Configurable supervision method and device based on risk grading and job type matching

Abstract

The invention relates to a configurable supervision method and device based on risk grading and operation type matching, and belongs to the technical field of supervision, and the method comprises the steps: obtaining an electronic operation ticket which comprises metadata, an operation type and an operation risk grade; generating an initial safety measure list by combining the operation type and the operation risk level with a safety production knowledge base and historical operation cases, and generating an initial AI algorithm list by combining with an AI algorithm mapping table; performing data maintenance on the initial security measure list and the initial AI algorithm list based on states of different dimensions to form an operation supervision process; and submitting the electronic work ticket and the work supervision process to an approver for approval, allowing work to be started and starting work safety control under the condition that the approval is passed, and modifying the electronic work ticket and the work supervision process under the condition that the approval is not passed until the approval is passed. The supervision intensity of high-risk operation is ensured, and alarm overload of a low-risk scene is avoided.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of supervision, and particularly relates to a configurable supervision method and device based on risk grading and operation type matching. BACKGROUND

[0002] In the related art, the existing system generally adopts a static and unified supervision strategy, and fails to realize dynamic adaptation of safety measures and monitoring algorithms according to operation types and risk levels, resulting in insufficient supervision strength for high-risk operations and potential safety hazards, and a large amount of invalid information due to alarm overload for low-risk operations, increasing the management burden of on-site personnel and false alarm response fatigue. SUMMARY

[0003] In view of the deficiencies of the prior art, the purpose of the application is to provide a configurable supervision method and device based on risk grading and operation type matching.

[0004] In a first aspect of the application, a configurable supervision method based on risk grading and operation type matching is provided, comprising: S1, obtaining an electronic work ticket, the electronic work ticket comprising metadata, an operation type and an operation risk level; S2, generating an initial safety measure list by combining the operation type and the operation risk level with a safety production knowledge base and historical operation cases, and generating an initial AI algorithm list by combining an AI algorithm mapping table; S3, performing data maintenance on the initial safety measure list and the initial AI algorithm list based on different dimensions of states to form an operation supervision process; and S4, submitting the electronic work ticket and the operation supervision process to an approval personnel for approval, allowing the operation to start and starting operation safety control if the approval is passed, and modifying the electronic work ticket and the operation supervision process until the approval is passed if the approval is not passed.

[0005] Further, generating an initial safety measure list by combining the operation type and the operation risk level with a safety production knowledge base and historical operation cases comprises: searching for all rules applicable to the operation type and the operation risk level from the safety production database, and extracting safety measures corresponding to the rules to form a first safety measure set; searching for all cases applicable to the operation type and the operation risk level from the historical operation cases, and determining safety measures with a frequency of occurrence greater than a preset threshold in the cases to form a second safety measure set; merging the first safety measure set and the second safety measure set, and removing duplicates of the merged safety measures to obtain safety measures after deduplication; and sorting the safety measures after deduplication according to the importance level of the measures and the source weight of the measures to obtain the initial safety measure list.

[0006] Furthermore, generating an initial AI algorithm list by combining the AI ​​algorithm mapping table includes: obtaining the AI ​​algorithm mapping table, which defines the AI ​​algorithms required for different operational risks and scenarios; searching for the AI ​​algorithms corresponding to the operational type and the operational risk level from the AI ​​algorithm mapping table to form the initial AI algorithm list.

[0007] Furthermore, the different dimensions include physical environment dimension, equipment and material dimension, personnel and organization dimension, time and dynamic dimension, and system and resource availability dimension.

[0008] Furthermore, based on different dimensions of status, the initial safety measures list and the initial AI algorithm list are maintained to form an operation supervision process, including: adding, deleting, and / or modifying the initial safety measures list through the operation controls of the safety measures list based on the different dimensions of status; and adding, deleting, and / or modifying the initial AI algorithm list through the operation controls of the AI ​​algorithm list based on the different dimensions of status, thus forming the operation supervision process.

[0009] Furthermore, submitting the electronic work order and the work supervision process to the approver for approval includes: determining the approver's permissions, wherein the approver is subject to hierarchical control based on the RBAC model, and the approver's permissions include functional permissions and data permissions; and submitting the electronic work order and the work supervision process to the approver for approval based on the approver's permissions.

[0010] Furthermore, it also includes: triggering alarm processing when collecting work environment parameters and abnormal data exists; and archiving and saving the electronic work ticket, supervision record, monitoring video, and alarm information after the work is completed.

[0011] A second aspect of the present invention proposes a configurable monitoring device based on risk classification and job type matching, comprising: an acquisition module for acquiring an electronic work ticket, the electronic work ticket including metadata, job type, and job risk level; a first generation module for generating an initial safety measures list by combining the job type and job risk level with a safety production knowledge base and historical job cases, and generating an initial AI algorithm list by combining an AI algorithm mapping table; a second generation module for maintaining the initial safety measures list and the initial AI algorithm list based on different dimensions of status, forming a job monitoring process; and an approval module for submitting the electronic work ticket and the job monitoring process to an approver for approval. If the approval is approved, the work is allowed to begin and job safety control is initiated. If the approval is not approved, the electronic work ticket and the job monitoring process are modified until the approval is granted.

[0012] A third aspect of the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to said at least one processor; wherein the memory stores instructions executable by said at least one processor, said instructions being executed by said at least one processor to enable said at least one processor to perform the method described in any one of the first aspects of the present invention.

[0013] A fourth aspect of the present invention provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method described in any one of the first aspects of the present invention.

[0014] The beneficial effects of this invention are as follows:

[0015] The configurable monitoring method and device based on risk classification and job type matching described in this invention obtains electronic work tickets, which include metadata, job type, and job risk level. The method then generates an initial safety measure list and an initial AI algorithm list by combining the job type and risk level with a safety production knowledge base and historical job cases, and a data maintenance process based on an AI algorithm mapping table. The electronic work ticket and the monitoring process are submitted to approvers for review. If approved, work can begin and job safety control is initiated. If not approved, the electronic work ticket and monitoring process are modified until approval is granted. This method dynamically binds safety measures, AI algorithms, job risk level, and job type, ensuring strong monitoring of high-risk jobs while avoiding alarm overload in low-risk scenarios, thus resolving the conflict between resource allocation and safety requirements. Attached Figure Description

[0016] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. It is obvious that the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings.

[0017] Figure 1 This is a flowchart of a configurable monitoring method based on risk classification and job type matching according to an embodiment of the present invention;

[0018] Figure 2 This is a flowchart of a configurable monitoring method based on risk classification and job type matching according to a specific embodiment of the present invention;

[0019] Figure 3This is a schematic diagram of a monitoring system according to an embodiment of the present invention;

[0020] Figure 4 This is a structural block diagram of a configurable monitoring device based on risk classification and operation type matching according to an embodiment of the present invention;

[0021] Figure 5 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts disclosed in this invention.

[0024] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. The terms "installed," "connected," and "linked" should be interpreted broadly; for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of methods and systems consistent with some aspects of the invention as detailed in the appended claims.

[0026] Existing systems generally employ a static and uniform monitoring strategy, failing to dynamically adapt safety measures and monitoring algorithms based on job type and risk level. This results in insufficient monitoring of high-risk jobs, posing safety hazards, while low-risk jobs generate a large amount of invalid information due to alarm overload, increasing the management burden on on-site personnel and causing fatigue in responding to false alarms. Furthermore, the existing systems have rigid safety measure management processes, typically using a fixed checklist model. This fails to intelligently link enterprise safety regulations with specific job types and risk levels, potentially leading to the omission of key mandatory measures. Approving personnel lack scientific basis to judge the rationality of safety measure configurations, relying solely on subjective experience for review, thus reducing the standardization and reliability of the process.

[0027] Currently, some advanced systems attempt to introduce artificial intelligence algorithms for job monitoring, but there are significant limitations in algorithm deployment and application: if all AI algorithms are enabled globally for all job scenarios, computing resources will be heavily occupied by low-priority tasks, resulting in wasted server resources and response delays; if administrators manually configure algorithms for each job ticket, it heavily relies on personnel experience and is cumbersome to operate, making it difficult to apply on a large scale in actual production. This contradiction between resource allocation and control accuracy cannot be effectively resolved under traditional architectures.

[0028] The current system has shortcomings in post-event traceability: data such as videos, alarms, and safety measure execution records generated during the operation are often stored in isolation and are not structured and linked with the work ticket. This makes it difficult to quickly reconstruct the full picture of the operation during accident investigation and compliance audit, resulting in low data retrieval efficiency and a lack of a complete chain of evidence to support event retrospection and liability determination.

[0029] To this end, the present invention proposes a configurable monitoring method, apparatus and related equipment based on risk classification and operation type matching. Specifically, the configurable monitoring method, apparatus and related equipment based on risk classification and operation type matching of the present invention are described below with reference to the accompanying drawings.

[0030] Figure 1 This is a flowchart of a configurable monitoring method based on risk classification and job type matching according to an embodiment of the present invention. It should be noted that the configurable monitoring method based on risk classification and job type matching of the present invention can be applied to the configurable monitoring device based on risk classification and job type matching of the present invention. This configurable monitoring device based on risk classification and job type matching can be configured on an electronic device or in a server. This application does not limit the scope of the application.

[0031] like Figure 1 As shown, the configurable regulatory approach based on risk classification and job type matching includes:

[0032] S110, Obtain electronic work ticket. Electronic work ticket includes metadata, work type and work risk level.

[0033] In an embodiment of the present invention, a user can create and fill out an electronic work ticket through a system interface. The electronic work ticket includes metadata, work type, and work risk level.

[0034] The metadata includes basic information such as the person doing the assignment, the supervisor, and the person in charge.

[0035] S120 combines the job type and job risk level with the safety production knowledge base and historical job cases to generate an initial list of safety measures, and combines the AI ​​algorithm mapping table to generate an initial list of AI algorithms.

[0036] In an embodiment of the present invention, upon obtaining an electronic work ticket, the work type and risk level in the electronic work ticket can be combined with a safety production knowledge base and historical work cases to generate an initial list of safety measures, and an initial list of AI algorithms can be generated by combining the information with an AI algorithm mapping table. Specific implementation details can be found in subsequent embodiments.

[0037] The safety production knowledge base includes breaking down national regulations, industry standards, and enterprise safety procedures into specific, enforceable rules and corresponding safety measures.

[0038] The AI ​​algorithm mapping table defines the optimal AI algorithm required for different operational risks and risk levels.

[0039] S130 maintains the data of the initial safety measures list and the initial AI algorithm list based on different dimensions of status, forming an operation supervision process.

[0040] In embodiments of the present invention, the different dimensions include physical environment, equipment and materials, personnel and organization, time and dynamics, and system and resource availability. That is, based on the states of these different dimensions, the initial list of safety measures and the initial list of AI algorithms are added to, deleted from, and / or modified to form an operational supervision process. Specific implementation methods can be found in subsequent embodiments.

[0041] S140: Submit the electronic work order and work supervision process to the approver for approval. If the approval is approved, the work can begin and work safety control is initiated. If the approval is not approved, modify the electronic work order and work supervision process until the approval is granted.

[0042] In an embodiment of the present invention, upon obtaining an electronic work order and a work supervision process, the electronic work order and the work supervision process can be submitted to an approver for approval. If the approval is approved, work can commence and work safety control can be initiated. If the approval is not approved, the electronic work order and the work supervision process can be modified until approval is granted. Specific implementation details can be found in subsequent embodiments.

[0043] According to an embodiment of the present invention, a configurable supervision method based on risk classification and job type matching obtains an electronic work order, which includes metadata, job type, and job risk level. The method then generates an initial safety measure list and an initial AI algorithm list by combining the job type and risk level with a safety production knowledge base and historical job cases, and by combining these with an AI algorithm mapping table. Based on different dimensions of status, the initial safety measure list and the initial AI algorithm list are maintained to form a job supervision process. The electronic work order and the job supervision process are submitted to approvers for review. If approved, the work is allowed to begin and job safety control is initiated. If not approved, the electronic work order and the job supervision process are modified until approval is granted. This method, by dynamically binding safety measures, AI algorithms, job risk level, and job type, ensures the intensity of supervision for high-risk jobs while avoiding alarm overload in low-risk scenarios, thus resolving the contradiction between resource allocation and safety requirements.

[0044] To enable those skilled in the art to more readily understand the present invention, Figure 2 This is a configurable monitoring method based on risk classification and operation type matching according to a specific embodiment of the present invention, such as... Figure 2 As shown, this configurable regulatory approach based on risk classification and job type matching includes:

[0045] S210, Obtain electronic work ticket, which includes metadata, work type, and work risk level.

[0046] S220 combines the job type and job risk level with the safety production knowledge base and historical job cases to generate an initial safety measures list.

[0047] In an embodiment of the present invention, all rules applicable to the job type and job risk level are searched from the safety production database, and the corresponding safety measures are extracted to form a first set of safety measures; all cases applicable to the job type and job risk level are searched from historical job cases, and safety measures appearing more frequently than a preset threshold in the cases are identified to form a second set of safety measures; the first set of safety measures and the second set of safety measures are merged, and the merged safety measures are deduplicated to obtain deduplicated safety measures; the deduplicated safety measures are sorted according to the importance level and source weight of the measures to obtain an initial list of safety measures.

[0048] The safety production knowledge base includes breaking down national regulations, industry standards, and enterprise safety procedures into specific, enforceable rules and corresponding safety measures.

[0049] For example, the safety production database is scanned to find all rules applicable to job types and job risk levels. The corresponding safety measures for these rules are extracted to form a first set of safety measures. Then, all job types and job risk levels are retrieved from historical job cases, and the safety measures that appear most frequently in these cases are counted to obtain a second set of safety measures. The first and second sets of safety measures are then merged and deduplicated. Finally, the deduplicated safety measures are sorted according to their importance (whether they are mandatory) and source weight (regulatory requirements have a higher weight than general recommendations) to generate an initial list of safety measures.

[0050] S230, Combine the AI ​​algorithm mapping table to generate an initial AI algorithm list.

[0051] In an embodiment of the present invention, an AI algorithm mapping table is obtained, which defines the AI ​​algorithms required for different operational risks and scenarios; the AI ​​algorithms corresponding to the operational type and operational risk level are found from the AI ​​algorithm mapping table to form an initial AI algorithm list.

[0052] The AI ​​algorithm mapping table defines the optimal AI algorithms required for different operational risks and risk levels. For example, if the operation type is high-altitude work with a risk level of 2, the corresponding recommended algorithms are the helmet / safety belt detection algorithm and the perimeter intrusion detection algorithm; as another example, if the operation type is hot work with a risk level of 1, the corresponding recommended algorithms are the smoke and fire detection algorithm and the personnel intrusion detection algorithm.

[0053] S240 maintains the data of the initial safety measures list and the initial AI algorithm list based on different dimensions of status, forming an operation supervision process.

[0054] The different dimensions include physical environment, equipment and materials, personnel and organization, time and dynamics, and system and resource availability. The physical environment dimension refers to the specific physical conditions of the work site; the equipment and materials dimension refers to the specific requirements of the tools, equipment, and materials involved in the work; the personnel and organization dimension refers to the capabilities of the personnel involved in the work and the on-site organization; the time and dynamics dimension refers to the dynamic changes that the work may encounter over time; and the system and resource availability dimension refers to the availability of the technologies and physical resources on which the solution depends on the site.

[0055] In embodiments of the present invention, based on different dimensional states, the initial safety measures list is added, deleted, and / or modified through the operation controls of the safety measures list; based on different dimensional states, the initial AI algorithm list is added, deleted, and / or modified through the operation controls of the AI ​​algorithm list, thus forming an operation supervision process.

[0056] The operation controls for the safety measures list include check / uncheck, edit button, delete button, add button, sorting function, and mandatory measure identification checkbox.

[0057] The AI ​​algorithm list includes control options such as enable / disable switch, parameter configuration, and camera selection.

[0058] For any addition, deletion, and / or modification, the system will display a brief input box, requiring the applicant to fill in the reason for the adjustment.

[0059] S250 submits the electronic work order and work supervision process to the approver for approval. If the approval is approved, the work can begin and work safety control is initiated. If the approval is not approved, the electronic work order and work supervision process are modified until the approval is obtained.

[0060] In an embodiment of the present invention, upon obtaining an electronic work order and a work supervision process, the authority of the approver is determined. The approver is subject to hierarchical control based on the RBAC model, and the approver's authority includes functional authority and data authority. Based on the approver's authority, the electronic work order and the work supervision process are submitted to the approver for approval.

[0061] In embodiments of the present invention, when collecting operational environment parameters and abnormal data are present, an alarm is triggered; and after the operation is completed, the electronic work ticket, supervision record, monitoring video, and alarm information are archived and saved.

[0062] In one embodiment of the present invention, such as Figure 3As shown, the monitoring system comprises a presentation layer, a functional application layer, and a data acquisition layer. The presentation layer serves as the system's information display and interaction window, providing diverse terminal formats for different usage scenarios. The functional application layer carries the system's core business logic, responsible for the entire operation process management, intelligent assistance, risk control, and data analysis, providing comprehensive business support for back-office supervisors, management departments, and on-site supervisors. The data acquisition layer is responsible for collecting multi-source data from the operation site, mainly including video, images, and gas environment parameters, providing the data foundation for the functional application layer's real-time monitoring, AI analysis, and alarm processing.

[0063] The presentation layer includes mobile, web, and large-screen display terminals. The mobile terminal is used by on-site supervisors and is mainly used for operations such as work order confirmation, confirmation of pre-operation safety measures, alarm reception and feedback, etc., facilitating necessary supervisory interactions on-site. The web terminal is used to display approval processes, work supervision screens, statistical reports and other information in the management backend, facilitating unified viewing and operation by management personnel. The large-screen display terminal is used to centrally display the status of the work site, including real-time video, alarm distribution and work status overview, providing visual support for command and decision-making.

[0064] The functional application layer includes a knowledge base management module, a safety measure intelligent recommendation module, a work ticket management module, an intelligent video monitoring and alarm module, a data statistics module, and a permission management module. The knowledge base management module supports centralized uploading and storage of risk cases, regulations, standards, and company policies, providing knowledge support for work ticket approval, risk assessment, and intelligent recommendations. The safety measure intelligent recommendation module automatically generates corresponding safety measure lists based on risk level, work type, and knowledge base rules, assisting supervisors in quickly configuring pre-work safety protection. The work ticket management module enables fully electronic processing of work tickets, covering application, approval, execution, closure, and archiving, and supports ticket tracking. The system includes: a traceability and compliance verification module; an intelligent video monitoring and alarm module that uses fixed or mobile video terminals and AI algorithms to identify violations and abnormal states in real time, automatically triggering alarms and pushing them to supervisors; a data statistics module that performs statistical analysis and visualization of data such as work application volume, approval efficiency, violations, alarm types, and risk distribution to support management decisions; and an access control module based on the RBAC model that hierarchically controls system users' operation permissions and data access, enabling role definition, permission allocation, user management, and operation auditing. This ensures that users in different departments and positions can only access and operate functions and information within their scope of responsibility, guaranteeing system security and compliance.

[0065] The data acquisition layer includes mobile surveillance cameras, fixed cameras, smart helmets, law enforcement recorders, and gas detectors. Mobile surveillance cameras are used for video capture and real-time transmission at temporary or key work sites, supporting dynamic monitoring of on-site operations by back-end supervisors. Fixed cameras provide continuous video monitoring of the work site, achieving 24 / 7 coverage of key areas and high-risk work sites. Smart helmets, worn by operators or supervisors, collect real-time video, photos, and voice information from the work site, and can upload location or status data to the system. Law enforcement recorders, carried by supervisors, record on-site operations and abnormal situations, ensuring on-site behavioral traceability and evidence preservation. Gas detectors collect real-time environmental parameters such as combustible gases, toxic gases, and oxygen at the work site, and upload abnormal data to the system for work risk assessment and alarm triggering. Therefore, the full-process management of electronic work permits (application, approval, execution, and archiving) achieves digitalization and standardization of work supervision, reducing the cumbersome and error-prone paper processes and accelerating work initiation. The intelligent safety measure recommendation module and the automatic generation of AI algorithm lists reduce manual configuration time and subjective judgment, improving the efficiency of generating supervisory strategies. Meanwhile, the user experience is enhanced by allowing quick adjustments through operation controls (such as checkmarks and edits); the presentation layer provides mobile, web, and large-screen interfaces, supporting real-time interaction among on-site personnel, managers, and decision-makers, enabling information synchronization and collaborative work, and reducing communication costs; the data statistics module analyzes and visualizes work application volume, violations, and alarm types, providing insights for management and supporting resource allocation and process optimization; the data acquisition layer collects multi-source data through various devices (such as cameras and gas detectors), combines AI algorithms to identify anomalies in real time, trigger alarms and push notifications, achieving proactive supervision and rapid response.

[0066] According to embodiments of the present invention, a configurable monitoring method based on risk classification and job type matching automatically generates an initial list of safety measures by combining a safety production knowledge base (regulations, standards, and enterprise procedures) and historical job cases. This ensures that the measures are both compliant with regulations and based on practical experience, reducing human negligence and improving the comprehensiveness and relevance of safety measures. Based on an AI algorithm mapping table, it recommends optimal AI algorithms (such as safety helmet detection and smoke detection) for different job types and risk levels, enabling real-time monitoring and abnormal behavior identification, providing early warnings of potential risks, and reducing the accident rate. By dynamically adjusting safety measures and AI algorithms based on dimensions such as physical environment, equipment and materials, and personnel and organization, the monitoring process can adapt to complex changes on-site, enhancing the flexibility and accuracy of risk prevention and control. (Safety production knowledge base...) The use of historical cases makes safety measure recommendations more scientific and practical, assisting approvers in making more informed decisions. Based on different dimensions of status (such as environment, equipment, and personnel), users can add, delete, or modify safety measures and AI algorithms, and record the reasons for adjustments, enabling the system to adapt to diverse operational scenarios and enterprise needs. Role-based access control ensures data security and operational compliance, with users in different positions only able to access functions within their scope of responsibility, reducing the risk of misoperation. Through risk classification and job type matching, the system recommends the most necessary safety measures and AI algorithms, avoiding over-regulation or waste of resources and improving the utilization efficiency of equipment (such as cameras and detectors). Real-time monitoring and alarms help to promptly detect equipment failures or environmental anomalies, reducing downtime and maintenance costs.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0068] According to one aspect of the present invention, a configurable monitoring device based on risk classification and operation type matching is also proposed. Figure 4 This is a structural block diagram of a configurable monitoring device based on risk classification and operation type matching according to an embodiment of the present invention; as shown below. Figure 4 As shown, it includes:

[0069] The acquisition module 410 is used to acquire electronic work tickets, which include metadata, work type and work risk level;

[0070] The first generation module 420 is used to generate an initial safety measures list by combining the job type and the job risk level with a safety production knowledge base and historical job cases, and to generate an initial AI algorithm list by combining an AI algorithm mapping table.

[0071] The second generation module 430 is used to maintain the data of the initial safety measures list and the initial AI algorithm list based on different dimensions of the state, and form an operation supervision process.

[0072] The approval module 440 is used to submit the electronic work ticket and the work supervision process to the approver for approval. If the approval is approved, the work can be started and the work safety control is initiated. If the approval is not approved, the electronic work ticket and the work supervision process are modified until the approval is approved.

[0073] According to an embodiment of the present invention, a configurable monitoring device based on risk classification and job type matching acquires an electronic work order, which includes metadata, job type, and job risk level. The device combines the job type and job risk level with a safety production knowledge base and historical job cases to generate an initial list of safety measures and an initial list of AI algorithms using an AI algorithm mapping table. Based on different dimensions of status, the device maintains the initial list of safety measures and the initial list of AI algorithms to form a job supervision process. The electronic work order and the job supervision process are submitted to approvers for review. If approved, the work is allowed to begin and job safety control is initiated. If not approved, the electronic work order and the job supervision process are modified until approval is granted. This dynamic binding of safety measures, AI algorithms, job risk level, and job type ensures the intensity of supervision for high-risk jobs while avoiding alarm overload in low-risk scenarios, resolving the contradiction between resource allocation and safety requirements.

[0074] Optionally, the first generation module 420 is specifically configured to: search for all rules applicable to the job type and the job risk level from the safety production database, and extract the safety measures corresponding to the rules to form a first safety measure set; search for all cases applicable to the job type and the job risk level from the historical job cases, and determine the safety measures that appear more frequently than a preset threshold in the cases to form a second safety measure set; merge the first safety measure set and the second safety measure set, and remove duplicates from the merged safety measures to obtain deduplicated safety measures; sort the deduplicated safety measures according to the importance level and source weight of the measures to obtain the initial safety measure list.

[0075] Optionally, the first generation module 420 is specifically used to obtain the AI ​​algorithm mapping table, which defines the AI ​​algorithms required for different job risks and scenarios; and to find the AI ​​algorithms corresponding to the job type and the job risk level from the AI ​​algorithm mapping table to form the initial AI algorithm list.

[0076] Optionally, the different dimensions include physical environment dimension, equipment and material dimension, personnel and organization dimension, time and dynamic dimension, and system and resource availability dimension.

[0077] Optionally, the second generation module 430 is specifically used to add, delete, and / or modify the initial safety measures list based on the different dimensional states through the operation controls of the safety measures list; and to add, delete, and / or modify the initial AI algorithm list based on the different dimensional states through the operation controls of the AI ​​algorithm list, thereby forming the operation supervision process.

[0078] Optionally, the approval module 440 is specifically used to determine the permissions of the approver, wherein the approver is subject to hierarchical control based on the RBAC model, and the approver's permissions include functional permissions and data permissions; based on the approver's permissions, the electronic work ticket and the work supervision process are submitted to the approver for approval.

[0079] Optionally, it also includes a data acquisition module and an archiving module. The data acquisition module is used to trigger alarm processing when collecting work environment parameters and abnormal data is present. The archiving module is used to archive and save the electronic work ticket, supervision record, monitoring video, and alarm information after the work is completed.

[0080] According to one aspect of the present invention, an electronic device is provided.

[0081] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 5 As shown, an electronic device may include one or more ( Figure 5 Only one is shown in the image. A processor 102 (which may include, but is not limited to, a microprocessor unit (MPU) or a programmable logic device (PLD)) and a memory 104 for storing data are also shown. In one exemplary embodiment, the electronic device may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 5 The structure shown is for illustrative purposes only and does not limit the structure of the terminal device described above. For example, the terminal device may also include components that are more...Figure 5 The more or fewer components shown, or having the same Figure 5 Equivalent functions or ratios shown Figure 5 The functions shown have more different configurations.

[0082] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the configurable monitoring method based on risk classification and job type matching in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to terminal devices via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the switching device. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0084] This invention proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute a configurable monitoring method based on risk classification and job type matching.

[0085] The applicant of this invention has provided a detailed description of the embodiments of the invention in conjunction with the accompanying drawings. However, those skilled in the art should understand that the above embodiments are merely preferred embodiments of the invention. The detailed description is only intended to help readers better understand the spirit of the invention and is not intended to limit the scope of protection of the invention. On the contrary, any improvements or modifications made based on the inventive spirit of the invention should fall within the scope of protection of the invention.

[0086] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0087] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A configurable supervisory method based on risk rating and job type matching, characterized by, Comprise: S1, obtain an electronic work order, the electronic work order comprising metadata, work type and work risk level; S2, combine the work type and the work risk level with a safety production knowledge base and historical work cases to generate an initial safety measure list, and combine the initial safety measure list with an AI algorithm mapping table to generate an initial AI algorithm list; S3, based on the state of different dimensions, data maintenance is performed on the initial safety measure list and the initial AI algorithm list to form a work supervision process; S4, submit the electronic work order and the work supervision process to an approval personnel for approval, in the case of approval, allow the work to start and start work safety control, in the case of disapproval, modify the electronic work order and the work supervision process until the approval is passed.

2. The configurable supervisory method based on risk grading and job type matching of claim 1, wherein, Combine the work type and the work risk level with a safety production knowledge base and historical work cases to generate an initial safety measure list, comprising: Find all rules applicable to the work type and the work risk level from the safety production database, and extract the safety measures corresponding to the rules to form a first safety measure set; Find all cases applicable to the work type and the work risk level from the historical work cases, and determine the safety measures with a frequency greater than a preset threshold in the cases to form a second safety measure set; Merge the first safety measure set and the second safety measure set, and remove duplicates of the merged safety measures to obtain the de-duplicated safety measures; Sort the de-duplicated safety measures according to the importance level of the measures and the source weight of the measures to obtain the initial safety measure list.

3. The configurable supervisory method based on risk grading and job type matching as claimed in claim 1, wherein, Combine the initial safety measure list with an AI algorithm mapping table to generate an initial AI algorithm list, comprising: Obtain the AI algorithm mapping table, which defines the AI algorithms required for different work risks and scenarios; Find the AI algorithms corresponding to the work type and the work risk level from the AI algorithm mapping table to form the initial AI algorithm list.

4. The configurable supervisory method based on risk grading and job type matching of claim 1, wherein, The different dimensions include physical environment dimension, equipment and material dimension, personnel and organization dimension, time and dynamic dimension, system and resource availability dimension.

5. The configurable supervisory method based on risk grading and job type matching as claimed in claim 1, wherein, Based on the state of different dimensions, data maintenance is performed on the initial safety measure list and the initial AI algorithm list to form a work supervision process, comprising: Based on the state of different dimensions, add, delete and / or modify the initial safety measure list through the operation controls of the safety measure list; Based on the state of different dimensions, add, delete and / or modify the initial AI algorithm list through the operation controls of the AI algorithm list to form the work supervision process.

6. The configurable supervisory method based on risk grading and job type matching as claimed in claim 1, wherein, Submit the electronic work order and the work supervision process to an approval personnel for approval, comprising: Determine the permissions of the approval personnel, wherein the approval personnel are controlled hierarchically based on an RBAC model, and the permissions of the approval personnel include function permissions and data permissions; Based on the permissions of the approval personnel, submit the electronic work order and the work supervision process to the approval personnel for approval.

7. The configurable supervisory method based on risk grading and job type matching as claimed in claim 1, wherein, Further comprise: In the case of collecting operation environment parameters and there are abnormal data, triggering alarm processing; And after the completion of the operation, the electronic operation ticket, supervision record, monitoring video, alarm information are filed and saved.

8. A configurable supervisory device based on risk ranking and job type matching, characterized by, Comprise: An acquisition module is configured to acquire an electronic operation ticket, wherein the electronic operation ticket comprises metadata, operation type and operation risk level; A first generation module is configured to generate an initial safety measure list by combining the operation type and the operation risk level with a safety production knowledge base and historical operation cases, and generate an initial AI algorithm list by combining an AI algorithm mapping table; A second generation module is configured to perform data maintenance on the initial safety measure list and the initial AI algorithm list based on different dimensions of states to form an operation supervision process; An approval module is configured to submit the electronic operation ticket and the operation supervision process to an approval personnel for approval, allow starting operation and starting operation safety control in the case of passing the approval, and modify the electronic operation ticket and the operation supervision process until the approval passes in the case of failing to pass the approval.

9. An electronic device, comprising: Comprise: At least one processor; And A memory connected in communication with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1 to 7.

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