Classification and grading monitoring management system and method for chemical enterprises

Abstract

The embodiment of the invention discloses a chemical enterprise classification and grading monitoring management system and method, and relates to the technical field of enterprise monitoring management, and the system comprises a data monitoring module which is used for recognizing override access according to a data access log and generating first early warning information; the video monitoring module is used for identifying an illegal entering behavior according to the video monitoring data and generating second early warning information; and the system end is used for grading and classifying the monitoring area and the file storage position and generating an early warning analysis report according to the first early warning information or the second early warning information. In conclusion, the chemical enterprise classification and grading monitoring management system provided by the embodiment of the invention has the advantages of improving safety, improving efficiency, reducing human errors, improving response speed and the like through mutual cooperation of the data monitoring module, the video monitoring module and the system end.

Description

Technical Field

[0001] The present disclosure relates to the technical field of enterprise monitoring and management, and in particular to a classification and grading monitoring and management system and method for chemical enterprises. Background Art

[0002] Chemical companies are organizations engaged in chemical industry activities. Their core work is to process natural resources or raw materials into chemical products through chemical reactions or physical processes. These products usually include basic chemical raw materials (such as ethylene and propylene), intermediates (such as dyes, plastics, fertilizers), special chemicals (such as coatings, adhesives, pharmaceutical intermediates), etc. Chemical companies are usually characterized by large scale, complex equipment, continuous production and high energy consumption. Their production activities are often accompanied by the use and storage of a large number of hazardous chemicals, so they face huge safety and environmental challenges.

[0003] The chemical industry has various risk factors, including chemical reaction risks, physical process risks, and operational risks. The common point of these risks is that they may lead to serious safety and environmental incidents, such as leakage, fire, explosion, and major accidents to the surrounding environment. Therefore, a classification and grading method is needed to manage different risks in a targeted manner.

[0004] Chemical companies use a manually set method to carry out classification and grading management, which has a certain degree of flexibility and specificity based on experience and specific circumstances, but also has some obvious shortcomings, such as: strong subjectivity, low efficiency, and slow response speed. Summary of the invention

[0005] In view of this, the embodiments of the present disclosure provide a system and method for a classification and grading monitoring and management system for a chemical enterprise, which automates classification and grading management and improves management efficiency.

[0006] In the first aspect, the embodiment of the present disclosure provides a classification and grading monitoring and management system for a chemical enterprise, which adopts the following technical solutions:

[0007] A classification and grading monitoring and management system for chemical enterprises, comprising:

[0008] A data monitoring module, used to identify bypassing access based on data access logs and generate first warning information;

[0009] A video monitoring module, used for identifying illegal entry behavior based on video monitoring data and generating a second warning message;

[0010] The system side is used to classify the monitoring areas and file storage locations and generate an early warning analysis report based on the first early warning information or the second early warning information.

[0011] As an optional implementation, the system end includes:

[0012] A classification module is used to classify video surveillance areas and data storage locations, and divide video surveillance areas into to-be-classified supervision areas and general supervision areas, and divide data storage locations into to-be-classified supervision locations and general supervision locations;

[0013] A grading module, used for grading the supervision areas to be graded and the supervision locations to be graded, and obtaining graded supervision areas and graded supervision locations;

[0014] The early warning analysis report generating module is used to generate an early warning analysis report according to the first early warning information or the second early warning information.

[0015] As an optional implementation, the data monitoring module includes:

[0016] A log acquisition unit, used to acquire logs of hierarchical supervision locations;

[0017] The first warning unit uses a log warning model to analyze the acquired logs and generate first warning information.

[0018] As an optional implementation, the video monitoring module includes:

[0019] A video surveillance data acquisition unit, which acquires video surveillance data of a graded supervision area;

[0020] The second warning module uses a face recognition model to identify faces appearing in the video surveillance data. If there is any skipping of levels to enter the supervision area, a second warning message is generated.

[0021] As an optional implementation manner, the system end further includes:

[0022] The video supervision classification adjustment module uses the video supervision classification adjustment model to identify the video surveillance data of the general supervision area, and adjusts the area to a graded supervision area if the graded supervision conditions are met;

[0023] The video supervision hierarchical adjustment module uses the video supervision hierarchical adjustment model to identify the video surveillance data of the low-level area in the hierarchical supervision area, and adjusts the area to a high-level supervision area if the high-level supervision conditions are met.

[0024] As an optional implementation manner, the system end further includes:

[0025] The video supervision classification adjustment suggestion module uses the video supervision classification adjustment suggestion model to identify the video surveillance data of the graded supervision area. If the general supervision conditions are met, it generates a suggestion to adjust the area to a general supervision area for the management personnel to choose;

[0026] The video supervision classification adjustment suggestion module uses the video supervision classification adjustment suggestion model to identify the video surveillance data of the high-level area in the hierarchical supervision area. If the low-level supervision conditions are met, it generates a suggestion to adjust the area to a low-level supervision area for the management personnel to choose.

[0027] As an optional implementation, the adopting the log warning model to analyze the acquired log to generate the first warning information includes:

[0028] Input the acquired logs into the log warning model to obtain the IP address and user name of the bypassed access;

[0029] Determine the zone where the IP address is located based on the IP address and DHCP server records;

[0030] Generate the first warning information based on the access content, number of accesses, access duration, IP address, user name and region recorded in the log;

[0031] The face recognition model is used to recognize faces appearing in the video surveillance data. If there is a person entering the supervision area beyond the level, the second warning information is generated, including:

[0032] Use face recognition models to identify faces appearing in video surveillance data;

[0033] Determine one by one whether the recognized faces have the authority to access the area;

[0034] If not, then

[0035] Determine whether the face is an employee of the enterprise;

[0036] If the employee is an employee of the company, the second warning information will be generated with the employee's name, facial information, and the supervision area entered;

[0037] If the person is not an employee of the company, an advanced warning sign will be generated, and the advanced warning sign, facial information, and the entered supervision area will be used to generate a second warning message.

[0038] As an optional implementation manner, generating a warning analysis report according to the first warning information or the second warning information includes:

[0039] Obtain the original log corresponding to the first warning information;

[0040] Using the first intent analysis model to identify intent information based on the original log;

[0041] Generate a warning analysis report based on the first warning information and the intention information;

[0042] or,

[0043] Acquire original video surveillance data corresponding to the second warning information;

[0044] Using a second intent analysis model to identify intent information based on raw video surveillance data;

[0045] Generate a warning analysis report based on the second warning information and the intention information.

[0046] In a second aspect, the embodiment of the present disclosure further provides a chemical enterprise classification and grading monitoring and management method, based on the above-mentioned chemical enterprise classification and grading monitoring and management system, comprising:

[0047] Using the data monitoring module to identify the bypass access according to the data access log and generate the first warning information;

[0048] Using the video surveillance module to identify illegal entry behavior based on video surveillance data and generate a second warning information;

[0049] The system end is used to classify the monitoring areas and file storage locations and generate an early warning analysis report based on the first early warning information or the second early warning information.

[0050] As an optional implementation, using the system end to classify the monitoring area and the file storage location and generate a warning analysis report according to the first warning information or the second warning information includes:

[0051] Using the classification module to classify the video surveillance area and the data storage location, the video surveillance area is divided into the to-be-classified supervision area and the general supervision area, and the data storage location is divided into the to-be-classified supervision location and the general supervision location;

[0052] Using a grading module to grade the supervision areas to be graded and the supervision locations to be graded, to obtain graded supervision areas and graded supervision locations;

[0053] The early warning analysis report generation module is used to generate an early warning analysis report according to the first early warning information or the second early warning information.

[0054] In summary, the classified and graded monitoring and management system for chemical enterprises provided in this embodiment arranges the data monitoring module, the video monitoring module and the system end separately, reducing the computing power requirements for arranging them together. The data monitoring module and the video monitoring module can be arranged in different plant areas to realize edge computing, with fast computing speed. In addition, it also has the following effects:

[0055] The system can monitor and identify over-level access and illegal entry in real time. By generating the first and second warning information, it can quickly respond to potential security threats and reduce the risk of accidents. This multi-level monitoring method ensures the dual protection of chemical companies at the information security and physical security levels.

[0056] The system can automatically perform classification and grading settings, greatly reducing the time for manual settings and adjustments, and improving the efficiency of management work. The automatically generated early warning analysis report can help managers quickly understand the security status and formulate corresponding measures.

[0057] Through automated processing, the possibility of human judgment bias and missed risks is reduced. This reduces the impact of subjective factors on risk management and makes risk identification and response more standardized and consistent.

[0058] The automated monitoring system is able to respond to abnormal situations in real time and dynamically adjust the monitoring level and strategy according to the actual situation. This flexibility allows the system to automatically adapt according to changes in the actual on-site environment, effectively improving emergency response capabilities.

[0059] The system can integrate diverse information sources from data monitoring and video surveillance, and automatically generate early warning analysis reports through background data analysis to provide managers with a global perspective, thereby conducting more comprehensive risk assessments and decision-making support.

[0060] In general, the system provided by the present disclosure makes the safety management of chemical enterprises more intelligent, efficient and reliable through highly integrated technical means, can significantly reduce the risk of accidents, and improve the level of enterprise safety operations through early warning and analysis reports.

[0061] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following preferred embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 A schematic diagram of the structure of a chemical enterprise classification and grading monitoring and management system provided by an embodiment of the present disclosure;

[0064] Figure 2 A schematic flow chart of a chemical enterprise classification and grading monitoring and management method provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0065] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0066] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0067] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0068] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0069] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0070] In order to solve some of the problems pointed out in the background technology, refer to Figure 1 The first aspect of the present invention provides a classification and grading monitoring and management system for chemical enterprises, comprising:

[0071] A data monitoring module, used to identify bypassing access based on data access logs and generate first warning information;

[0072] A video monitoring module, used for identifying illegal entry behavior based on video monitoring data and generating a second warning message;

[0073] The system side is used to classify the monitoring areas and file storage locations and generate an early warning analysis report based on the first early warning information or the second early warning information.

[0074] The chemical enterprise classification and grading monitoring and management system provided by the present disclosure can achieve the following benefits through the cooperation of the data monitoring module, the video monitoring module and the system end:

[0075] The system can monitor and identify over-level access and illegal entry in real time. By generating the first and second warning information, it can quickly respond to potential security threats and reduce the risk of accidents. This multi-level monitoring method ensures the dual protection of chemical companies at the information security and physical security levels.

[0076] The system can automatically perform classification and grading settings, greatly reducing the time for manual settings and adjustments, and improving the efficiency of management work. The automatically generated early warning analysis report can help managers quickly understand the security status and formulate corresponding measures.

[0077] Through automated processing, the possibility of human judgment bias and missed risks is reduced. This reduces the impact of subjective factors on risk management and makes risk identification and response more standardized and consistent.

[0078] The automated monitoring system is able to respond to abnormal situations in real time and dynamically adjust the monitoring level and strategy according to the actual situation. This flexibility allows the system to automatically adapt according to changes in the actual on-site environment, effectively improving emergency response capabilities.

[0079] The system can integrate diverse information sources from data monitoring and video surveillance, and automatically generate early warning analysis reports through background data analysis to provide managers with a global perspective, thereby conducting more comprehensive risk assessments and decision-making support.

[0080] In general, the system provided by the present disclosure makes the safety management of chemical enterprises more intelligent, efficient and reliable through highly integrated technical means, can significantly reduce the risk of accidents, and improve the level of enterprise safety operations through early warning and analysis reports.

[0081] As an optional implementation, the system end includes:

[0082] The classification module is used to classify video surveillance areas and data storage locations, dividing video surveillance areas into areas for supervision to be classified and general supervision areas, and dividing data storage locations into locations for supervision to be classified and general supervision locations; general supervision areas generally refer to public areas that are freely accessible to everyone, while areas for supervision to be classified are accessible only to designated persons, such as dangerous goods storage locations, areas near dangerous equipment, etc.; general supervision locations are storage locations for general data, such as company promotional materials, public address books, etc., while locations for supervision to be classified store important information, such as confidential formulas, customer contact information, contracts, etc.

[0083] A grading module is used to grade the regulatory areas to be graded and the regulatory positions to be graded to obtain graded regulatory areas and graded regulatory positions; graded regulatory areas refer to multiple levels of regulatory areas, for example, from low to high, including first-level regulatory areas, second-level regulatory areas, and third-level regulatory areas; graded regulatory positions refer to multiple levels of regulatory positions, for example, from low to high, including first-level regulatory positions, second-level regulatory positions, and third-level regulatory positions.

[0084] The early warning analysis report generating module is used to generate an early warning analysis report according to the first early warning information or the second early warning information.

[0085] Specifically, generating a warning analysis report according to the first warning information or the second warning information includes:

[0086] Obtain the original log corresponding to the first warning information;

[0087] Use the first intent analysis model to identify intent information based on the original log; the first intent analysis model is obtained through neural network training, and identifies intent information based on the data recorded in the log, whether it is repeated access or accidental access, the amount of data accessed, and whether there is copying, modification, etc.

[0088] Generate a warning analysis report based on the first warning information and the intention information;

[0089] or,

[0090] Acquire original video surveillance data corresponding to the second warning information;

[0091] Use a second intention analysis model to identify intention information based on the original video surveillance data; the second intention analysis model is obtained through neural network training, and generates intention information based on the behavior of people in the original video surveillance data, such as the length of stay, whether information is collected specifically, whether there is dangerous behavior, etc.;

[0092] Generate a warning analysis report based on the second warning information and the intention information.

[0093] As an optional implementation, the data monitoring module includes:

[0094] A log acquisition unit, used to acquire logs of hierarchical supervision locations;

[0095] The first warning unit uses a log warning model to analyze the acquired logs and generate first warning information.

[0096] Specifically, the adopting the log warning model to analyze the acquired log to generate the first warning information includes:

[0097] Input the obtained logs into the log warning model to obtain the IP address and user name of the bypassed access. Generally speaking, internal enterprise data is usually stored in the intranet and isolated from the external network. Therefore, the IP address is internal to the enterprise, and a lot of information can be determined through the IP address.

[0098] Determine the region where the IP address is located based on the IP address and DHCP server records; the DHCP server records can record the IP address band of each region, so the region where the IP address is located is confirmed based on the band where the IP address is located.

[0099] Generate the first warning information based on the access content, number of accesses, access duration, IP address, user name and region recorded in the log;

[0100] As an optional implementation, the video monitoring module includes:

[0101] A video surveillance data acquisition unit, which acquires video surveillance data of a graded supervision area;

[0102] The second warning module uses a face recognition model to identify faces appearing in the video surveillance data. If there is any skipping of levels to enter the supervision area, a second warning message is generated.

[0103] Specifically, the face recognition model is used to recognize faces appearing in the video surveillance data, and if there is a person entering the supervision area beyond the level, the second warning information is generated, including:

[0104] Use face recognition models to identify faces appearing in video surveillance data;

[0105] Determine one by one whether the recognized faces have the authority to access the area;

[0106] If not, then

[0107] Determine whether the face is an employee of the enterprise;

[0108] If the employee is an employee of the company, the second warning information will be generated with the employee's name, facial information, and the supervision area entered;

[0109] If the person is not an employee of the company, an advanced warning sign will be generated, and the advanced warning sign, facial information, and the entered supervision area will be used to generate a second warning message.

[0110] As an optional implementation manner, the system end further includes:

[0111] The video supervision classification adjustment module uses the video supervision classification adjustment model to identify the video surveillance data of the general supervision area, and adjusts the area to a graded supervision area if the graded supervision conditions are met;

[0112] The video supervision hierarchical adjustment module uses the video supervision hierarchical adjustment model to identify the video surveillance data of the low-level area in the hierarchical supervision area, and adjusts the area to a high-level supervision area if the high-level supervision conditions are met.

[0113] Based on the cooperation of the above-mentioned video supervision classification adjustment module and the video supervision grading adjustment module, the video supervision level can be automatically increased. For example, if a location is originally a public area and later it is detected in the video that dangerous chemicals are stored in this location, the video supervision level will be automatically increased to further ensure safety. Detecting whether dangerous chemicals are stored in the video can be achieved by model recognition of whether there are warning lines or fixed packaging of dangerous chemicals; specifically, the technical effects of this embodiment are:

[0114] The video supervision classification adjustment module and the video supervision grade adjustment module have the following advantages:

[0115] By automatically identifying and adjusting the level of regulatory areas, the workload of manual monitoring and management is reduced and management efficiency is improved.

[0116] The system can flexibly respond to changing environments and risks. For example, when the risk factors in a certain area increase (such as the storage of hazardous chemicals), the supervision level of the area can be adjusted in a timely manner, thereby improving the timeliness and accuracy of safety monitoring.

[0117] Through intelligent analysis of video surveillance data, we can ensure that high-risk areas are subject to stricter supervision, while low-risk areas remain under more relaxed management, so that resources can be allocated rationally.

[0118] Improving risk perception capabilities can prevent potential safety accidents more promptly and protect corporate assets and employee safety.

[0119] Ensure that the company's monitoring and management system complies with industry standards and regulatory requirements, and facilitate subsequent audits and inspections.

[0120] As an optional implementation manner, the system end further includes:

[0121] The video supervision classification adjustment suggestion module uses the video supervision classification adjustment suggestion model to identify the video surveillance data of the graded supervision area. If the general supervision conditions are met, it generates a suggestion to adjust the area to a general supervision area for the management personnel to choose;

[0122] The video supervision classification adjustment suggestion module uses the video supervision classification adjustment suggestion model to identify the video surveillance data of the high-level area in the hierarchical supervision area. If the low-level supervision conditions are met, it generates a suggestion to adjust the area to a low-level supervision area for the management personnel to choose.

[0123] In this embodiment, the video supervision classification adjustment suggestion module and the video supervision grade adjustment suggestion module can provide suggestions to managers in the system instead of directly making automatic adjustments, which brings the following technical effects:

[0124] By generating recommendations rather than direct actions, these modules facilitate human-machine collaboration, providing automated analysis while retaining decision-making power and ultimate control for managers.

[0125] Automated systems may occasionally make identification errors or misjudgments. By generating recommendations for review by managers, unnecessary risks caused by system misjudgments can be effectively avoided.

[0126] Combining the data analysis capabilities provided by the automated system with the experience and judgment of managers can improve the accuracy and reliability of the final decision.

[0127] Through the suggestion mechanism, managers can make flexible decisions based on actual conditions and experience, adjust or reject system suggestions, and maintain overall control over regulatory adjustments.

[0128] Providing preliminary analysis and decision recommendations can significantly reduce managers' judgment burden, improve work efficiency, and enable them to focus on more complex or strategic tasks.

[0129] In areas involving safety and regulatory compliance, retaining management review processes can effectively reduce potential compliance issues and risks caused by automated adjustment errors.

[0130] In summary, these proposed modules, while maintaining the intelligence of the system, also take into account the key role of humans in the decision-making process, which helps to achieve more efficient and safer regulatory management.

[0131] On the other hand, referring to Figure 2 The present invention provides a classification and grading monitoring and management method for a chemical enterprise, based on the above-mentioned classification and grading monitoring and management system for a chemical enterprise, comprising:

[0132] Step S1, using a data monitoring module to identify bypassing access based on data access logs and generate first warning information;

[0133] Step S2, using the video monitoring module to identify illegal entry behavior based on the video monitoring data and generate a second warning information;

[0134] Step S3, using the system to classify the monitoring areas and file storage locations and generate an early warning analysis report based on the first early warning information or the second early warning information.

[0135] As an optional implementation, step S3, using the system end to classify the monitoring area and the file storage location and generate a warning analysis report according to the first warning information or the second warning information includes:

[0136] Step S31, using a classification module to classify the video surveillance area and the data storage location, the video surveillance area is divided into a to-be-classified supervision area and a general supervision area, and the data storage location is divided into a to-be-classified supervision location and a general supervision location;

[0137] Step S32, using a grading module to grade the supervision areas to be graded and the supervision locations to be graded, to obtain graded supervision areas and graded supervision locations;

[0138] Step S33: using the early warning analysis report generation module to generate an early warning analysis report according to the first early warning information or the second early warning information.

[0139] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0140] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0141] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0142] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0143] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0144] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0145] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0146] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A classification and grading monitoring and management system for chemical enterprises, characterized in that: include: A data monitoring module, used to identify bypassing access based on data access logs and generate first warning information; A video monitoring module, used for identifying illegal entry behavior based on video monitoring data and generating a second warning message; The system side is used to classify the monitoring areas and file storage locations and generate an early warning analysis report based on the first early warning information or the second early warning information.

2. The chemical enterprise classification and grading monitoring and management system according to claim 1 is characterized in that: The system end includes: A classification module is used to classify video surveillance areas and data storage locations, and divide video surveillance areas into to-be-classified supervision areas and general supervision areas, and divide data storage locations into to-be-classified supervision locations and general supervision locations; A grading module, used for grading the supervision areas to be graded and the supervision locations to be graded, and obtaining graded supervision areas and graded supervision locations; The early warning analysis report generating module is used to generate an early warning analysis report according to the first early warning information or the second early warning information.

3. The chemical enterprise classification and grading monitoring and management system according to claim 2 is characterized in that: The data monitoring module comprises: A log acquisition unit, used to acquire logs of hierarchical supervision locations; The first warning unit uses a log warning model to analyze the acquired logs and generate first warning information.

4. The chemical enterprise classification and grading monitoring and management system according to claim 3 is characterized in that: The video monitoring module comprises: A video surveillance data acquisition unit, which acquires video surveillance data of a graded supervision area; The second warning module uses a face recognition model to identify faces appearing in the video surveillance data. If there is any skipping of levels to enter the supervision area, a second warning message is generated.

5. The classification and grading monitoring and management system for chemical enterprises according to claim 4 is characterized in that: The system end also includes: The video supervision classification adjustment module uses the video supervision classification adjustment model to identify the video surveillance data of the general supervision area, and adjusts the area to a graded supervision area if the graded supervision conditions are met; The video supervision hierarchical adjustment module uses the video supervision hierarchical adjustment model to identify the video surveillance data of the low-level area in the hierarchical supervision area, and adjusts the area to a high-level supervision area if the high-level supervision conditions are met.

6. The classification and grading monitoring and management system for chemical enterprises according to claim 5 is characterized in that: The system end also includes: The video supervision classification adjustment suggestion module uses the video supervision classification adjustment suggestion model to identify the video surveillance data of the graded supervision area. If the general supervision conditions are met, it generates a suggestion to adjust the area to a general supervision area for the management personnel to choose; The video supervision classification adjustment suggestion module uses the video supervision classification adjustment suggestion model to identify the video surveillance data of the high-level area in the hierarchical supervision area. If the low-level supervision conditions are met, it generates a suggestion to adjust the area to a low-level supervision area for the management personnel to choose.

7. The chemical enterprise classification and grading monitoring and management system according to claim 6 is characterized in that: The adopting the log warning model to analyze the acquired log to generate the first warning information includes: Input the acquired logs into the log warning model to obtain the IP address and user name of the bypassed access; Determine the zone where the IP address is located based on the IP address and DHCP server records; Generate the first warning information based on the access content, number of accesses, access duration, IP address, user name and region recorded in the log; The face recognition model is used to recognize faces appearing in the video surveillance data. If there is a person entering the supervision area beyond the level, the second warning information is generated, including: Use face recognition models to identify faces appearing in video surveillance data; Determine one by one whether the recognized faces have the authority to access the area; If not, then Determine whether the face is an employee of the enterprise; If the employee is an employee of the company, the second warning information will be generated with the employee's name, facial information, and the supervision area entered; If the person is not an employee of the company, an advanced warning sign will be generated, and the advanced warning sign, facial information, and the entered supervision area will be used to generate a second warning message.

8. The chemical enterprise classification and grading monitoring and management system according to claim 7 is characterized in that: Generating a warning analysis report according to the first warning information or the second warning information includes: Obtain the original log corresponding to the first warning information; Using the first intent analysis model to identify intent information based on the original log; Generate a warning analysis report based on the first warning information and the intention information; or, Acquire original video surveillance data corresponding to the second warning information; Using a second intent analysis model to identify intent information based on raw video surveillance data; Generate a warning analysis report based on the second warning information and the intention information.

9. A classification and grading monitoring and management method for chemical enterprises, based on the classification and grading monitoring and management system for chemical enterprises according to any one of claims 1 to 8, characterized in that: include: Using the data monitoring module to identify the bypass access according to the data access log and generate the first warning information; Using the video surveillance module to identify illegal entry behavior based on video surveillance data and generate a second warning information; The system end is used to classify the monitoring areas and file storage locations and generate an early warning analysis report based on the first early warning information or the second early warning information.

10. The chemical enterprise classification and grading monitoring and management method according to claim 9 is characterized in that: Using the system end to classify the monitoring area and the file storage location and generate an early warning analysis report according to the first early warning information or the second early warning information includes: Using the classification module to classify the video surveillance area and the data storage location, the video surveillance area is divided into the to-be-classified supervision area and the general supervision area, and the data storage location is divided into the to-be-classified supervision location and the general supervision location; Using a grading module to grade the supervision areas to be graded and the supervision locations to be graded, to obtain graded supervision areas and graded supervision locations; The early warning analysis report generation module is used to generate an early warning analysis report according to the first early warning information or the second early warning information.

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