Operation and maintenance safety management system adaptive to smart park

Abstract

This invention belongs to the field of smart park security management and involves data analysis technology. It addresses the problem that existing technologies cannot comprehensively analyze the risk priority of inspection nodes and the spatial distribution of each node. Specifically, it is adapted to the operation and maintenance security management system of smart parks, including a confidence assessment module, a dual-line analysis module, an inspection configuration module, and a task allocation module that are connected in sequence. The dual-line analysis module, the inspection configuration module, and the task allocation module are all connected to a database. This application plans inspection tasks from two core dimensions: risk priority and inspection efficiency. The confidence route ensures that high-risk nodes are prioritized, while the efficiency route ensures the shortest possible total inspection path. This bidirectional analysis mechanism enables the system to better balance the urgency of inspections and overall efficiency in subsequent configuration and allocation processes.

Description

Technical Field

[0001] This invention belongs to the field of smart park security management and involves data analysis technology, specifically adapted to the operation and maintenance security management system of smart parks. Background Technology

[0002] With the deep integration of technologies such as the Internet of Things, big data, and artificial intelligence, traditional industrial parks and science parks are accelerating their transformation into "smart parks." Smart parks integrate and deploy massive amounts of IT infrastructure (such as servers and network equipment), OT operation equipment (such as power distribution, HVAC, and elevators), and various IoT sensing terminals (such as environmental sensors and smart cameras), forming a complex and interconnected physical-information fusion system. Ensuring the safe, stable, and efficient operation of this huge system has become the core challenge of park operation and maintenance management.

[0003] The invention patent with publication number CN118175264B discloses a smart property-based unmanned inspection security monitoring system for parks. This monitoring system determines the optimized inspection path for drones by analyzing the priority weights of each access path belonging to each key space. Then, by extracting historical inspection data, it further optimizes the inspection path to make the drone inspection path more in line with the actual needs of the park. However, this monitoring system can only plan routes for a single indicator and cannot combine the risk priority of inspection nodes with the spatial distribution between nodes for comprehensive analysis. As a result, the generated inspection path cannot take into account both the inspection processing priority of nodes and the overall inspection execution efficiency.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide an operation and maintenance safety management system adapted to smart parks, which solves the problem that existing technologies cannot comprehensively analyze the risk priority of inspection nodes and the spatial distribution of each node.

[0006] The technical problem to be solved by this invention is: how to provide an operation and maintenance safety management system adapted to smart parks that can comprehensively analyze the risk priority of inspection nodes and the spatial distribution of each node.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] An operation and maintenance security management system adapted to smart parks includes a confidence assessment module, a dual-line analysis module, an inspection configuration module, and a task allocation module that are connected in sequence. The dual-line analysis module, the inspection configuration module, and the task allocation module are all connected to the database.

[0009] The confidence assessment module is used to perform confidence assessment analysis on the inspection nodes in the smart park: generating a continuous management cycle, acquiring fault data, density data and correlation data of the inspection nodes at the beginning of the management cycle; and generating a confidence value of the inspection nodes through the fault data, density data and correlation data.

[0010] The dual-line analysis module is used to perform bidirectional analysis on the inspection route of the smart park: all inspection nodes are arranged in ascending order of confidence value to obtain a confidence route; the inspection node with the smallest confidence value is marked as the starting node, and an efficiency route is generated with the starting node as the inspection starting position. The efficiency route meets the following requirements: the total inspection path is the shortest.

[0011] The inspection configuration module is used to analyze and configure the inspection routes and inspection robots in the smart park and generate several configuration sub-routes.

[0012] The task allocation module is used to allocate and analyze the operation and maintenance inspection tasks of the smart park: it retrieves several inspection robots and marks them as execution objects. The number of execution objects retrieved is equal to the number of sub-routes generated. The execution objects are then assigned tasks according to the configuration sub-routes.

[0013] Furthermore, the fault data is the failure rate of the key equipment corresponding to the inspection node in the most recent L1 days; the process of obtaining density data includes: retrieving the pedestrian flow heat map of the smart park, drawing a circle with the inspection node as the center and R1 as the radius, marking the obtained circular area as the heat area, and marking the pedestrian flow density value of the heat area in the pedestrian flow heat map as density data; the associated data is the number of associated equipment of the key equipment of the inspection node.

[0014] Furthermore, the process of generating the confidence value of the inspection node includes: arranging all inspection nodes in the smart park in descending order of fault data, descending order of density data, and descending order of correlation data to obtain the fault sequence, density sequence, and correlation sequence; and marking the sum of the serial numbers of the inspection node in the fault sequence, density sequence, and correlation sequence as the confidence value of the inspection node.

[0015] Furthermore, the specific process of the inspection configuration module in configuring and analyzing the inspection routes and inspection robots in the smart park includes: marking key nodes as configuration objects i, i=1, 2, ..., n, where n is a positive integer; performing configuration analysis on confidence routes and efficiency routes: marking the confidence routes and efficiency routes generated by configuration object i as Ai and Bi respectively; determining whether the first-ranked configuration object in Ai and Bi corresponds to the same key node; if yes, adding the first-ranked key node in Bi to the configuration sub-route, removing the starting nodes of Ai and Bi, and re-determining whether the first-ranked configuration objects in Ai and Bi correspond to the same key node; if no, performing isolation analysis; and so on, until all key nodes are added to the corresponding configuration sub-route.

[0016] Furthermore, the specific process of isolation analysis includes: marking the configuration object ranked first in Bi with its ordinal number in Ai as the isolation value; retrieving the isolation threshold from the database; comparing the isolation value with the isolation threshold; and selecting the key nodes for inserting configuration sub-routes based on the comparison results.

[0017] Furthermore, the specific process of comparing the isolation value with the isolation threshold includes: if the isolation value is less than the isolation threshold, the first-ranked critical node in Bi is marked as an insertion node, the insertion node is added to the configuration sub-route, and then the insertion nodes in Ai and Bi are removed, and it is re-determined whether the first-ranked configuration objects in Ai and Bi correspond to the same critical node; if the isolation value is greater than or equal to the isolation threshold, the first-ranked configuration object in Ai is marked as a truncated node, the critical node in Bi located after the truncated node is marked as a cutoff node, the truncated node and the cutoff node are added to the configuration sub-route in sequence, and the configuration sub-route is sealed to generate a new configuration sub-route for subsequent critical node addition, and then it is re-determined whether the first-ranked configuration object in Ai and Bi corresponds to the same critical node.

[0018] Furthermore, the specific process of task allocation for execution objects includes: obtaining the load value of the configuration sub-route, arranging the configuration sub-route in ascending order of load value to obtain a load sequence, arranging the execution objects in descending order of remaining power to obtain a saturation sequence, selecting the execution objects in the saturation sequence and assigning them to configuration sub-routes with the same sequence number in the load sequence, and performing operation and maintenance inspections according to the allocation results for the execution objects.

[0019] Furthermore, the number of key nodes and the total inspection distance in the configuration sub-route are marked as the execution value and travel value of the configuration sub-route, respectively. All configuration sub-routes are arranged in descending order of execution value and descending order of travel value to obtain execution sequence and travel sequence. The sum of the sequence numbers of the configuration sub-route in the execution sequence and travel sequence is marked as the load value of the configuration sub-route.

[0020] The present invention has the following beneficial effects:

[0021] By defining fault data as the failure rate of key equipment corresponding to inspection nodes within the most recent L1 day, the system can promptly capture the operational health status of equipment, avoiding judgments based on outdated information. By introducing density data from pedestrian flow heatmaps, the system can incorporate the impact of environmental factors and personnel activities on equipment, making the assessment results closer to actual operating scenarios. Simultaneously, by quantifying the number of associated devices of key equipment as correlation data, the system can accurately identify nodes with high-risk impacts on the operation of the entire park. These specific and quantified data definitions greatly improve the accuracy and reliability of the confidence values ​​generated by the confidence assessment module, thereby enabling subsequent inspection route planning and task allocation to be more scientific and reasonable. This effectively avoids misjudgments and resource waste caused by data ambiguity, significantly improving the intelligence level and overall efficiency of smart park operation and maintenance inspections.

[0022] This application plans inspection tasks from two core dimensions: risk priority and inspection efficiency. The confidence route ensures that high-risk nodes are processed first, while the efficiency route guarantees the shortest possible total inspection path. This two-way analysis mechanism enables the system to better balance the urgency of inspections and overall efficiency in subsequent configuration and allocation processes, effectively avoiding the problem in existing technologies that "cannot take into account both the inspection processing priority of nodes and the overall inspection execution efficiency."

[0023] This technical challenge involves fusing confidence-based routes with shortest-path-based efficiency routes to generate configuration sub-routes that balance the importance of key nodes with inspection efficiency. When the starting nodes of the confidence and efficiency routes are the same, the node can be directly and efficiently included in the configuration sub-routes, ensuring that high-priority and high-efficiency nodes are processed first. When the starting nodes are different, an isolation analysis mechanism is introduced, allowing the system to weigh confidence and efficiency using more complex strategies, avoiding blind spots or resource waste that might result from simply favoring one side. This iterative configuration analysis process ensures that all key nodes are ultimately allocated appropriately to configuration sub-routes, significantly improving the planning quality and execution efficiency of maintenance and inspection tasks. This enables inspection robots to complete tasks more intelligently and effectively, ensuring the safe and stable operation of the smart park.

[0024] By refining the comparison results between isolation values ​​and isolation thresholds, the inspection configuration module can dynamically adjust the node selection strategy when generating configuration sub-routes based on the matching degree between confidence routes and efficiency routes. When the difference between two routes is small, the system prioritizes the nodes of the efficiency route to ensure inspection efficiency. When the difference is large, the system uses the nodes of the confidence route as the basis, archives the current configuration sub-routes, and generates new configuration sub-routes to avoid forcibly merging incompatible nodes, thereby ensuring the rationality and executability of each configuration sub-routes. This mechanism effectively balances the reliability and efficiency of inspections, making the generated configuration sub-routes more in line with actual operation and maintenance needs, and providing a more reliable foundation for subsequent task allocation.

[0025] By accurately assessing and sorting the load values ​​of the configured sub-routes, and combining this with the real-time remaining power of the execution objects (inspection robots), and employing a sequence-matching allocation strategy, task allocation becomes more scientific and reasonable. This not only ensures that the workload of each execution object matches its current capacity, avoiding situations where robots with sufficient power have too light a task or robots with insufficient power have too heavy a task, thus extending the single-cycle endurance of the inspection robots and reducing charging frequency and downtime, but also maximizes overall inspection efficiency, improves the continuity and reliability of smart park operation and maintenance safety management, reduces the need for manual intervention, and further enhances the level of automation. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] In traditional smart park operation and maintenance management, the integrated IT infrastructure, OT operation equipment and IoT sensing terminals in the park constitute a complex physical-information fusion system. Existing inspection path planning methods can only optimize a single indicator and cannot simultaneously consider the risk priority of inspection nodes and the spatial distribution characteristics between nodes. This leads to a conflict between the node processing priority and the overall execution efficiency in the generated inspection path, which in turn affects the system reliability and the rationality of operation and maintenance resource allocation.

[0031] For example, in the actual operation and maintenance scenarios of large technology parks, inspection nodes such as power distribution rooms, server rooms, and environmental monitoring points are distributed in different areas. The heat map of human flow shows that the density around some nodes is high, and the historical failure rates of key equipment vary. When a planning method that only aims at the shortest path is adopted, the system prioritizes the inspection of low-risk nodes that are spatially close, while delaying the processing of high-risk nodes (such as power distribution equipment with a high historical failure rate). This results in high-risk nodes not being included in the inspection sequence in a timely manner. At the same time, the inspection robot needs to travel back and forth to the scattered nodes multiple times, increasing the ineffective movement distance.

[0032] If the above problems are not resolved, high-risk nodes may cause equipment failures to spread due to inspection delays, leading to a decrease in system stability. At the same time, if the inspection path does not take into account the risk characteristics and spatial relationships of nodes, it will cause repeated scheduling of operation and maintenance resources, reduce the overall inspection efficiency, and may expand potential security risks due to missed inspections of critical nodes.

[0033] For ease of understanding, the following explains some key terms in this embodiment:

[0034] A smart park refers to a modern park that integrates and manages its infrastructure, operating equipment, and various sensing terminals through technologies such as the Internet of Things, big data, and artificial intelligence to achieve safe, stable, and efficient operation.

[0035] An operation and maintenance security management system is a management platform used to ensure the normal operation of various devices and systems within a smart park and to improve their security.

[0036] A database is a storage system used to store and manage various types of data within a smart park (such as inspection node information, fault records, and pedestrian flow data).

[0037] Inspection nodes refer to physical locations or equipment points within a smart park that require regular or irregular inspection and maintenance.

[0038] The confidence value is an assessment value assigned to each inspection node after comprehensive analysis of fault data, density data, and correlation data. This value reflects the importance or risk level of the inspection node.

[0039] A confidence route is an inspection path formed by arranging all inspection nodes in ascending order of their confidence values. Nodes with lower confidence values ​​(i.e., higher risk or priority) are usually prioritized.

[0040] An efficiency route refers to an inspection route that starts from the inspection node with the lowest confidence value and aims to achieve the shortest total inspection path.

[0041] The configuration sub-route refers to the local inspection path generated by the inspection configuration module and assigned to a single inspection robot for execution.

[0042] Inspection robots are robotic devices that perform automated inspection tasks within smart parks.

[0043] The execution target refers to the inspection robot that is summoned and assigned inspection tasks.

[0044] Example 1: As Figure 1 As shown, the operation and maintenance security management system adapted to smart parks includes a confidence assessment module, a dual-line analysis module, an inspection configuration module, and a task allocation module that are connected in sequence. The dual-line analysis module, the inspection configuration module, and the task allocation module are all connected to the database.

[0045] The confidence assessment module is used to perform confidence assessment analysis on inspection nodes within the smart park: It generates continuous management cycles, acquiring fault data, density data, and correlation data for inspection nodes at the beginning of each cycle. Fault data represents the failure rate of the key equipment corresponding to the inspection node within the most recent L1 days. The density data acquisition process includes: retrieving the pedestrian heat map of the smart park, drawing a circle with the inspection node as the center and R1 as the radius, marking the resulting circular area as a heat map area, and marking the pedestrian density value of the heat map area as density data. Correlation data represents the number of associated devices of the key equipment of the inspection node. All inspection nodes within the smart park are arranged in descending order of fault data, density data, and correlation data to obtain fault sequences, density sequences, and correlation sequences. The sum of the indices of the inspection node in the fault sequence, density sequence, and correlation sequence is marked as the confidence value of the inspection node. The inspection node with the smallest confidence value is marked as the starting node.

[0046] Specifically, this module first generates continuous management cycles, such as daily, weekly, or monthly. At the beginning of each management cycle, the module acquires fault data, density data, and related data for the inspection nodes. For example, fault data can be the number of recent faults entered manually or extracted from equipment logs; density data can be the average pedestrian traffic in the area based on historical statistics; and related data can be the number of other devices associated with the node, manually maintained. Subsequently, the module generates a confidence value for each inspection node by performing a simple weighted average or table lookup operation on these acquired fault data, density data, and related data. For example, nodes with more faults, higher pedestrian density, and more associated devices can have lower confidence values, indicating higher priority.

[0047] The fault data is defined as the failure rate of the critical equipment corresponding to the inspection node within the most recent L1 day. This data aims to quantify the operational reliability of critical equipment, directly reflecting the frequency of its recent failures. It can be obtained by continuously monitoring the equipment's operating status, recording each fault event, and calculating the ratio of the number of failures within the specified time period L1 to the total operating time. Alternatively, this data can be extracted from historical operating logs provided by the equipment manufacturer or from the equipment management platform within the smart park, which typically aggregates equipment fault records and operational statistics.

[0048] The process of acquiring density data involves retrieving the pedestrian heat map of the smart park, drawing a circle with the inspection node as the center and R1 as the radius, marking the resulting circular area as a heat map area, and marking the pedestrian density value of the heat map area as density data. This data reflects the density of personnel activity around the inspection node, indirectly indicating potential wear and tear, safety hazards, or environmental changes in the area. Pedestrian heat maps can be generated using various technologies, such as using Wi-Fi probes, Bluetooth beacons, video surveillance systems (combined with AI visual analysis technology), or mobile communication base station data deployed within the park to collect and aggregate personnel location information in real time. Another approach is to utilize data from environmental sensors deployed in specific areas (such as carbon dioxide concentration sensors and air quality sensors), which can also reflect changes in personnel density within the area to some extent.

[0049] Correlation data is defined as the number of associated devices of a critical device at an inspection node. This data aims to assess the potential cascading impact on the entire system should a single critical device fail. It can be obtained by constructing a topology diagram or dependency diagram of all devices within the smart park and then counting the number of other devices directly or indirectly connected to the critical device. Alternatively, this data can be obtained by consulting the device's system design documents, equipment lists, or extracting it from the park's asset management system, which typically details the connections and functional dependencies between devices.

[0050] The dual-line analysis module is used to perform bidirectional analysis on the inspection routes of the smart park: all inspection nodes are arranged in ascending order of confidence value to obtain a confidence route; an efficiency route is generated with the starting node as the starting position of the inspection. The efficiency route meets the following requirements: the total inspection path is the shortest.

[0051] This module generates both confidence routes and efficiency routes, planning inspection tasks from two core dimensions: risk priority and inspection efficiency. Confidence routes ensure that high-risk nodes are prioritized, while efficiency routes guarantee the shortest possible overall inspection path. This bidirectional analysis mechanism allows the system to better balance the urgency and economy of inspections during subsequent configuration and allocation, effectively avoiding the problem in existing technologies that "cannot balance the inspection priority of nodes with the overall inspection execution efficiency."

[0052] The above solutions propose confidence assessment analysis of inspection nodes within a smart park, and based on this, generate confidence routes and efficiency routes. However, in the implementation process, simply having these two routes does not directly provide an optimal or robust strategy for configuring actual inspection sub-routes. When there are differences in the node ordering of the confidence route and the efficiency route, how to effectively balance the risk priority of key nodes with the operational efficiency of the inspection path to generate a configured sub-route that takes into account the advantages of both is a technical problem that needs to be solved.

[0053] The inspection configuration module is used to perform configuration analysis on the inspection routes and inspection robots in the smart park: Key nodes are marked as configuration objects i, i = 1, 2, ..., n, where n is a positive integer. Configuration analysis is performed on confidence routes and efficiency routes: the confidence route and efficiency route generated by configuration object i are marked as Ai and Bi respectively. It is determined whether the first-ranked configuration object in Ai and Bi corresponds to the same key node: if yes, the first-ranked key node in Bi is added to the configuration sub-route, the starting node of Ai and Bi is removed, and the first-ranked configuration object in Ai and Bi is re-determined to correspond to the same key node; if no, isolation analysis is performed: the sequence number of the first-ranked configuration object in Bi is marked as the isolation value in Ai, the isolation threshold is retrieved from the database, and the isolation value is compared with the isolation threshold: if the isolation value is less than the isolation threshold, then... The first critical node in Bi is marked as an insertion node, and the insertion node is added to the configuration sub-route. Then, the insertion nodes in Ai and Bi are removed, and it is re-determined whether the first-ranked configuration objects in Ai and Bi correspond to the same critical node. If the isolation value is greater than or equal to the isolation threshold, the first-ranked configuration object in Ai is marked as a truncated node, and the critical nodes in Bi that are after the truncated node are marked as truncated nodes. The truncated nodes and truncated nodes are added to the configuration sub-route in sequence, and the configuration sub-route is sealed to generate a new configuration sub-route for subsequent critical node additions. The truncated nodes and truncated nodes in Ai and Bi are removed, and it is re-determined whether the first-ranked configuration objects in Ai and Bi correspond to the same critical node. This process is repeated until all critical nodes are added to their corresponding configuration sub-routes.

[0054] In this system, key nodes are labeled as configuration objects i, where i = 1, 2, ..., n, and n is a positive integer. This means assigning a unique numerical identifier i to every specific location or equipment point within the smart park that needs to be inspected. These key nodes typically represent important infrastructure, high-risk areas, or equipment requiring special attention. Through this labeling, the system can uniformly and identifiably reference and process these nodes during the configuration analysis process, ensuring that all key nodes awaiting inspection are included in the configuration flow.

[0055] Configuration analysis of confidence routes and efficiency routes involves comparing, integrating, and optimizing inspection routes with different sorting logics to generate practically usable configuration sub-routes. The goal is to determine the inspection order and grouping while considering both the confidence level (importance or risk) of key nodes and the efficiency of the inspection path. For example, algorithmic logic can iteratively compare the node order of the two routes, deciding which nodes to include and exclude based on preset rules; alternatively, graph theory algorithms or heuristic search methods can be used to find the optimal combination of configuration sub-routes under the constraints of confidence routes and efficiency routes.

[0056] Labeling the confidence route and efficiency route generated by configuration object i as Ai and Bi respectively means that during configuration analysis, the key node sequence sorted based on confidence value is temporarily designated as Ai, reflecting the risk or importance priority of the nodes; the key node sequence generated based on the shortest path principle is temporarily designated as Bi, reflecting the efficiency priority of the inspection. This labeling helps to clearly reference and operate these two routes in the algorithm.

[0057] Determining whether the first-ranked configuration object in Ai and Bi corresponds to the same critical node is one of the core judgment logics in configuration analysis. It checks whether the starting nodes of the confidence path and the efficiency path are consistent at the current stage. If they are consistent, it means that the most important (first in Ai) and most efficient (first in Bi) node is the same, and can be prioritized. For example, this can be done by comparing whether the first element of the Ai list and the first element of the Bi list have the same critical node identifier; or, at the beginning of each iteration, obtaining the head nodes of Ai and Bi and calling a comparison function to determine whether they point to the same physical critical node.

[0058] If so, add the first-ranked key node in Bi to the configuration sub-route, remove the starting nodes of Ai and Bi, and re-determine whether the first-ranked configuration objects in Ai and Bi correspond to the same key node. This means that if the starting nodes of Ai and Bi are the same, it indicates that the node simultaneously satisfies the priority conditions of high confidence and high efficiency. Adding it to the configuration sub-route is a reasonable choice. Subsequently, remove the node from Ai and Bi to continue the next round of judgment and configuration for the remaining nodes. For example, add the first element of Bi to the configuration sub-route list, then remove the element from Ai and Bi, and loop back to the judgment step; or, use a pointer or iterator to point to the current starting position of Ai and Bi, and move the pointer or iterator to the next node after the node is selected and added to the configuration sub-route.

[0059] If not, isolation analysis is performed when the starting nodes of Ai and Bi are inconsistent, meaning that the most important node and the most efficient node are different. Isolation analysis is needed to determine how to balance these two priorities, which node to add to the configuration sub-route, or how to adjust subsequent configuration strategies. For example, a separate isolation analysis subroutine or function can be called to handle this inconsistency based on preset rules or parameters; alternatively, one node can be directly selected based on preset priority rules (e.g., confidence priority or efficiency priority), or a more complex evaluation model can be used for decision-making.

[0060] This process continues until all critical nodes are added to their corresponding configuration sub-routes. This indicates that configuration analysis is an iterative process; the above judgment and processing logic will be executed repeatedly until all critical nodes are assigned to at least one configuration sub-route. This ensures that all critical nodes requiring inspection are eventually included in the inspection plan. For example, this can be implemented using a while loop, where the loop condition is that the set of critical nodes is not empty, or that there are still unprocessed nodes in Ai and Bi; or, a recursive function can be used, processing each pair of starting nodes in Ai and Bi until all nodes have been processed.

[0061] The specific process of isolation analysis aims to provide a decision-making mechanism when the top-ranked configuration object in confidence route Ai and efficiency route Bi are inconsistent, intelligently selecting key nodes to be added to subsequent configuration sub-routes. Its purpose is to avoid simply discarding the advantage of one route, but rather to generate more optimized inspection sub-routes by quantifying differences and combining them with preset criteria. Marking the ordinal number of the top-ranked configuration object in Bi in Ai as the isolation value is a step used to quantify the relative importance or priority of priority nodes in the confidence route. Specifically, the system can traverse confidence route Ai, find the position index of the top-ranked configuration object in efficiency route Bi in Ai, and use this index value as the isolation value. Alternatively, the system can pre-store the ordinal number of each node in confidence route Ai, and directly query the pre-stored ordinal number of the top-ranked configuration object in efficiency route Bi in Ai when the isolation value needs to be calculated. Retrieving the isolation threshold from the database introduces a configurable decision criterion. The isolation threshold is a preset value used to determine whether the isolation value is within an acceptable range. This threshold can be set by operations personnel based on actual operational strategies, risk preferences, or historical data analysis results, and stored in the database for dynamic retrieval by the system during isolation analysis. Comparing the isolation value with the isolation threshold is the core decision-making step. By comparing the isolation value and the isolation threshold, the system can determine whether nodes prioritized for efficiency routes still have sufficient confidence. For example, it can determine if the isolation value is less than the isolation threshold. The selection of key nodes for insertion into the configuration sub-route based on the comparison results involves implementing different strategies to determine which key node to add to the current configuration sub-route. For example, if the isolation value meets specific conditions (such as being less than the isolation threshold), the node ranked first in efficiency route Bi may be prioritized; conversely, if the isolation value does not meet the conditions, other strategies may be necessary.

[0062] This application provides an intelligent and flexible isolation analysis mechanism that effectively solves the decision-making challenge caused by the inconsistency between the starting nodes of confidence routes and efficiency routes when generating configuration sub-routes. The scheme quantifies the relative position (isolation value) of efficiency-priority nodes in the confidence sequence and compares it with a configurable isolation threshold. This allows the system to dynamically balance the efficiency and safety of inspection tasks based on actual operational needs and risk preferences. This avoids simplistic and arbitrary decision-making that may occur during route generation, thus generating optimized configuration sub-routes that effectively cover high-risk areas while minimizing inspection paths. Ultimately, this significantly improves the rationality, safety, and execution efficiency of smart park operation and maintenance inspection tasks.

[0063] The task allocation module is used to allocate and analyze the operation and maintenance inspection tasks of the smart park: It retrieves several inspection robots and marks them as execution objects, with the number of retrieved execution objects equal to the number of generated configuration sub-routes. Task allocation is then performed on the execution objects: the number of key nodes and the total inspection distance in the configuration sub-routes are marked as the execution value and travel value of the configuration sub-routes, respectively. All configuration sub-routes are arranged in descending order of execution value and descending order of travel value to obtain the execution sequence and travel sequence. The sum of the sequence numbers of the configuration sub-routes in the execution sequence and travel sequence is marked as the load value of the configuration sub-routes. The configuration sub-routes are arranged in ascending order of load value to obtain the load sequence. The execution objects are arranged in descending order of remaining power to obtain the saturation sequence. Execution objects in the saturation sequence are selected and assigned to configuration sub-routes with the same sequence number in the load sequence. Operation and maintenance inspections are then performed according to the allocation results for each execution object.

[0064] The load value is an indicator that measures the complexity or workload required to configure a sub-route. It aims to quantify the resource input or time cost required to complete the inspection route. For example, the load value can reflect factors such as the number of critical nodes in the configured sub-route and the total inspection distance. By evaluating the load values ​​of the configured sub-routes, the system can obtain the relative "weight" of each route, providing a data foundation for subsequent balanced allocation. Arranging the configured sub-routes in ascending order of load value creates a load sequence; this step aims to prioritize or sort the generated configured sub-routes by difficulty. By placing routes with lower load values ​​first and routes with higher load values ​​later, the workload distribution of each route can be clearly displayed.

[0065] Example 2: Figure 2 As shown, the operation and maintenance security management method adapted to smart parks includes the following steps:

[0066] Step 1: Conduct confidence assessment and analysis of inspection nodes in the smart park: Generate a continuous management cycle and obtain the confidence value of the inspection node at the beginning of the management cycle;

[0067] Step 2: Perform bidirectional analysis on the inspection routes of the smart park: Arrange all inspection nodes in ascending order of confidence value to obtain a confidence route; generate an efficiency route with the starting node as the starting position of the inspection.

[0068] Step 3: Analyze the configuration of the inspection routes and inspection robots in the smart park and generate several configuration sub-routes;

[0069] Step 4: Analyze and allocate maintenance and inspection tasks for the smart park: Retrieve several inspection robots and mark them as execution objects, and allocate tasks to the execution objects by configuring the load value of the sub-route.

[0070] Adapted for the operation and maintenance safety management system of smart parks, during operation, it generates continuous management cycles. At the beginning of the management cycle, it obtains the confidence value of the inspection nodes; it arranges all inspection nodes in ascending order of confidence value to obtain a confidence route; it generates an efficiency route with the starting node as the inspection starting position; it performs configuration analysis on the inspection routes and inspection robots of the smart park and generates several configuration sub-routes; it calls up several inspection robots and marks them as execution objects, and assigns tasks to the execution objects based on the load value of the configuration sub-routes.

[0071] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0072] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0073] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A system for operation and maintenance safety management suitable for a smart park, characterized in that, It includes a confidence assessment module, a dual-line analysis module, an inspection configuration module, and a task allocation module that are connected in sequence. The dual-line analysis module, the inspection configuration module, and the task allocation module are all connected in communication with the database. The confidence assessment module is used to perform confidence assessment analysis on the inspection nodes in the smart park: generating a continuous management cycle, acquiring fault data, density data and correlation data of the inspection nodes at the beginning of the management cycle; and generating a confidence value of the inspection nodes through the fault data, density data and correlation data. The dual-line analysis module is used to perform bidirectional analysis on the inspection route of the smart park: all inspection nodes are arranged in ascending order of confidence value to obtain a confidence route; the inspection node with the smallest confidence value is marked as the starting node, and an efficiency route is generated with the starting node as the inspection starting position. The efficiency route meets the following requirements: the total inspection path is the shortest. The inspection configuration module is used to analyze and configure the inspection routes and inspection robots in the smart park and generate several configuration sub-routes. The task allocation module is used to allocate and analyze the operation and maintenance inspection tasks of the smart park: it retrieves a number of inspection robots and marks them as execution objects. The number of execution objects retrieved is equal to the number of configuration sub-routes generated. The execution objects are then assigned tasks according to the configuration sub-routes. The specific process of the inspection configuration module in configuring and analyzing the inspection routes and inspection robots in the smart park includes: marking key nodes as configuration objects i, i=1,2,...,n, where n is a positive integer; performing configuration analysis on confidence routes and efficiency routes: marking the confidence routes and efficiency routes generated by configuration object i as Ai and Bi respectively; determining whether the first-ranked configuration object in Ai and Bi corresponds to the same key node; if yes, adding the first-ranked key node in Bi to the configuration sub-route, removing the starting nodes of Ai and Bi, and re-determining whether the first-ranked configuration objects in Ai and Bi correspond to the same key node; if no, performing isolation analysis; and so on, until all key nodes are added to the corresponding configuration sub-route. The specific process of isolation analysis includes: marking the configuration object ranked first in Bi with its ordinal number in Ai as the isolation value; retrieving the isolation threshold from the database; comparing the isolation value with the isolation threshold; and selecting the key nodes for inserting configuration sub-routes based on the comparison results. 2.The operation and maintenance security management system adapted to a smart park according to claim 1, wherein, The fault data is the failure rate of the key equipment corresponding to the inspection node in the most recent L1 days; the process of obtaining density data includes: retrieving the pedestrian flow heat map of the smart park, drawing a circle with the inspection node as the center and R1 as the radius, marking the obtained circular area as the heat area, and marking the pedestrian flow density value of the heat area in the pedestrian flow heat map as density data; the associated data is the number of associated equipment of the key equipment of the inspection node.

3. The operation and maintenance security management system adapted to smart parks according to claim 2, characterized in that, The process of generating the confidence value of the inspection node includes: arranging all inspection nodes in the smart park in descending order of fault data, descending order of density data, and descending order of correlation data to obtain the fault sequence, density sequence, and correlation sequence; and marking the sum of the serial numbers of the inspection node in the fault sequence, density sequence, and correlation sequence as the confidence value of the inspection node.

4. The operation and maintenance security management system adapted to smart parks according to claim 3, characterized in that, The specific process of comparing the isolation value with the isolation threshold includes: if the isolation value is less than the isolation threshold, the first-ranked critical node in Bi is marked as an insertion node, the insertion node is added to the configuration sub-route, and then the insertion nodes in Ai and Bi are removed, and it is re-determined whether the first-ranked configuration objects in Ai and Bi correspond to the same critical node; if the isolation value is greater than or equal to the isolation threshold, the first-ranked configuration object in Ai is marked as a truncated node, the critical nodes in Bi that are after the truncated node are marked as truncated nodes, the truncated nodes and truncated nodes are added to the configuration sub-route in sequence, and the configuration sub-route is sealed to generate a new configuration sub-route for subsequent critical node addition, and then it is re-determined whether the first-ranked configuration objects in Ai and Bi correspond to the same critical node.

5. The operation and maintenance security management system adapted to smart parks according to claim 4, characterized in that, The specific process of task allocation for execution objects includes: obtaining the load value of the configuration sub-route, arranging the configuration sub-route in ascending order of load value to obtain a load sequence, arranging the execution objects in descending order of remaining power to obtain a saturation sequence, selecting the execution objects in the saturation sequence and assigning them to the configuration sub-route with the same sequence number in the load sequence, and performing operation and maintenance inspections according to the allocation results for the execution objects.

6. The operation and maintenance security management system adapted to smart parks according to claim 5, characterized in that, The number of key nodes and the total inspection distance in the configuration sub-route are marked as the execution value and travel value of the configuration sub-route, respectively. All configuration sub-routes are arranged in descending order of execution value and descending order of travel value to obtain the execution sequence and travel sequence. The sum of the sequence numbers of the configuration sub-route in the execution sequence and travel sequence is marked as the load value of the configuration sub-route.

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