation management data, industry status data, development trend data, litigation related data, asset operation data, financial benefit data, innovation capability data and enterprise/stakeholder/high-level related news data of the first enterprise through public financial report, propaganda data and the like, and the first credit data is analyzed by using a trained first anomaly judgment model to obtain a first credit analysis result and judge whether a preset first anomaly condition exists in the first credit analysis result, wherein the first anomaly condition identified by the first anomaly judgment model is an anomaly (or an anomaly that a difference between public data related to the enterprise and a development trend of the enterprise exceeds a preset difference value) existing in comparison with public data related to the enterprise in the same period as the history, but not a credit anomaly; when the first abnormal condition exists, in order to further determine whether the first enterprise has a credit abnormality, sending a credit data sniffing instruction (including but not limited to sniffing/acquisition requirement, sniffing/acquisition security requirement, privacy protection requirement, sniffing/acquisition frequency, sniffing/acquisition data type and the like) to a corresponding first credit data sniffer in the credit data sniffers; connecting the first credit data sniffer to sniffer nodes in the corresponding network (the corresponding network and sniffer nodes can be determined according to a predetermined determination rule in advance or according to the first credit analysis result); the first credit data sniffer acquires second credit data and sends the second credit data to the credit investigation server; judging whether the first enterprise has credit abnormal risks according to the second credit data analysis; if the credit abnormal risk exists, an early warning prompt and a rectification instruction are sent out, and further the intelligent park management terminal controls a patrol terminal (such as an unmanned aerial vehicle) to patrol the area where the first enterprise is located.

By adopting the technical scheme of the embodiment, whether the abnormality exists or not is analyzed by automatically acquiring the related data of the enterprise, and when the abnormality exists, the credit data sniffer is utilized to acquire the credit data so as to judge the credit abnormal risk, so that the credit data sniffer flow can be automatically triggered, the credit condition of the enterprise can be timely known, the corresponding sniffer can be configured according to the characteristics of the enterprise, and the accuracy and the safety of data sniffer are ensured.

It should be noted that the block diagram of the big data based intelligent supervision system shown in fig. 2 is only schematic, and the number of the illustrated modules does not limit the protection scope of the present invention.

In some possible embodiments of the present invention, the step of configuring, by the credit server, a credit data sniffer for the first enterprise according to the submitted first enterprise information is specifically configured to:

extracting first enterprise feature data of the first enterprise from the first enterprise information;

determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model;

an associated enterprise of the first enterprise is determined, a second sniffer of the associated enterprise is associated with a first sniffer of the first enterprise, and the first sniffer and the second sniffer are used as the credit data sniffer of the first enterprise.

It can be understood that, in this embodiment, in order to ensure the comprehensiveness and accuracy of the acquired credit data, first enterprise feature data (such as feature data in the aspects of enterprise operation scope, enterprise properties, architecture, operation mode, etc.) of the first enterprise is extracted from the first enterprise information; determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model (the sniffer determination model is obtained by training a neural network by utilizing a large amount of enterprise characteristic data and characteristic data in aspects of sniffer function, performance and the like); determining an associated enterprise of the first enterprise (for example, determining related companies such as a stakeholder enterprise, a subsidiary enterprise, a branch enterprise, a company with the same stakeholder and the like of the first enterprise, determining the associated enterprise according to data in terms of regional characteristics, business transaction, operation scope, enterprise property, architecture, operation mode and the like, such as an enterprise with the same stakeholder and the same business dependency relationship or the same operation scope), determining whether the associated enterprise is configured with a second sniffer through a central cloud server, further acquiring credit data of the associated enterprise when the associated enterprise is not configured with the second sniffer, judging whether the configuration is necessary according to the credit data of the associated enterprise, configuring the second sniffer if necessary (for example, configuring the corresponding second sniffer if the credit level of the associated enterprise is risky according to the credit data of the associated enterprise, and taking the second sniffer and the first sniffer as the first sniffer credit data of the first sniffer through unique identifiers of the two.

It should be noted that, in this embodiment, the first/second sniffer may be a sniffer, or may be a hardware sniffer, where the execution flow of the sniffer may be: the sniffing program captures data packets on an accessed network and analyzes and processes the data packets; analyzing the data packet, extracting various information therein, such as a source IP address, a target IP address, a protocol type, a port number and the like; recording the analyzed information, including source IP address, target IP address, protocol type, port number, data packet length, etc.; filtering the data packets according to preset rules, and only reserving the data packets conforming to the rules; analyzing the filtered data packet to obtain useful information such as the type of application, the protocol used, the requested data, etc.; and sending the analyzed information to a credit server for analysis by the credit server. Since the sniffing process in the network is dynamic, new data packets and rules may appear at any time, the sniffing program in this embodiment may be continuously learned and trained by the central cloud server according to the collected new data packets and rules, so as to improve the efficiency and accuracy thereof.

In some possible embodiments of the invention, the first credit data sniffer is an intelligent mobile sniffer; the central cloud server is configured to:

respectively establishing a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located;

determining a reference data acquisition point and a reference data acquisition model of the first physical space and the second physical space according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model;

after controlling the first credit data sniffer to acquire the second credit data, the credit server is configured to: controlling the intelligent mobile sniffer to acquire first reference data at the reference data acquisition point according to the reference data acquisition model;

the step of controlling the first credit data sniffer to acquire second credit data and judging whether the first enterprise has credit abnormal risk according to the second credit data analysis, wherein the credit investigation server is configured to:

and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

It will be appreciated that in this embodiment, the first credit data sniffer (i.e. the first sniffer, or the second sniffer, or both) is an intelligent mobile sniffer.

In order to obtain more credit data to obtain more accurate credit evaluation results, in this embodiment, a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located are respectively established; determining reference data acquisition points of the first physical space and the second physical space (for example, determining credit data to be acquired according to abnormal conditions in a first credit analysis result, determining acquisition points of related reference data according to the credit data, for example, determining that the credit data is on-time delivery rate, determining that the reference data is inventory image data, and determining that the acquisition points are warehouses) and a reference data acquisition model (for example, acquiring angles, quantity, image quality standards and the like of the image data) according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model; after the first credit data sniffer (namely the intelligent mobile sniffer) acquires the second credit data, the intelligent mobile sniffer is controlled to acquire first reference data at the reference data acquisition point according to the reference data acquisition model by combining the states (such as the object arrangement state, the object working state and the like) of the objects in the first physical space and the second physical space; and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

In some possible embodiments of the present invention, in the step of analyzing and determining whether the first enterprise has a credit abnormal risk according to the second credit data and the first reference data, the credit investigation server is configured to:

preprocessing the second credit data and the first reference data, and extracting first characteristic data according to a preset rule;

inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report;

and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

It can be understood that, in order to accurately perform credit assessment and risk prediction, in this embodiment, the second credit data and the first reference data are preprocessed, and first feature data are extracted according to a preset rule; inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report; and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

In some possible embodiments of the present invention, the intelligent mobile sniffer comprises a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a network connection component, a control processor, a data acquisition unit, a positioning unit, an optical communication component, a mechanical foot, a power component and a transmission component; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body;

the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer;

the control processor is deployed with a data sniffing model and a data acquisition model which are trained by utilizing a neural network;

the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit;

the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit;

the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer

A cavity is formed between the bottom end of the heat dissipation shell and the bottom end of the sniffer body, a dust removal component and a fan component are arranged in the cavity, the dust removal component and the fan component are respectively and electrically connected with the power component, the intelligent movable sniffer is favorable for improving the heat dissipation efficiency of the heat dissipation shell, timely eliminates dust in the heat dissipation shell, and prevents low equipment working efficiency caused by dust blockage when the intelligent movable sniffer works for a long time.

It will be appreciated that, in order to make the intelligent mobile sniffer more intelligent and flexible to perform data sniffing more efficiently, in this embodiment, the intelligent mobile sniffer (the structure of the intelligent mobile sniffer is not shown in the figure) includes a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a control processor, a network connection component electrically connected to the control processor, a data acquisition unit, a positioning unit, an optical communication component, a mechanical foot, a power component and a transmission component; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body; the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer; the control processor is deployed with a data sniffing model and a data acquisition model trained by using a neural network (the data sniffing/acquisition model comprises, but is not limited to, a data sniffing/acquisition object, a data sniffing/acquisition address, accessed network connection point information, a data sniffing/acquisition data type or type, a data quality standard, a data security standard, a data precision standard, a data sniffing/acquisition operation flow, a data sniffing/acquisition triggering condition and the like) so as to control the intelligent mobile sniffer to perform data sniffing and acquisition; the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit; the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit; the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer the bottom of heat dissipation shell with form a cavity between the bottom of sniffer body, be provided with dust removal subassembly and fan subassembly in the cavity, dust removal subassembly with the fan subassembly respectively with the power subassembly electricity is connected, is favorable to improving the radiating efficiency of heat dissipation shell to carry out timely clearance with the dust in the heat dissipation shell, prevent the long-time during operation of intelligent mobile sniffer leads to equipment work efficiency low because of the dust jam.

It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.

The foregoing has outlined rather broadly the more detailed description of embodiments of the application, wherein the principles and embodiments of the application are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Although the present application is disclosed above, the present application is not limited thereto. Variations and modifications, including combinations of the different functions and implementation steps, as well as embodiments of the software and hardware, may be readily apparent to those skilled in the art without departing from the spirit and scope of the application.

Claims (10)

1. An intelligent supervision method based on big data, which is used for carrying out credit supervision on enterprises in an intelligent park, and is characterized by comprising the following steps:

registering a first enterprise on a credit server when registering the first enterprise;

the credit investigation server configures a credit data sniffer for the first enterprise according to the submitted first enterprise information;

the credit investigation server generates first credit investigation index information of the first enterprise and sends the first credit investigation index information to a central cloud server;

the credit investigation server acquires first credit data of the first enterprise and analyzes the first credit data to obtain a first credit analysis result;

judging whether a preset first abnormal condition exists in the first credit analysis result;

if the first abnormal condition exists, sending a credit data sniffing instruction to a corresponding first credit data sniffer in the credit data sniffers;

connecting the first credit data sniffer to sniffer nodes in a corresponding network;

the first credit data sniffer acquires second credit data;

judging whether the first enterprise has credit abnormal risks according to the second credit data analysis;

And if the credit abnormal risk exists, sending out an early warning prompt and a rectifying instruction.

2. The big data based intelligent supervision method according to claim 1, wherein the step of the credit server configuring a credit data sniffer for the first enterprise according to the submitted first enterprise information comprises:

extracting first enterprise feature data of the first enterprise from the first enterprise information;

determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model;

an associated enterprise of the first enterprise is determined, a second sniffer of the associated enterprise is associated with a first sniffer of the first enterprise, and the first sniffer and the second sniffer are used as the credit data sniffer of the first enterprise.

3. The big data based intelligent supervision method according to claim 2, wherein the first credit data sniffer is an intelligent mobile sniffer; the method also comprises the steps of:

respectively establishing a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located;

Determining a reference data acquisition point and a reference data acquisition model of the first physical space and the second physical space according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model;

after the intelligent mobile sniffer acquires the second credit data, controlling the intelligent mobile sniffer to acquire first reference data at the reference data acquisition point according to the reference data acquisition model;

the step of the first credit data sniffer obtaining second credit data and judging whether the first enterprise has credit abnormal risk according to the analysis of the second credit data comprises the following steps:

and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

4. The big data based intelligent supervision method according to claim 3, wherein the step of analyzing and judging whether the first enterprise has credit abnormality risk according to the second credit data and the first reference data and performing credit prediction comprises:

preprocessing the second credit data and the first reference data, and extracting first characteristic data according to a preset rule;

Inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report;

and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

5. The intelligent supervision method based on big data according to claims 3-4, wherein the intelligent mobile sniffer comprises a sniffer body, a heat dissipation shell sleeved on the periphery of the sniffer body, a network connection assembly, a control processor, a data acquisition unit, a positioning unit, an optical communication assembly, a mechanical foot, a power assembly and a transmission assembly; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body;

the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer;

The control processor is deployed with a data sniffing model and a data acquisition model which are trained by utilizing a neural network;

the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit;

the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit;

the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer

A cavity is formed between the bottom end of the heat dissipation shell and the bottom end of the sniffer body, a dust removal component and a fan component are arranged in the cavity, and the dust removal component and the fan component are respectively and electrically connected with the power component.

6. An intelligent supervision system based on big data is used for carrying out credit supervision on enterprises in an intelligent park and is characterized by comprising a registration terminal, a credit data sniffer, a credit investigation server and a central cloud server, wherein,

the registration terminal is configured to: when first enterprise registration is processed, first enterprise information of the first enterprise is sent to the credit investigation server to register;

the credit server is configured to:

Configuring the credit data sniffer for the first enterprise according to the received first enterprise information;

generating first credit index information of the first enterprise, and sending the first credit index information to the central cloud server;

acquiring first credit data of the first enterprise, and analyzing the first credit data to obtain a first credit analysis result;

judging whether a preset first abnormal condition exists in the first credit analysis result;

if the first abnormal condition exists, sending a credit data sniffing instruction to a corresponding first credit data sniffer in the credit data sniffers;

connecting the first credit data sniffer to sniffer nodes in a corresponding network;

controlling the first credit data sniffer to acquire second credit data, and analyzing and judging whether the first enterprise has credit abnormal risks according to the second credit data;

and if the credit abnormal risk exists, sending out an early warning prompt and a rectifying instruction.

7. The big data based intelligent supervision system according to claim 6, wherein the step of the credit server configuring a credit data sniffer for the first enterprise according to the submitted first enterprise information is specifically configured to:

Extracting first enterprise feature data of the first enterprise from the first enterprise information;

determining a first sniffer corresponding to the first enterprise according to the first enterprise characteristic data and a preset sniffer determination model;

an associated enterprise of the first enterprise is determined, a second sniffer of the associated enterprise is associated with a first sniffer of the first enterprise, and the first sniffer and the second sniffer are used as the credit data sniffer of the first enterprise.

8. The big data based intelligent supervision system according to claim 7, wherein the first credit data sniffer is an intelligent mobile sniffer; the central cloud server is configured to:

respectively establishing a first three-dimensional model of a first physical space and a second three-dimensional model of a second physical space where the first enterprise and the associated enterprise are located;

determining a reference data acquisition point and a reference data acquisition model of the first physical space and the second physical space according to the first credit analysis result, the first three-dimensional model and the second three-dimensional model;

after controlling the first credit data sniffer to acquire the second credit data, the credit server is configured to: controlling the intelligent mobile sniffer to acquire first reference data at the reference data acquisition point according to the reference data acquisition model;

The step of controlling the first credit data sniffer to acquire second credit data and judging whether the first enterprise has credit abnormal risk according to the second credit data analysis, wherein the credit investigation server is configured to:

and analyzing and judging whether the first enterprise has credit abnormal risk according to the second credit data and the first reference data, and carrying out credit prediction.

9. The big data based intelligent supervisory system according to claim 8, wherein in the step of determining whether the first enterprise is at risk of credit anomaly based on the second credit data and the first reference data analysis and performing credit prediction, the credit server is configured to:

preprocessing the second credit data and the first reference data, and extracting first characteristic data according to a preset rule;

inputting the first characteristic data into a pre-trained credit risk assessment model to obtain a first credit risk assessment report;

and analyzing the first credit risk assessment report by combining the enterprise portrait data of the first enterprise to judge whether the first enterprise has credit abnormal risk or not, and predicting the credit risk of the first enterprise.

10. The big data based intelligent supervision system according to claims 8-9, wherein the intelligent mobile sniffer comprises a sniffer body, a heat dissipation shell sleeved on the sniffer body, a network connection component, a control processor, a data acquisition unit, a positioning unit, an optical communication component, a mechanical foot, a power component and a transmission component; the network connection assembly, the control processor, the data acquisition unit, the positioning unit, the power assembly and the transmission assembly are all arranged in the sniffer body;

the mechanical foot is arranged on the lower surface of the heat dissipation shell to support the intelligent mobile sniffer, and the mechanical foot is controlled to move through the power assembly and the transmission assembly so as to realize the movement of the intelligent mobile sniffer;

the control processor is deployed with a data sniffing model and a data acquisition model which are trained by utilizing a neural network;

the network connection component comprises a plurality of wireless connection units, a plurality of wired connection units and a connection switching unit;

the control processor is respectively and electrically connected with the wireless connection unit and the wired connection unit;

the connection switching unit is electrically connected with the control processor to control the network access state of the intelligent mobile sniffer

A cavity is formed between the bottom end of the heat dissipation shell and the bottom end of the sniffer body, a dust removal component and a fan component are arranged in the cavity, and the dust removal component and the fan component are respectively and electrically connected with the power component.