Management method and system for municipal public facilities

Abstract

The invention relates to the technical field of product life cycle management, in particular to a management method and system for municipal public facilities, and the method comprises the following steps: calling facility operation monitoring data to calculate a disturbance variation, deducing a life cycle residual coefficient, judging a risk section, analyzing a task expansion speed, and adjusting a scheduling level. According to the method, the facility state offset is described through the operation disturbance variable quantity, the advance of risk identification is enhanced by utilizing the corresponding relation between the life cycle residual coefficient and the risk section, and the risk identification accuracy is improved. The task advancing speed trend is combined to correct the task sequence to enhance the distinguishing ability of task urgency, team response behavior characteristics are adopted to match the scheduling rhythm to improve the integrating degree of resource configuration, and the track overlapping characteristics and the operation state are used to measure the resource consumption level of collaborative operation at the same time; and the overall process overall planning capability from state cognition to resource implementation is realized.

Description

Technical Field

[0001] This invention relates to the field of product lifecycle management technology, and in particular to management methods and systems for municipal public facilities. Background Technology

[0002] Product lifecycle management technology involves the comprehensive management of information flow, material flow, and business flow throughout the entire process of a product, from conceptual design, project initiation, R&D, manufacturing, operation, maintenance, to decommissioning and recycling. Specifically, it includes a unified management mechanism for product structure definition, engineering data organization, configuration version control, change process coordination, and cross-departmental collaboration. By constructing a digital information model covering the entire lifecycle, it achieves systematic organization of product-related data and process management of business activities at different stages, ensuring information consistency and traceability. Traditional management methods for municipal public facilities, such as road lighting, traffic signals, drainage networks, and sanitation facilities, involve manual inspections to record facility location, condition, and abnormalities during daily operation. Problems are registered manually using ledgers or paper forms, and maintenance tasks are assigned via telephone or on-site reporting. Maintenance personnel then bring tools and equipment to the site to disassemble and inspect faulty components, clear blockages, or replace damaged parts. After the work is completed, maintenance information is updated manually. Traditional methods generally rely on offline verification, paper records, manual statistics, and decentralized management for facility status confirmation, defect handling, and maintenance process tracking.

[0003] Existing technologies rely on manual inspections, paper records, and experience-based judgments to collect facility status information. The detection path is limited by the coverage of manual inspections. Load fluctuations, start-stop anomalies, and early signs of failure in facility operation often cannot be identified in real time during continuous operation. The inspection cycle is fixed, making it difficult to adjust maintenance schedules according to dynamic changes in facility operation status. The scheduling process relies on manual judgment of task priority, lacking quantitative understanding of the scope and expansion trend of task impact. Differences in maintenance team response cannot be identified through system means. Resource matching is based on experience. The operating trajectories of work vehicles cannot be compared and analyzed during multi-vehicle collaboration. Repeated work is difficult to detect in a timely manner, resulting in delayed maintenance response, unbalanced resource allocation, and increased work redundancy. Summary of the Invention

[0004] To achieve the above objectives, the present invention adopts the following technical solution, a method for managing municipal public facilities, comprising the following steps: S1: Call the periodic operation facility monitoring dataset, extract various equipment operation indicators, calculate the load level and identify the number of start and stop actions, combine them to form a relative disturbance frequency, compare the deviation of the disturbance frequency with the equipment stable state, judge the fluctuation trend of the operating state, and generate operation disturbance factor parameters. S2: Call the aforementioned operational disturbance factor parameters, combine the activation time and operational cycle, calculate the operational load intensity and perform a difference mapping with the lifetime benchmark to form the lifetime remaining coefficient, match the reference risk segment, and generate risk comparison results; S3: Using the risk comparison results, receive scheduling task requests, analyze the differences in task distribution areas, calculate the task boundary expansion range, pair the boundary expansion distance with the cycle span, generate the advancement speed trend of the task's influence range, adjust the task scheduling order, and generate task level adjustment results. S4: Call the task level adjustment results, analyze the response actions of multiple maintenance teams, extract the duration of the response phase, form a team response behavior curve, compare it with the task rhythm parameters, evaluate the response adaptability, construct a comprehensive dispatch index, and generate facility maintenance matching results. S5: Call the facility maintenance matching results, analyze the trajectory sequence of the operation robot, identify overlapping operation sections, combine the device status information to determine the characteristics of repeated operation coverage, calculate the loss of redundant resources, and generate regional redundancy quantification results.

[0005] As a further aspect of the present invention, the operational disturbance factor parameters specifically include periodic disturbance frequency value, load level jump value, and start / stop action frequency density; the risk comparison results include the remaining lifecycle percentage, risk cluster interval number, and lifecycle matching status; the task level adjustment results specifically include spatial expansion rate level, task level ranking position, and ranking adjustment source identifier; the facility maintenance matching results include response rhythm adaptation coefficient, response team location path number, and comprehensive selection index value; and the regional redundancy quantification results specifically include the duplicate operation grid number, resource waste magnitude index, and duplicate coverage task load offset.

[0006] As a further aspect of the present invention, the step of obtaining the running disturbance factor parameters specifically includes: S101: Obtain the monitoring dataset of periodically operating facilities, extract various equipment operation indicators, including current, power, and flow, calculate the load level of each periodically operating facility within the cycle, and generate the load level value; S102: Based on the load level value, analyze the operating signal of each periodically operating facility within the cycle, filter the number of jumps and start-stop actions of the equipment within the cycle, combine the load level change and start-stop actions to form a relative disturbance frequency, and generate disturbance frequency parameters. S103: Based on the disturbance frequency parameters, compare the offset of the relative disturbance frequency with the range of stable operating state parameters of similar equipment, determine the fluctuation trend of the operating state of the periodically operating facility within the period, and establish the operating disturbance factor parameters.

[0007] As a further aspect of the present invention, the step of obtaining the risk comparison result specifically includes: S201: Call the operation disturbance factor parameters, detect the activation time and cumulative operation cycle in the facility operation record, combine the operation disturbance factor parameters and cycle duration, calculate the operation load intensity of each facility in the current cycle, and generate the operation load intensity value; S202: Based on the operating load intensity value, compare the operating load intensity value with the facility life benchmark parameter and perform difference mapping to obtain the life cycle remaining coefficient of each facility and generate the life cycle remaining analysis results; S203: Based on the cycle remaining analysis results, and based on the failure frequency density characteristics reflected in the cycle data of similar facilities before the anomaly, determine the clustering range of failure frequency in the lifespan interval, identify the lifespan segment corresponding to the target range as the reference risk segment, and perform interval matching between the lifespan remaining coefficient and the reference risk segment to establish a risk comparison result.

[0008] As a further aspect of the present invention, the process of determining the clustering range of fault occurrence frequency over the lifespan interval specifically comprises: By comparing the operational cycle position corresponding to each failure with the cycle interval between each operational cycle position in the periodic data recorded in the periodic data before the anomaly of similar facilities, the continuous interval segments formed by multiple operational cycle positions within the lifespan are detected. A fault concentration coefficient is established to characterize the degree of fault concentration by using the proportional relationship between the length of a continuous interval and the fault count within the continuous interval. By utilizing the difference between the periodic discrete parameter characterizing the range of normal operation cycle variation of similar facilities and the fault discrete parameter characterizing the range of fault occurrence fluctuation, a concentrated segment identification threshold is established. The correspondence between the fault concentration coefficient and the concentrated segment identification threshold is determined to form a cluster range number used to limit the fault cluster area within the lifespan interval.

[0009] As a further aspect of the present invention, the step of obtaining the task level adjustment result specifically includes: S301: Call the risk comparison results, receive the scheduling task request, obtain the facility identifier and spatial unit number, analyze the spatial distribution differences of the task distribution area in adjacent periods, identify the boundary expansion range of the task set, and generate the task boundary expansion interval. S302: Based on the task boundary expansion interval, pair the boundary expansion distance with the scheduling cycle span to form the advancement speed trend of the task's influence range, call the spatial expansion change reference interval in facility management, and generate advancement speed trend parameters; S303: Based on the propulsion speed trend parameters and referring to the spatial expansion change reference range, determine the change range of the propulsion speed trend, adjust the sorting level of the scheduling task in the scheduling list, and establish the task level adjustment result.

[0010] As a further aspect of the present invention, the step of obtaining the facility maintenance matching result specifically includes: S401: Call the task level adjustment results, analyze the task response actions recorded by multiple maintenance teams within the management cycle, extract the duration of the response phase of each maintenance team in multiple tasks, calculate the changing trend of multiple response phases within each maintenance team, and generate response behavior change parameters. S402: Based on the response behavior change parameters, a maintenance team response behavior curve is formed. The response behavior curve is compared with the current task scheduling rhythm parameters to determine the degree of deviation between rhythm features and obtain the response rhythm comparison coefficient. S403: Combining the response rhythm comparison coefficient with the road travel path between the team location and the task space unit, evaluate the adaptability of the task request to the response of multiple teams, construct a comprehensive selection index, and generate facility maintenance matching results.

[0011] As a further aspect of the present invention, the step of obtaining the regional redundancy quantization result specifically includes: S501: Call the facility maintenance matching results, analyze the spatial trajectory sequence recorded by the robot during the operation cycle, match the trajectory coordinates with the grid cells defined in the operation area, filter the operation time periods of multiple robots in the same grid cell, and generate the grid operation overlapping time period interval. S502: Based on the overlapping time interval of the grid operation, and combined with the status information of the robot's operating device in the overlapping section, determine the repeated coverage characteristics of the operation behavior in the grid cell to obtain the repeated coverage section of the operation. S503: Based on the overlapping coverage area of ​​the operation, compare the difference between the task load index and the operation frequency of the operation area, calculate the resource consumption degree of the repeated operation, and establish the regional redundancy quantification result.

[0012] Management systems for municipal public facilities include: The operation status analysis module calls the periodic operation facility monitoring dataset, extracts various equipment operation indicators, calculates the load level and identifies the number of start and stop actions, combines them to form a relative disturbance frequency, compares the deviation of the disturbance frequency with the equipment stable state, judges the fluctuation trend of the operation status, and generates operation disturbance factor parameters. The lifecycle management module calls the operational disturbance factor parameters, combines the activation time and the operational cycle, calculates the operational load intensity and maps it to the lifecycle benchmark to form the lifecycle remaining coefficient, matches the reference risk segment, and generates risk comparison results. The task scheduling optimization module uses the risk comparison results to receive scheduling task requests, analyze the differences in task distribution areas, calculate the task boundary expansion range, pair the boundary expansion distance with the cycle span, generate the progress speed trend of the task's influence range, adjust the task scheduling order, and generate task level adjustment results. The maintenance response adaptation module calls the task level adjustment results, analyzes the response actions of multiple maintenance teams, extracts the duration of the response phase, forms a team response behavior curve, compares it with the task rhythm parameters, evaluates the response adaptability, constructs a comprehensive dispatch index, and generates facility maintenance matching results. The resource redundancy optimization module calls the facility maintenance matching results, analyzes the trajectory sequence of the operation robot, identifies overlapping operation sections, judges the characteristics of repeated operation coverage by combining the device status information, calculates the loss of redundant resources, and generates regional redundancy quantification results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, facility status shifts are characterized by changes in operational disturbances, the correspondence between the remaining lifecycle coefficient and risk zones is used to enhance the advance risk identification, task prioritization is corrected by combining task progress speed trends to enhance the ability to distinguish task urgency, team response behavior characteristics are used to match scheduling rhythm to improve the fit of resource allocation, and trajectory overlap features are used in conjunction with operational status to measure the resource consumption level of collaborative operations, thereby achieving the ability to coordinate the entire process from status awareness to resource implementation. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0015] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0017] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0018] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0019] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0021] Please see Figure 1 This invention provides a management method for municipal public facilities, comprising the following steps: S1: Call the periodic operation facility monitoring dataset, extract various equipment operation indicators, calculate the load level and identify the number of start and stop actions, combine them to form a relative disturbance frequency, compare the deviation of the disturbance frequency with the equipment stable state, judge the fluctuation trend of the operating state, and generate operation disturbance factor parameters. S2: Call the running disturbance factor parameters, combine the activation time and running cycle, calculate the running load intensity and map it with the life benchmark difference to form the life cycle remaining coefficient, match the reference risk segment, and generate risk comparison results; S3: Utilize the risk comparison results to receive scheduling task requests, analyze the differences in task distribution areas, calculate the task boundary expansion range, pair the boundary expansion distance with the cycle span, generate the progress speed trend of the task's influence range, adjust the task scheduling order, and generate task level adjustment results. S4: Call the task level adjustment results, analyze the response actions of multiple maintenance teams, extract the duration of the response phase, form the team response behavior curve, compare it with the task rhythm parameters, evaluate the response adaptability, construct a comprehensive dispatch index, and generate facility maintenance matching results. S5: Call the facility maintenance matching results, analyze the trajectory sequence of the operation robot, identify overlapping operation sections, combine the device status information to judge the characteristics of repeated operation coverage, calculate the loss of redundant resources, and generate regional redundancy quantification results.

[0022] The operational disturbance factor parameters specifically include the periodic disturbance frequency value, load level jump value, and start / stop action frequency density. The risk comparison results include the remaining life cycle percentage, risk cluster interval number, and life cycle matching status. The task level adjustment results specifically refer to the spatial expansion rate level, task level ranking, and ranking adjustment source identifier. The facility maintenance matching results include the response rhythm adaptation coefficient, response team location path number, and comprehensive selection index value. The regional redundancy quantification results specifically include the duplicate operation grid number, resource waste magnitude index, and duplicate coverage task load offset.

[0023] Please see Figure 2 The specific steps for obtaining the perturbation factor parameters are as follows: S101: Obtain the monitoring dataset of periodically operating facilities, extract various equipment operation indicators, including current, power, and flow, calculate the load level of each periodically operating facility within the cycle, and generate the load level value; In the specific implementation of monitoring the operational status of submersible sewage pumps, the core power unit in urban drainage networks, the system first initiates the data acquisition process. The data acquisition terminal establishes a stable connection with the on-site PLC control cabinet via a pre-laid industrial Ethernet or 4G / 5G wireless transmission module. The system sets the data sampling cycle frequency to 1Hz, meaning it captures the equipment's operational status data once per second, ensuring data continuity and timeliness. Within the current monitoring period (set to 24 hours), the system continuously reads the multi-dimensional operational indicators of the sewage pump numbered P-A03. These indicators specifically include the instantaneous three-phase current values ​​acquired by Hall current sensors, the active power values ​​acquired by power transmitters, and the real-time flow data fed back by electromagnetic flowmeters installed on the outlet pipe. To transform these heterogeneous physical quantities into a unified measurement standard, the system executes load level calculation logic. First, the system obtains the rated electrical parameters of the equipment, including a rated power of 55 kW and a rated current of 110 amperes. Then, the system performs time integration and averaging on the acquired instantaneous power data to obtain the average operating power within the monitoring period. For example, during this monitoring period, the measured average operating power was 48.8 kW. The system then divided this measured average power by the rated power to obtain the power load factor, i.e., 48.8 divided by 55, with a result of approximately 0.887. Simultaneously, the system introduced a flow rate indicator as a correction term, calculating the ratio of the measured flow rate to the rated flow rate. To generate the final load level value, the system used a lookup table method to map continuous load rate values ​​to discrete level intervals. As shown in Table 1, the system presets a refined load level classification standard. The system compared the calculated comprehensive load factor of 0.887 with the intervals in the table, confirming that it falls within the "high load operation" interval, corresponding to a load level value of 8. This value not only records the power intensity of the equipment but also digitally eliminates the interference of instantaneous fluctuations, providing standardized quantitative data for assessing the overall stress state of the equipment during the current period. This data will directly serve as the basic input for subsequent disturbance analysis.

[0024] Table 1 Load Rating Mapping Table for Submersible Sewage Pumps

[0025] As shown in Table 1, the non-linear setting of the load level values ​​(such as jumping directly from 3 to 8) is intended to highlight the weight of the impact of high load on equipment lifespan.

[0026] S102: Based on the load level value, analyze the operating signal of each periodically operating facility within the cycle, screen the number of equipment jumps and the number of start-stop actions within the cycle, combine the load level changes and start-stop actions to form a relative disturbance frequency, and generate disturbance frequency parameters. By calling upon the load level numerical sequence and combining it with the original time-series monitoring data, the system deeply analyzes the operational stability of the equipment at a micro-timescale. The system aims to identify "pathological" operational characteristics where the average load is normal, but the process is extremely unstable. First, the system executes jump detection logic. The system sets a jump judgment threshold; when the load level value changes by more than three levels between two adjacent sampling time points (e.g., a sudden jump from level 3 to level 8), and this change is not triggered by a speed adjustment command issued by the operator, the system marks it as an "abnormal jump." Within the current cycle, by traversing the data sequence throughout the day, the system counted 18 abnormal jumps in the equipment, which usually indicates intermittent blockage at the inlet or severe fluctuations in the grid voltage. Second, the system counts the equipment's start-up and shutdown actions. The system scans the contactor status signals in the control loop, recording a complete engagement and disengagement process as one start-up and shutdown. Statistics show that the equipment frequently started and stopped 12 times within 24 hours. To comprehensively quantify these unstable factors, the system constructs a calculation logic for relative disturbance frequency. The system introduces two influence weighting coefficients: a jump weighting coefficient set to 0.6 and a start-stop weighting coefficient set to 0.4. These coefficients are determined based on the proportion of the impact of mechanical bearing wear and electrical shock on lifespan measured in the laboratory. The system multiplies the number of jumps (18) by the weighting coefficient 0.6, and adds the number of start-stops (12) multiplied by the weighting coefficient 0.4, to obtain a weighted disturbance value of 15.6. Finally, the system divides this weighted value by the actual operating hours of the equipment (e.g., 20 hours), i.e., 15.6 divided by 20, to calculate the relative disturbance frequency of 0.78. This disturbance frequency parameter of 0.78, as a dimensionless index, accurately describes the intensity of non-steady-state shocks suffered by the equipment per unit operating time; a higher value indicates more severe operating conditions.

[0027] S103: Based on the disturbance frequency parameters, compare the deviation of the relative disturbance frequency from the range of stable operating state parameters of similar equipment to determine the fluctuation trend of the operating state of the periodically operating facility within the period, and establish the operating disturbance factor parameters. After obtaining the disturbance frequency parameter of 0.78 for the current period, the system needs to determine whether this value is within a normal and acceptable range. To do this, the system retrieves baseline operating data for similar equipment (submersible sewage pumps of the same model and operating condition) stored in the facility management database. Through statistical analysis of a year's worth of historical healthy operating data, the system determines the disturbance frequency parameter range for this type of equipment under stable operating conditions to be as follows: This means that under normal operating conditions, the equipment's disturbance frequency should not exceed 0.5. The system compares the currently calculated 0.78 with the upper limit of the stable range, 0.5, to calculate its offset. Specifically, the difference between 0.78 and 0.5, i.e., 0.28, is calculated. This difference is then divided by the range's span (0.5 - 0.2 = 0.3) or directly by the upper limit. Here, the upper limit is used to reflect the excess multiple, i.e., 0.28 divided by 0.5, resulting in an offset ratio of 0.56. This indicates that the current disturbance level is 56% higher than the normal upper limit. To convert this physical offset into a correction factor for subsequent lifetime calculations, the system establishes an operating disturbance factor parameter. The system sets the base factor to 1 and adds the offset ratio as an increment, i.e., 1 plus 0.56, resulting in the operating disturbance factor parameter 1.56. The physical meaning of this parameter is that, given the current high disturbance state, the mechanical fatigue and electrical aging losses caused by one hour of equipment operation are equivalent to 1.56 hours of operation under stable conditions. The establishment of this parameter enables the conversion from "physical time" to "aging time," and can keenly capture the accelerating erosion effect of fluctuations in operating status on equipment lifespan.

[0028] Please see Figure 3 The specific steps for obtaining the risk comparison results are as follows: S201: Call the operation disturbance factor parameters, detect the activation time and cumulative operation cycle in the facility operation record, combine the operation disturbance factor parameters and cycle duration, calculate the operation load intensity of each facility in the current cycle, and generate the operation load intensity value; The system invokes a disturbance factor parameter of 1.56 to begin calculating the operating load intensity for the sewage pump. First, it accesses the equipment's full lifecycle archive, retrieving the equipment's activation date, which shows the pump officially began operation on January 1, 2023. Simultaneously, it reads the cumulative operating cycle data up to the end of the previous monitoring cycle, indicating a cumulative physical operating time of 3000 hours. Entering the current monitoring cycle, the system records a physical operating time of 24 hours. To accurately reflect the equipment's wear and tear under the current high-disturbance conditions, the system does not directly use 24 hours as the load data but instead uses the operating disturbance factor for weighted correction. The system performs a multiplication operation, multiplying the physical duration of 24 hours by the operating disturbance factor of 1.56, resulting in 37.44. This value of 37.44 represents the operating load intensity for the current cycle, expressed in "standard equivalent hours." This means that although only 24 hours have passed in this day, the equipment's lifespan has actually consumed 37.44 hours of its lifespan. The system adds this value to historical cumulative values ​​to ensure that the differences in operating conditions each day are accurately recorded. This step effectively solves the drawbacks of traditional maintenance based solely on calendar time or physical running time, prevents sudden failures caused by neglecting high-load operating conditions, and provides high-precision input data with operating condition correction attributes for subsequent remaining life prediction.

[0029] S202: Based on the operating load intensity value, compare the operating load intensity value with the facility life benchmark parameter and perform difference mapping to obtain the life cycle remaining coefficient of each facility and generate the life cycle remaining analysis results. Based on the numerical value and cumulative value of the operating load intensity, the system further assesses the current life cycle status of the equipment. First, the system retrieves the facility life baseline parameter for this model of submersible sewage pump. This parameter, provided by the equipment manufacturer, is the theoretical design life derived from MTBF (Mean Time Between Failures) testing, set at 50,000 standard equivalent hours. The system reads the cumulative total operating load intensity up to the current cycle, assuming that after accumulation, this value has reached 35,000 standard equivalent hours. To visually represent the remaining value of the equipment, the system performs a difference mapping calculation. Specifically, the system subtracts the cumulative operating load intensity of 35,000 from the facility life baseline parameter of 50,000, obtaining a remaining theoretical lifespan of 15,000 hours. Subsequently, the system divides this remaining amount by the baseline parameter of 50,000, i.e., 15,000 divided by 50,000, calculating a value of 0.3. This value is the life cycle remaining coefficient, indicating that the equipment currently has only 30% of its theoretical lifespan remaining. The system generates the cycle remaining analysis result based on this coefficient and marks it as "aging period". This result is not a simple countdown, but a dynamic assessment derived from the actual operating pressure of each past operating cycle. If the equipment frequently operates under high disturbance, its coefficient will decrease much faster than the calendar time, thus prompting managers to plan major overhauls or replacements in advance and avoid equipment failure at critical moments.

[0030] S203: Based on the cycle remaining analysis results, and based on the failure frequency density characteristics reflected in the cycle data of similar facilities before the anomaly, determine the clustering range of failure frequency in the lifespan interval, identify the lifespan segment corresponding to the target range as the reference risk segment, and perform interval matching between the lifespan remaining coefficient and the reference risk segment to establish risk comparison results. Based on a lifecycle remaining coefficient of 0.3, risk assessment was conducted using historical failure data of similar equipment. The system queried the failure knowledge base and extracted lifecycle location data of serious failures (such as bearing seizure and impeller breakage) of the same type of pump station within the past five years. The system used statistical methods to analyze the distribution characteristics of these failure frequencies across the lifecycle interval. The specific process is as follows: The system divided the lifecycle interval (0 to 1) into several sub-periods and counted the number of failure cases falling into each period. The analysis revealed that when the lifecycle remaining coefficient was 0.3, the risk of failure was significantly higher than that of other equipment. Within a specific interval, the frequency density of fault occurrences is significantly higher than in other intervals, exhibiting a distinct "fault peak cluster." The system calculates the interval between fault samples within this interval and adjacent intervals, finding that the average interval between fault occurrences within this interval is extremely short, consistent with clustering characteristics. Based on this, the system identifies... The system uses a reference risk zone and defines it as a "high-risk fatigue failure zone." Then, it performs interval matching between the current equipment's remaining lifespan coefficient of 0.3 and this reference risk zone. Since 0.3 precisely falls within this risk zone... Within the specified range, the system determines that the match is successful. This indicates that the device is currently in a high-risk period for failure; although it has not yet been shut down, the probability of a structural failure occurring internally is extremely high. Based on this, the system establishes a risk comparison result, marks the device's risk level as "red alert," and generates a detailed risk report, pointing out its high degree of consistency with historical high-risk failure patterns.

[0031] Please see Figure 4 The specific steps to obtain the task level adjustment results are as follows: S301: Invoke the risk comparison results, receive the scheduling task request, obtain the facility identifier and spatial unit number, analyze the spatial distribution differences of the task distribution area within adjacent periods, identify the boundary expansion range of the task set, and generate the task boundary expansion interval. After generating risk comparison results at the equipment level, the system proceeds to spatial analysis at the task scheduling level. The system receives a task scheduling request from the municipal command center, reflecting a problem with poor drainage in a certain area's stormwater pipe network. The system extracts the facility identifier (related to the pipe network surrounding pump station P-A03) and spatial unit number (e.g., grid G50) from the request. Given that equipment P-A03 has been identified as high-risk (result S203), the system needs to assess the spatial evolution trend of this drainage problem. The system retrieves the distribution data of reported tasks in the area and surrounding grids within two adjacent monitoring periods (period T-1 and the current period T). In period T-1, waterlogging repair points are mainly concentrated in the center of grid G50, with a coverage radius of approximately 100 meters; while in the current period T, the system identifies newly added repair points appearing in adjacent grids G51 and G52, with the furthest repair point reaching 300 meters from the center. The system compares the boundary ranges of the two periods by calculating the geometric boundaries of the task set (e.g., minimum circumcircle or convex hull). The calculation results show that the boundary of the task coverage area has expanded outward by 200 meters. This physical expansion phenomenon was captured by the system and generated the task boundary expansion range. It no longer treats the task as a static point, but as a dynamically spreading surface, intuitively revealing the reality that the water accumulation area caused by the drainage failure is rapidly getting out of control.

[0032] S302: Based on the task boundary expansion interval, pair the boundary expansion distance with the scheduling cycle span to form the advancement speed trend of the task's influence range, call the spatial expansion change reference interval in facility management, and generate advancement speed trend parameters. Based on the task boundary expansion range (200 meters beyond the boundary), the system performs rate analysis in conjunction with the scheduling cycle's time span. The time span for this scheduling is the interval between cycle T and cycle T-1, i.e., 24 hours. The system performs a pairwise operation, dividing the 200-meter boundary expansion distance by the 24-hour time span, calculating the projected speed trend of the task's impact area to be approximately 8.33 meters per hour. To assess the severity of this speed, the system invokes a preset spatial expansion change reference range in facility management. This reference range is set based on historical flooding data: speeds less than 2 meters per hour are considered "low-speed spread," 2 to 5 meters per hour are "medium-speed spread," and greater than 5 meters per hour are "high-speed burst." The system compares the calculated 8.33 meters per hour with the above range, confirming that it falls within the "high-speed burst" range. This means that the current drainage failure is worsening at an extremely rapid rate, and without immediate intervention, the flooded area will grow exponentially. Based on this, the system generates a propulsion speed trend parameter, which includes a specific rate value (8.33 m / h) and a corresponding level label (high-speed burst), providing a strong quantitative basis for subsequent adjustments to task priorities and ensuring that scheduling decisions are based on the dynamic rate of event development rather than static reserves.

[0033] S303: Based on the propulsion speed trend parameters and in comparison with the spatial expansion change reference range, determine the change range of the propulsion speed trend, adjust the sorting level of the scheduling task in the scheduling list, and establish the task level adjustment result; Based on the advance speed trend parameter (high-speed burst, 8.33 m / h), the current scheduling task is adjusted in priority. In the original scheduling list, this drainage and dredging task might only be marked as a normal "routine maintenance" level, ranked 10th in the scheduling queue. However, given such a high advance speed and the associated equipment (P-A03) being in a high-risk state, the system determines that the conventional ranking must be broken. The system refers to the preset priority adjustment rules: when the advance speed is in the "high-speed burst" range, the task level is directly upgraded to the highest level, "emergency rescue". The system modifies the priority field of this task in the database, increasing its weight score from the normal 10 points to the emergency 90 points, and forcibly promotes its ranking in the scheduling list to the top three. The system establishes the task level adjustment result, outputs a new task list, and includes the adjustment explanation: "Due to the water spread speed exceeding the 5 m / h threshold and the core pumping station facing failure risk, the emergency upgrade mechanism is triggered." This step ensures that limited maintenance resources are prioritized for those problem areas that are rapidly deteriorating and have the greatest potential for harm, realizing a shift from passive response to proactive risk prevention.

[0034] Please see Figure 5 The specific steps for obtaining facility maintenance matching results are as follows: S401: Call the task level adjustment results, analyze the task response actions recorded by multiple maintenance teams within the management cycle, extract the duration of the response phase for each maintenance team in multiple tasks, calculate the changing trend of multiple response phases within each maintenance team, and generate response behavior change parameters. After identifying an emergency task, the system needs to select the most suitable maintenance team from multiple options. The system analyzes the task response history of three maintenance teams (Team-A, Team-B, and Team-C) within the past month's management cycle. The system extracts the response phase duration for each team in the most recent five emergency tasks, i.e., the time from receiving the dispatch order to actually arriving at the site and checking in. Taking Team-A as an example, its response times for the most recent five tasks were 20 minutes, 22 minutes, 25 minutes, 28 minutes, and 30 minutes, respectively. The system performs linear regression analysis on this data, calculating the slope of change. The calculation reveals that Team-A's response time shows a significant upward trend (positive slope), indicating that the team may have recently experienced fatigue or poor robot condition, with response efficiency decreasing with each task. Conversely, Team-C's response time remains stable at around 18 minutes with minimal fluctuation. The system calculates the response behavior variation parameters for each team, including the average response time, the standard deviation of the response time, and the slope of the trend. These parameters constitute an accurate profile of the maintenance team's current "combat status," avoiding the one-sidedness of dispatching tasks based solely on the team's location, and ensuring that the selected team is not only at the right distance but also in good operational condition.

[0035] S402: Based on the response behavior change parameters, a maintenance team response behavior curve is formed. The response behavior curve is compared with the current task scheduling rhythm parameters to determine the degree of deviation between rhythm features and obtain the response rhythm comparison coefficient. Based on response behavior change parameters, a maintenance team response behavior curve is constructed and matched with the scheduling rhythm parameters of the current emergency task. Since this task is classified as "emergency rescue" (S303 result), the required scheduling rhythm parameters are "extremely fast and reliable," specifically quantified as: expected response time less than 25 minutes and stability deviation less than 10%. The system compares the behavior curve characteristics of each team with these indicators. Although Team-A's historical average time is acceptable, its recent deterioration has led to excessive stability deviation; Team-B is further away, with an average time exceeding 30 minutes; while Team-C perfectly meets the "extremely fast and reliable" requirements in all indicators. The system calculates the degree of deviation between rhythm characteristics and generates a response rhythm comparison coefficient. Team-C's coefficient is close to 0 (indicating a high degree of matching), while Team-A's coefficient is higher (indicating a low degree of matching). This coefficient reflects the fit between the team's actual capabilities and the urgent needs of the task. Through this step, the system is not just finding people, but finding the "right people," ensuring that the selected team can withstand the time pressure of the emergency task.

[0036] S403: By combining the response rhythm comparison coefficient with the road travel path between the team's location and the task space unit, assess the adaptability of the task request to the response of multiple teams, construct a comprehensive dispatch index, and generate facility maintenance matching results. Taking into account both the response rhythm comparison coefficient and geospatial factors, the final dispatch decision was constructed. The system obtained the current GPS coordinates of each team and calculated their road routes to the task spatial unit (G50 grid). Although Team-A was only 3 kilometers away from the task point in a straight line, the roads they took were congested during the morning rush hour, with an estimated travel time of 40 minutes; while Team-C, although 5 kilometers away, could reach the task directly via an urban expressway, with an estimated travel time of only 15 minutes. The system constructed a comprehensive dispatch index calculation formula, weighting and summing the response rhythm comparison coefficient and the estimated travel time. The time weight was set to 0.7, and the rhythm matching weight to 0.3. After calculation, Team-C's comprehensive score was significantly better than Team-A's. Based on this, the system generated a facility maintenance matching result, officially selecting Team-C as the execution team for the task, and sent a dispatch instruction to its terminal. This result was the optimal solution after comprehensively considering team status, task urgency, and real-time road conditions, maximizing the timeliness of the emergency response task.

[0037] Please see Figure 6 The specific steps for obtaining the regional redundancy quantization results are as follows: S501: Call the facility maintenance matching results, analyze the spatial trajectory sequence recorded by the operation robot during the operation cycle, match the trajectory coordinates with the grid cells defined in the operation area, filter the operation time periods of multiple robots in the same grid cell, and generate the grid operation overlapping time period interval. While Team-C receives orders and begins operations, the system continuously monitors the robot trajectories at the work site to prevent resource waste. The system analyzes the spatial trajectory sequences of all relevant robots (including Team-C's traffic control truck and sanitation vehicles within the area) during the work cycle (e.g., 9:00 AM to 11:00 AM). The system maps the GPS coordinates of these robots to a finely divided grid (10m x 10m) of the work area. The system filters out records of multiple robots appearing within the same grid cell. Analysis reveals that during the 15-minute period from 10:15 AM to 10:30 AM, Team-C's traffic control truck and another patrol vehicle belonging to Team-D simultaneously appeared in grid G50 and the surrounding G51. Through timestamp comparison, the system confirms a high degree of spatial and temporal overlap between the two vehicles. Based on this, the system generates overlapping time intervals for grid operations, specifically marked as follows: The generation of this interval data provides a precise spatiotemporal index for subsequent judgments on whether duplicate work exists, and is an important tool for refined management.

[0038] S502: Based on the overlapping time interval of grid operations, combined with the status information of the robot's operating device in the overlapping section, the repeated coverage characteristics of the operation behavior in the grid cell are determined to obtain the repeated coverage section of the operation. Based on the overlapping time intervals of grid operations, the system further analyzes the robot's operational behavior to distinguish between "passing by" and "repeated work." The system retrieves the status information of the operating devices uploaded by the robot's CAN bus. For the Team-C dredging vehicle, the system detects that its high-pressure flushing pump is in the "on" state, the water pressure is 15MPa, and the vehicle speed is less than 5km / h, confirming that it is performing dredging operations. For the Team-D patrol vehicle, the system detects that although it is also within the grid, its operating devices (such as sweeping discs and water spray guns) are all in the "off" state, and the vehicle speed is 30km / h, clearly indicating that it is just passing through the area to the next task point. Based on this information, the system determines that the operational behavior within the grid cell does not have substantial overlapping characteristics. However, if in another scenario, the system detects that the Team-D vehicle also has its water spray device turned on and is moving at a low speed, the system will determine that the operation is overlapping. In this embodiment, the system only locks Team-C's operations as valid coverage, eliminating interference from Team-D, thereby obtaining accurate operation repetition coverage segments (marked if there are repetitions, empty if there are none, or only marked single-vehicle operations).

[0039] S503: Based on the overlapping coverage areas of the operation, compare the difference between the task load index and the operation frequency of the operation area, calculate the resource consumption degree of the repeated operation, and establish the regional redundancy quantification result. Assuming that Team-C and another Team-E vehicle simultaneously activated their washing equipment on the same 100-meter stretch of road, the system calculates the resource waste resulting from this repetitive operation. The system first compares the task load indicators of the work area. This area is marked as "slightly flooded," requiring only a single high-pressure wash. However, the actual frequency of operation is 200% higher than the standard value, representing a 100% redundancy. Based on an energy consumption model, the system calculates the additional resources consumed during the overlapping operation (assumed to be 10 minutes). Team-E consumes 1.5 liters of fuel and 2 tons of washing water within 10 minutes. The system converts these physical quantities into economic indicators to calculate the degree of resource waste from the repetitive operation. For example, fuel costs of 12 yuan plus water costs of 10 yuan, plus vehicle depreciation, totaling approximately 30 yuan in waste. The system establishes a quantitative result for regional redundancy, which is not just a number but also a management signal. The system feeds the result back to the dispatch center, suggesting that a minimum working distance constraint between robots be added to future dispatch algorithms (such as not being able to dispatch two washing trucks within 30 minutes on the same road segment) to achieve refined control of municipal operation and maintenance costs.

[0040] Please see Figure 7 Management systems for municipal public facilities, including: The operation status analysis module calls the periodic operation facility monitoring dataset, extracts various equipment operation indicators, calculates the load level and identifies the number of start and stop actions, combines them to form a relative disturbance frequency, compares the deviation of the disturbance frequency with the equipment stable state, judges the fluctuation trend of the operation status, and generates operation disturbance factor parameters. The lifecycle management module calls the operational disturbance factor parameters, combines the activation time and the operational cycle, calculates the operational load intensity and maps it to the lifecycle benchmark to form the lifecycle remaining coefficient, matches it with the reference risk segment, and generates risk comparison results. The task scheduling optimization module uses the risk comparison results to receive scheduling task requests, analyze the differences in task distribution areas, calculate the task boundary expansion range, pair the boundary expansion distance with the cycle span, generate the progress speed trend of the task's influence range, adjust the task scheduling order, and generate task level adjustment results. The maintenance response adaptation module calls the task level adjustment results, analyzes the response actions of multiple maintenance teams, extracts the duration of the response phase, forms the team response behavior curve, compares it with the task rhythm parameters, evaluates the response adaptability, constructs comprehensive dispatch indicators, and generates facility maintenance matching results. The resource redundancy optimization module calls the facility maintenance matching results, analyzes the trajectory sequence of the operation robot, identifies overlapping operation sections, judges the characteristics of repeated operation coverage by combining the device status information, calculates the loss of redundant resources, and generates regional redundancy quantification results.

[0041] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A management method for municipal public facilities, characterized in that, Includes the following steps: S1: Call the periodic operation facility monitoring dataset, extract various equipment operation indicators, calculate the load level and identify the number of start and stop actions, combine them to form a relative disturbance frequency, compare the deviation of the disturbance frequency with the equipment stable state, judge the fluctuation trend of the operating state, and generate operation disturbance factor parameters. S2: Call the aforementioned operational disturbance factor parameters, combine the activation time and operational cycle, calculate the operational load intensity and perform a difference mapping with the lifetime benchmark to form the lifetime remaining coefficient, match the reference risk segment, and generate risk comparison results; S3: Using the risk comparison results, receive scheduling task requests, analyze the differences in task distribution areas, calculate the task boundary expansion range, pair the boundary expansion distance with the cycle span, generate the advancement speed trend of the task's influence range, adjust the task scheduling order, and generate task level adjustment results. S4: Call the task level adjustment results, analyze the response actions of multiple maintenance teams, extract the duration of the response phase, form a team response behavior curve, compare it with the task rhythm parameters, evaluate the response adaptability, construct a comprehensive selection index, and generate facility maintenance matching results.

2. The management method for municipal public facilities according to claim 1, characterized in that, The operational disturbance factor parameters specifically include the periodic disturbance frequency value, the load level jump value, and the start-stop action frequency density. The risk comparison results include the remaining percentage of the life cycle, the risk cluster interval number, and the life cycle matching status. The task level adjustment results specifically refer to the spatial expansion rate level, the task level ranking, and the ranking adjustment source identifier. The facility maintenance matching results include the response rhythm adaptation coefficient, the response team location path number, and the comprehensive selection index value.

3. The management method for municipal public facilities according to claim 1, characterized in that, The specific steps for obtaining the operational disturbance factor parameters are as follows: S101: Obtain the monitoring dataset of periodically operating facilities, extract various equipment operation indicators, including current, power, and flow, calculate the load level of each periodically operating facility within the cycle, and generate the load level value; S102: Based on the load level value, analyze the operating signal of each periodically operating facility within the cycle, filter the number of jumps and start-stop actions of the equipment within the cycle, combine the load level change and start-stop actions to form a relative disturbance frequency, and generate disturbance frequency parameters. S103: Based on the disturbance frequency parameters, compare the offset of the relative disturbance frequency with the range of stable operating state parameters of similar equipment, determine the fluctuation trend of the operating state of the periodically operating facility within the period, and establish the operating disturbance factor parameters.

4. The management method for municipal public facilities according to claim 3, characterized in that, The steps for obtaining the risk comparison results are as follows: S201: Call the operation disturbance factor parameters, detect the activation time and cumulative operation cycle in the facility operation record, combine the operation disturbance factor parameters and cycle duration, calculate the operation load intensity of each facility in the current cycle, and generate the operation load intensity value; S202: Based on the operating load intensity value, compare the operating load intensity value with the facility life benchmark parameter and perform difference mapping to obtain the life cycle remaining coefficient of each facility and generate the life cycle remaining analysis results; S203: Based on the cycle remaining analysis results, and based on the failure frequency density characteristics reflected in the cycle data of similar facilities before the anomaly, determine the clustering range of failure frequency in the lifespan interval, identify the lifespan segment corresponding to the target range as the reference risk segment, and perform interval matching between the lifespan remaining coefficient and the reference risk segment to establish a risk comparison result.

5. The management method for municipal public facilities according to claim 4, characterized in that, The process of determining the clustering range of fault occurrence frequency over the lifespan interval is as follows: By comparing the operational cycle position corresponding to each failure with the cycle interval between each operational cycle position in the periodic data recorded in the periodic data before the anomaly of similar facilities, the continuous interval segments formed by multiple operational cycle positions within the lifespan are detected. A fault concentration coefficient is established to characterize the degree of fault concentration by using the proportional relationship between the length of a continuous interval and the fault count within the continuous interval. By utilizing the difference between the periodic discrete parameter characterizing the range of normal operation cycle variation of similar facilities and the fault discrete parameter characterizing the range of fault occurrence fluctuation, a concentrated segment identification threshold is established. The correspondence between the fault concentration coefficient and the concentrated segment identification threshold is determined to form a cluster range number used to limit the fault cluster area within the lifespan interval.

6. The management method for municipal public facilities according to claim 4, characterized in that, The specific steps for obtaining the task level adjustment result are as follows: S301: Call the risk comparison results, receive the scheduling task request, obtain the facility identifier and spatial unit number, analyze the spatial distribution differences of the task distribution area in adjacent periods, identify the boundary expansion range of the task set, and generate the task boundary expansion interval. S302: Based on the task boundary expansion interval, pair the boundary expansion distance with the scheduling cycle span to form the advancement speed trend of the task's influence range, call the spatial expansion change reference interval in facility management, and generate advancement speed trend parameters; S303: Based on the propulsion speed trend parameters and referring to the spatial expansion change reference range, determine the change range of the propulsion speed trend, adjust the sorting level of the scheduling task in the scheduling list, and establish the task level adjustment result.

7. The management method for municipal public facilities according to claim 6, characterized in that, The specific steps for obtaining the facility maintenance matching results are as follows: S401: Call the task level adjustment results, analyze the task response actions recorded by multiple maintenance teams within the management cycle, extract the duration of the response phase of each maintenance team in multiple tasks, calculate the changing trend of multiple response phases within each maintenance team, and generate response behavior change parameters. S402: Based on the response behavior change parameters, a maintenance team response behavior curve is formed. The response behavior curve is compared with the current task scheduling rhythm parameters to determine the degree of deviation between rhythm features and obtain the response rhythm comparison coefficient. S403: Combining the response rhythm comparison coefficient with the road travel path between the team location and the task space unit, evaluate the adaptability of the task request to the response of multiple teams, construct a comprehensive selection index, and generate facility maintenance matching results.

8. The management method for municipal public facilities according to claim 1, characterized in that, The method further includes: S5: Call the facility maintenance matching results, analyze the trajectory sequence of the operation robot, identify overlapping operation sections, combine the device status information to determine the characteristics of repeated operation coverage, calculate the redundant resource loss, and generate regional redundancy quantification results. The specific results of the regional redundancy quantification are the duplicate job grid number, the resource waste magnitude index, and the duplicate coverage task load offset.

9. The management method for municipal public facilities according to claim 8, characterized in that, The specific steps for obtaining the region redundancy quantization result are as follows: S501: Call the facility maintenance matching results, analyze the spatial trajectory sequence recorded by the robot during the operation cycle, match the trajectory coordinates with the grid cells defined in the operation area, filter the operation time periods of multiple robots in the same grid cell, and generate the grid operation overlapping time period interval. S502: Based on the overlapping time interval of the grid operation, and combined with the status information of the robot's operating device in the overlapping section, determine the repeated coverage characteristics of the operation behavior in the grid cell to obtain the repeated coverage section of the operation. S503: Based on the overlapping coverage area of ​​the operation, compare the difference between the task load index and the operation frequency of the operation area, calculate the resource consumption degree of the repeated operation, and establish the regional redundancy quantification result.

10. A management system for municipal public facilities, characterized in that, The system is used to implement the management method for municipal public facilities as described in any one of claims 1-9, the system comprising: The operation status analysis module calls the periodic operation facility monitoring dataset, extracts various equipment operation indicators, calculates the load level and identifies the number of start and stop actions, combines them to form a relative disturbance frequency, compares the deviation of the disturbance frequency with the equipment stable state, judges the fluctuation trend of the operation status, and generates operation disturbance factor parameters. The lifecycle management module calls the operational disturbance factor parameters, combines the activation time and the operational cycle, calculates the operational load intensity and maps it to the lifecycle benchmark to form the lifecycle remaining coefficient, matches the reference risk segment, and generates risk comparison results. The task scheduling optimization module uses the risk comparison results to receive scheduling task requests, analyze the differences in task distribution areas, calculate the task boundary expansion range, pair the boundary expansion distance with the cycle span, generate the progress speed trend of the task's influence range, adjust the task scheduling order, and generate task level adjustment results. The maintenance response adaptation module calls the task level adjustment results, analyzes the response actions of multiple maintenance teams, extracts the duration of the response phase, forms a team response behavior curve, compares it with the task rhythm parameters, evaluates the response adaptability, constructs a comprehensive dispatch index, and generates facility maintenance matching results. The resource redundancy optimization module calls the facility maintenance matching results, analyzes the trajectory sequence of the operation robot, identifies overlapping operation sections, judges the characteristics of repeated operation coverage by combining the device status information, calculates the loss of redundant resources, and generates regional redundancy quantification results.

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