An urban rail transit-oriented environment control system operation and maintenance management method and system

Abstract

The application provides a kind of urban rail transit-oriented environmental control system operation management method and system, relating to the operation management technical field of urban rail transit environmental control system, and its technical scheme points are: by obtaining environmental control equipment operation data and station working condition data, when identifying that station working condition is in the transition period of load state change, a transition judgment benchmark gradually adjusted from the first normal value range to the second normal value range is automatically generated, and the equipment operation data is compared with the transition benchmark to diagnose the equipment state;In this way, the diagnosis benchmark is real-time synchronized with the working condition change, avoiding misjudgment caused by fixed benchmark or switching lag, thereby solving the diagnosis distortion problem in the transition period, with the advantages of eliminating diagnosis distortion in the working condition switching transition period, improving the accuracy and timeliness of equipment state diagnosis.

Description

Technical Field

[0001] This application relates to the field of operation and maintenance management technology for urban rail transit environmental control systems, and more specifically, to a method and system for operation and maintenance management of environmental control systems for urban rail transit. Background Technology

[0002] The operation and maintenance management of environmental control systems in urban rail transit is typically applied to ventilation, air conditioning, and smoke extraction in underground stations and sections. In existing operation and maintenance schemes, the system collects environmental parameters such as temperature, humidity, and carbon dioxide concentration, as well as operating current, voltage, and speed data of equipment such as fans, chillers, and pumps, using sensors deployed within the station. The system pre-sets fixed numerical ranges as a benchmark for judging whether equipment is functioning correctly. During off-peak hours, due to lower and relatively stable passenger flow and minimal changes in station heat load, the operation and maintenance system directly compares the collected real-time equipment operating data with this fixed benchmark. When the data exceeds the preset range, the system determines that the equipment is malfunctioning and generates a maintenance work order. This method of comparison based on fixed numerical ranges can maintain basic monitoring and maintenance of environmental control equipment under conditions of stable passenger flow and minimal changes in environmental parameters.

[0003] However, when rail transit stations enter peak hours or experience frequent train arrivals and departures, passenger flow increases dramatically in a short period. Heat generated by human body heat dissipation and train braking causes a rapid shift in the heat load of underground spaces. At this time, fans and chillers need to immediately and significantly increase their operating power to respond to the drastic cooling and ventilation demands. The fixed baselines originally set for off-peak periods become inapplicable. Existing maintenance systems typically trigger baseline adjustments based on detected surges in passenger flow, retrieving higher threshold values ​​from the database to replace the original baseline. However, from the external signal triggering to the new baseline finally taking effect and being used for diagnostics, steps such as data verification, threshold lookup, and system replacement are required, inevitably resulting in a time gap of tens of seconds. During this gap, the system continues to use the outdated old baseline to anomaly detection of real-time collected equipment transient operating data. For example, when a chiller unit rapidly increases its operating current from 60 amps to 120 amps to cope with a large passenger flow, the system, having not yet completed the peak-hour benchmark replacement, still uses the off-peak upper limit of 80 amps as the judgment basis. This misjudges the normal load increase as an overload fault, generating a false alarm and sending a maintenance work order, severely disrupting normal operation and maintenance. Conversely, when the morning and evening peak hours end and passenger flow rapidly dissipates, if the system fails to promptly revert the judgment benchmark from the peak-hour standard to the off-peak state, the operating data of equipment that continues to operate at high load due to control failure may fall within the excessively high peak-hour benchmark range. This causes the system to mistakenly believe that the equipment is operating normally, thus masking the actual equipment malfunction and missing the opportunity for timely maintenance intervention.

[0004] The root cause of the above problems lies in the fact that the anomaly diagnosis conclusions of existing operation and maintenance systems directly rely on the numerical comparison results between real-time equipment operating data and currently effective judgment benchmarks. However, during the transition period of operating condition switching, the replacement of judgment benchmarks lags significantly behind the changes in operating conditions, causing the diagnostic basis to become invalid. When equipment makes normal adjustments to adapt to environmental changes, the system makes incorrect diagnostic judgments due to the disconnect from the benchmarks. Therefore, how to eliminate diagnostic distortion during the transition period of operating condition switching is a key technical problem currently facing the operation and maintenance management of environmental control systems.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for the operation and maintenance management of environmental control systems for urban rail transit, which has the advantages of eliminating diagnostic distortion during the transition period of operating conditions and improving the accuracy and timeliness of equipment status diagnosis.

[0007] This application provides a method for the operation and maintenance management of environmental control systems for urban rail transit, and the technical solution is as follows: include: Acquire equipment operation data of environmental control equipment and station operating condition data; When the station's operating conditions are identified as being in a transition period from the first load state to the second load state based on the station's operating condition data, a transition judgment benchmark is generated. The normal range of the equipment operating parameters corresponding to the transition judgment benchmark is adjusted from the first normal range corresponding to the first load state to the second normal range corresponding to the second load state. The equipment operating data is compared with the transition judgment benchmark to obtain the equipment status diagnosis results during the transition period.

[0008] The above scheme can adjust the normal range of equipment operating parameters in real time according to the transition period of station operating conditions, so that the diagnostic benchmark and the change of operating conditions are synchronized, avoiding false alarms or missed alarms caused by the lag in benchmark switching, and improving the accuracy and timeliness of equipment status diagnosis during the transition period.

[0009] Furthermore, this application also proposes that, when identifying a transition period from a first load state to a second load state based on station operating condition data, the method includes: Acquire at least one of the following: passenger flow change data, environmental parameter change data, equipment control command change data, and equipment operation feedback change data; Based on at least one of the acquired passenger flow change data, environmental parameter change data, equipment control command change data, and equipment operation feedback change data, calculate the operating condition change index used to characterize the trend of the station's operating conditions changing towards the second load state. When the indicators of operating condition changes all indicate the same direction of change within a continuous time slice set, it is identified that the station's operating condition is in a transition period.

[0010] The above solution improves the accuracy and reliability of transition period identification by comprehensively judging the trend of working condition changes through multi-dimensional data.

[0011] Furthermore, this application also proposes generating transition judgment criteria, including: Obtain the first normal value range of the equipment operating parameters corresponding to the first load state, and the second normal value range of the equipment operating parameters corresponding to the second load state; Acquire information on the trend of operating conditions, changes in control commands for environmental control equipment, preset allowable transition durations, and historical transient behavior information of environmental control equipment in the direction of change; Based on the information on the trend of changes in operating conditions, the information on changes in control commands, and the allowable transition time, determine the adjustment advancement parameters for adjusting from the first normal value range to the second normal value range; Based on the control command change information and historical transient behavior information, determine the transient compensation parameters; By adjusting the propulsion parameters and transient compensation parameters, the first normal value range and the second normal value range are calculated to obtain the normal value range of the equipment operating parameters at the current moment, which serves as a transition judgment benchmark.

[0012] By comprehensively considering the trend of operating condition changes, changes in control commands, allowable transition duration, and historical transient behavior, the generated transition judgment benchmark is made to better match the equipment operating characteristics during the actual transition process, thereby improving the adaptability of the diagnostic benchmark.

[0013] Furthermore, this application also proposes that generating transition judgment criteria further includes: Obtain operational feedback information from environmental control equipment; When there is an inconsistency between the direction of change indicated by the trend information of the operating condition, the direction of change indicated by the change information of the control command, and the direction of change indicated by the change information of the operation feedback, the adjustment of the propulsion parameters is restricted to be updated in the direction that makes the transition judgment benchmark approach the second normal value range. When the direction of change indicated by the operating condition change trend information reverses during the process of generating transition judgment benchmarks multiple times, the propulsion parameters are frozen and adjusted to keep the transition judgment benchmark unchanged.

[0014] The above scheme, through consistency judgment and reversal protection, prevents improper adjustments from being made to the transition judgment benchmark when the operating conditions are unclear or fluctuate, thereby improving the stability and reliability of benchmark generation.

[0015] Furthermore, this application also proposes comparing equipment operating data with transition judgment benchmarks to obtain equipment status diagnosis results during the transition period, including: Compare the equipment operating data with the normal range of equipment operating parameters corresponding to the transition judgment benchmark; Acquire information on the direction of change of equipment operating data, the direction of change of station operating conditions, and the direction of change of control commands for environmental control equipment; When the equipment operating data falls within the normal range, the direction of change of the equipment operating data is consistent with the direction of change of the station operating conditions and the direction of change of the control commands, and the current duration of the equipment operating data exceeding the first normal range does not exceed the preset allowable transition duration, the equipment status is determined to be the operating condition following state, and a first diagnostic record is generated. The first diagnostic record does not trigger an equipment fault alarm. Otherwise, the device is determined to be in an abnormal state, and a second diagnostic record is generated. The second diagnostic record is used to trigger device fault handling.

[0016] The above solution comprehensively judges the equipment's operating status from multiple dimensions, accurately distinguishes between normal operating conditions and abnormal conditions, reduces false alarms, and improves diagnostic accuracy.

[0017] Furthermore, this application also proposes that, when the transition period is the transition period during which the station's operating conditions change from the second load state to the first load state, the conditions for determining the equipment's state as the operating condition following state also include: The rate of decline in equipment operating data reaches the preset minimum decline rate, and within the preset allowable decline hysteresis time, the equipment operating data shows a decline that meets the preset decline amplitude requirements.

[0018] The above scheme, taking into account the characteristics of the descent transition period, adds judgments on descent rate and hysteresis time to further accurately identify the status of the equipment.

[0019] Furthermore, this application also proposes that the method further includes: After the transition period, the station's operating conditions were determined to have stabilized at the second load level based on the station's operating data. When the equipment operating data meets the second normal value range within a continuous stable time slice, the transition period ends, and the second normal value range is used as the judgment criterion for the equipment under the second load state.

[0020] The above approach allows for timely switching to a fixed benchmark after the operating conditions stabilize, ensuring the accuracy of subsequent diagnostics.

[0021] Furthermore, this application also proposes that the method further includes: Acquire records that were identified as being in a normal following state during the historical transition period, as well as historical equipment operation data of the environmental control equipment that served as the source of these records during the historical transition period; Based on the acquired historical equipment operation data, determine the allowable range of transient fluctuations and the threshold of normal transition duration exhibited by the environmental control equipment during the historical transition period; By utilizing the determined allowable range of transient fluctuations and the threshold of normal transition duration, the historical transient behavior information and allowable transition duration used in the subsequent generation of transition judgment benchmarks are corrected.

[0022] The above approach continuously optimizes the generation parameters of the transition judgment benchmark by self-learning and correcting historical data, thereby improving the system's adaptive capability.

[0023] Furthermore, this application also proposes that the station includes multiple areas, and the station operating condition data includes regional operating condition data corresponding to each of the multiple areas; When identifying a transition period from a first load state to a second load state based on station operating condition data, including: For each region, based on the region's operating condition data, identify whether the region is in a transition period; When the region is identified as being in a transition period, the second load state is gradually locked from a predetermined load state level sequence based on the continued trend of the region's operating conditions, and the initially locked second load state is the load state adjacent to the first load state in the sequence.

[0024] The above scheme enables independent diagnosis of stations in multiple regions and gradual adaptation to load conditions, thereby improving the accuracy of diagnosis under complex operating conditions in multiple regions.

[0025] This application also provides an operation and maintenance management system for environmental control systems in urban rail transit, the technical solution of which is as follows: To perform any of the above methods, including: The data acquisition module is used to acquire equipment operation data of the environmental control equipment and station operating data; The benchmark generation module is used to generate a transition judgment benchmark when the station's operating conditions are identified as being in a transition period from a first load state to a second load state based on the station's operating condition data. The normal value range of the equipment operating parameters corresponding to the transition judgment benchmark is adjusted from the first normal value range corresponding to the first load state to the second normal value range corresponding to the second load state. The diagnostic module is used to compare the equipment's operating data with the transition judgment benchmark to obtain the equipment's status diagnostic results during the transition period.

[0026] The above solution, by setting up a data acquisition module, a baseline generation module, and a diagnostic module, can automatically perform dynamic baseline generation and equipment status diagnosis during the transition period, thereby improving the intelligence level of environmental control system operation and maintenance management.

[0027] As can be seen from the above, the environmental control system operation and maintenance management method and system provided in this application for urban rail transit acquires environmental control equipment operation data and station operating condition data. When the station operating condition is identified as being in a transition period of load change, a transition judgment benchmark is automatically generated, which is gradually adjusted from a first normal value range to a second normal value range. The equipment operation data is then compared with this transition benchmark to diagnose the equipment status. In this way, the diagnostic benchmark is synchronized with the operating condition change in real time, avoiding misjudgments caused by fixed benchmarks or switching lags. This solves the problem of diagnostic distortion during the transition period and has the advantages of eliminating diagnostic distortion during the operating condition switching transition period and improving the accuracy and timeliness of equipment status diagnosis. Attached Figure Description

[0028] Figure 1 A schematic diagram of the method flow provided in this application.

[0029] Figure 2 A schematic diagram of the overall process provided for this application. Detailed Implementation

[0030] The following description, in conjunction with the technical solution of this application, provides a clearer and more complete explanation of the relevant content. It should be noted that the embodiments described herein are only a part of the implementation methods of this application, and not all of them. Other implementation methods obtained by those skilled in the art based on the embodiments of this application without creative effort should also fall within the protection scope of this application. Furthermore, in the description of this application, the terms "first," "second," etc., are mainly for distinction and should not be construed as indicating relative importance.

[0031] Reference Figure 1 This application proposes a method for the operation and maintenance management of environmental control systems for urban rail transit, including: S1. Obtain equipment operation data of environmental control equipment and station operating data; S2. When the station operating conditions are identified as being in a transition period from a first load state to a second load state based on the station operating condition data, a transition judgment benchmark is generated. The normal value range of the equipment operating parameters corresponding to the transition judgment benchmark is adjusted from the first normal value range corresponding to the first load state to the second normal value range corresponding to the second load state. S3. Compare the device operation data with the transition judgment benchmark to obtain the device status diagnosis result during the transition period.

[0032] In the existing operation and maintenance management process of urban rail transit environmental control systems, when the heat load of stations changes drastically during morning and evening rush hours or when trains enter and leave stations in concentrated bursts, environmental control equipment such as fans and chillers will immediately and significantly increase their operating power to respond to cooling demands. At this time, the fixed current or temperature thresholds originally used in the operation and maintenance system backend for off-peak periods have not yet been replaced with peak-period thresholds. However, the diagnostic process must continue to use this outdated benchmark to compare the transient high-load data generated by the equipment due to normal response to load changes. This disconnect between the benchmark and the operating conditions during the transition period causes the system to directly misjudge normal load increases by the equipment as overload faults, resulting in a large number of invalid alarms being pushed to the dispatch terminal. Furthermore, during the load reduction transition period after the peak ends, because the benchmark is not narrowed in time, the data of equipment that continues to operate at high loads due to control failures will fall into the overly wide range of the old benchmark, thus masking the true anomalies. One of the core problems that this method aims to solve is to correct the diagnostic distortion caused by the delay in benchmark replacement during the operating condition switching transition period.

[0033] In step S1, the acquired equipment operation data of the environmental control equipment can include real-time feedback parameters generated during the operation of equipment such as ventilation fans, combined air conditioning units, chillers, chilled water pumps, cooling pumps, smoke exhaust fans, makeup air fans, and related valve actuators within the station and its sections. These parameters include operating current, voltage, speed, frequency, air volume, air supply temperature, return air temperature, valve position, differential pressure, and start / stop status. Station operating condition data refers to multi-source information reflecting the station's heat load level and trends. This includes passenger flow data from entrance gates, train arrival and departure density data, environmental parameter changes such as temperature, humidity, and carbon dioxide concentration collected by environmental sensors in the station hall and platform, outdoor meteorological data, and control command changes and equipment operation feedback data obtained from equipment controllers. This data can be collected and synchronized at fixed sampling periods on the station-level operation and maintenance server or central operation and maintenance platform. For example, it can be merged and organized in time slices of several seconds to tens of seconds to align data from different sources in the time dimension, providing a consistent benchmark for subsequent operating condition assessments.

[0034] Next, in step S2, when the station's operating conditions are identified as being in a transition period from a first load state to a second load state based on the station's operating condition data, a transition judgment benchmark specifically for this transition phase is generated for the current moment. Here, the first load state and the second load state correspond to two different stable levels of the station's heat load, such as off-peak conditions, general passenger flow conditions, peak conditions, or low-load conditions at night, off-peak conditions on weekdays, and peak conditions on weekends, depending on the actual operating mode of the line. The so-called transition period refers to the continuous change process during which the thermal environment of the station area has not yet reached a new equilibrium between the two stable load states, and the environmental control equipment is still adjusting its operating parameters in response to external demands and internal control commands.

[0035] Identifying whether a station's operating conditions have entered a transition period can be based on one or more trends in the station's operating condition data. For example, when passenger flow shows a continuous increase or decrease over several consecutive sampling time slots, or when train arrival and departure frequencies change significantly, or when environmental parameters such as temperature, humidity, and carbon dioxide concentration within the station begin to deviate from the normal fluctuation range of the previous stable operating condition and show continuous deviation, or when the equipment controller has issued control commands to increase or decrease airflow, cooling capacity, or water flow, or when the changes in operating parameters fed back by the equipment itself exceed the normal fluctuation range allowed by the original stable operating condition, it can be considered that the station's operating conditions are no longer in the original first load state, but have entered a transition period towards the second load state. In one implementation, these multi-source changes can be comprehensively calculated to obtain an index of the degree of change in operating conditions. However, this solution is not limited to a specific algorithm. The key is to utilize early signals that have not yet been fully confirmed but have shown a clear trend, and to switch the judgment criteria to the transition state in advance, so that the timing of the benchmark replacement is significantly advanced from the original ex-post confirmation.

[0036] Once the transition period is identified, a transition judgment benchmark is generated. The normal range of equipment operating parameters corresponding to this benchmark is not directly adopted from the first normal range under the first load condition, nor directly from the second normal range under the second load condition, but rather a dynamically adjusted intermediate range between the two. This transition judgment benchmark can have different value boundaries at different points in the transition period. It evolves gradually from the first normal range to the second normal range, depending on the direction and magnitude of the change in operating conditions, the duration of the transition, and the normal response range exhibited by the equipment under similar load changes. For example, for a transition period in the direction of increasing load, the upper limit of the normal range defined by the transition judgment benchmark will gradually widen from the upper limit of the first normal range towards the upper limit of the second normal range to cover phenomena such as short-term current surges or rapid speed increases that may occur during load increases. Simultaneously, the lower limit of the normal range may also be adjusted appropriately to maintain a reasonable coverage of the actual operating range of the equipment. During the transition period of load reduction, this range will gradually narrow from the upper limit of the second normal value range towards the first normal value range, so as to promptly identify equipment that has failed to reduce load synchronously with the operating conditions during the load shedding process. This continuous adjustment method ensures that the judgment benchmark compared with the equipment operating data at any sampling time always matches the current stage of operating condition evolution, avoiding diagnostic gaps where the old benchmark has significantly deviated from the actual operating conditions while the new benchmark has not yet taken effect. The specific method for generating the transition judgment benchmark can be based on pre-saved transition benchmark generation rules, combined with real-time acquired operating condition change trend information, control command change information, and historical transient behavior information of the equipment. These implementation details will be further elaborated later.

[0037] Finally, in step S3, the equipment operating data obtained in step S1 is compared with the transition judgment benchmark generated in step S2 at the current time to obtain the equipment's status diagnosis result during the transition period. This comparison process is essentially no different in operation from directly comparing the operating data with a fixed judgment benchmark under stable operating conditions; it still involves determining whether the current operating data falls within the normal value range and whether it meets constraints such as the direction and duration of change.

[0038] The essential difference lies in the fact that the benchmark used for comparison is a transitional benchmark that is dynamically adjusted according to the operating conditions. Therefore, when equipment generates normal high-load operating data in response to large passenger flows or dense train arrivals and departures, such as a rapid increase in the operating current of a chiller unit during the load increase transition period, as long as the current value falls within the transitional judgment benchmark range that has been relaxed according to the load increase direction, and is consistent with the direction of change of the equipment's control command and the direction of change of operating conditions, it can be judged as normal operating condition following behavior, not as an equipment overload fault, and will not trigger a false alarm pushed to the dispatch terminal. For equipment that fails to reduce load as expected during the load reduction transition period due to control stagnation or local faults, although its operating current may not have exceeded the upper limit of the original peak stability benchmark, as the transitional judgment benchmark gradually narrows, the current value will begin to exceed the ever-tightening upper limit of the normal range, or its rate of decline may not meet the minimum decline requirement preset for the equipment in that direction, thus being promptly identified by the system as a real anomaly, avoiding missed judgments due to an excessively high benchmark.

[0039] Through the above steps, this method establishes a continuously changing judgment benchmark that matches the current operating condition before each diagnostic comparison. This ensures that the key logical relationship at the diagnostic entry point—the comparison between the real-time operating data of the equipment and the currently effective judgment benchmark—remains valid. During the transition period between operating conditions, transient operating data is no longer forced to match the lagging old benchmark, but instead matches an intermediate benchmark specifically generated for the transition period. This eliminates false alarms caused by upstream processing delays in benchmark replacement and avoids the real equipment hazards masked by the delayed downstream transmission of benchmarks after peak periods. This processing step occurs before the anomaly diagnosis comparison action is established. Its correction target is how the judgment benchmark itself should be determined and when it should take effect during the transition phase. It does not involve downstream steps such as subsequent maintenance work order dispatch, manual on-site confirmation, or scheduling strategy changes. Therefore, it can restore diagnostic conditions from the starting point of the relationship break without changing the existing operation and maintenance organizational process.

[0040] In existing operation and maintenance management processes, when passenger flow changes abruptly or heat load fluctuates dramatically, it is usually necessary to wait for the data on the surge in passenger flow at the entrance gates and complete the confirmation of the operating conditions before replacing the judgment benchmark. This process often involves a time gap. During this period, the diagnostic process is still in a steady-state monitoring state, and continuing to use the judgment benchmark matched with the previous load condition to diagnose abnormalities in equipment operation data can easily lead to delays in benchmark replacement. To address this, this application proposes a method for identifying a transition period from a first load state to a second load state based on station operating condition data. This includes: acquiring at least one of passenger flow change data, environmental parameter change data, equipment control command change data, and equipment operation feedback change data; calculating an operating condition change index to characterize the trend of the station operating conditions changing towards the second load state based on the acquired passenger flow change data, environmental parameter change data, equipment control command change data, and equipment operation feedback change data; and identifying a transition period when the operating condition change index indicates the same direction of change within a continuous time slice set.

[0041] Specifically, to minimize the gap in judgment benchmark replacement caused by waiting for operational condition confirmation, multiple data sources, including turnstiles, environmental sensors, and equipment controllers, are collected in each sampling period or time slice. These data can include passenger flow changes, environmental parameter changes, equipment control command changes, and equipment operation feedback changes. In practical applications, at least one of these data sources can be selected for subsequent calculations based on project requirements. Multiple data sources are introduced because single data sources often suffer from lag or localized distortion. For example, relying solely on turnstile passenger flow data may not promptly reflect the accumulation of localized heat loads on the platform, while integrating environmental parameters and equipment control commands can detect signs of operational condition changes in advance.

[0042] After obtaining the above data, a condition change index is calculated to characterize the trend of station operating conditions changing towards the second load state. As an optional implementation, various change data can first be normalized to obtain normalized values ​​for passenger flow changes, train arrival / departure density changes, environmental changes, and equipment control command changes. Environmental changes can be obtained by combining the offsets and rates of change of temperature, humidity, and carbon dioxide concentration relative to the previous stable operating condition. Subsequently, weights can be assigned to each of the above normalized values, and a weighted sum can be performed to obtain the condition change index for the current time slice. For example, the condition change index can be expressed as the sum of the products of each normalized value and its corresponding weight. When this index is greater than zero, it can characterize, to some extent, a tendency for the load to increase; when the index is less than zero, it can characterize a tendency for the load to decrease. By integrating multi-source information to calculate the condition change index, the changing trends of heat load and ventilation demand within the station can be reflected, avoiding misjudgments caused by fluctuations from a single data source.

[0043] It is worth noting that, to avoid frequent state switching triggered by data jumps in a single time slice, a directional consistency analysis is performed on the operational condition change indicators within several recent consecutive time slices. This set of consecutive time slices can be understood as a preset sliding judgment window, the number of which can be configured according to the actual response characteristics of the operational conditions. When the operational condition change indicators all indicate the same direction of change within this set of consecutive time slices, the station's operational condition can be identified as being in a transition period. Furthermore, as a supplementary triggering condition, even if passenger flow signals are not yet fully confirmed, as long as the equipment control command has issued a clear load increase or decrease action, and the equipment operation feedback begins to show a change in the same direction, the transition period can also be identified in advance. The initial intention of this design is that the actions of the equipment controller often precede changes in environmental parameters; using the same-direction change between control commands and equipment feedback as an auxiliary judgment basis can further advance the identification of the transition period.

[0044] Through the aforementioned early identification mechanism, the diagnostic process can enter the transition judgment state at an early stage, without waiting for the peak or off-peak confirmation process to end. This significantly advances the activation of the transition benchmark, cutting off the distorted path of forced hard matching of transient operating data with lagging judgment benchmarks at the source, and providing a time basis for subsequent elimination of false alarms and missed alarms. After identifying the transition period, the previous stable operating condition and the current target stable operating condition are locked, thereby triggering the subsequent transition benchmark generation steps.

[0045] During the transition period of operating condition switching, if the fixed normal value range bound to the previous load state is continued to be used, the real-time operating data of the equipment may exceed the old range due to the equipment responding to environmental changes by operating at high or low loads, leading to a large number of false alarms in the diagnostic logic. If the system directly jumps to the new normal value range bound to the target load state, the benchmark may be excessively widened or tightened instantaneously, causing abnormal equipment that has not yet fully responded to be incorrectly included in the new range and thus unidentifiable. Simply performing linear interpolation between the two is also insufficient to solve the problem of misjudgment caused by the inherent transient behavior of the equipment in the early stage of the transition. To address this, this application proposes a scheme to dynamically generate transition judgment benchmarks during the transition period.

[0046] Furthermore, in some preferred methods, generating a transition judgment benchmark includes: obtaining a first normal value range of equipment operating parameters corresponding to a first load state and a second normal value range of equipment operating parameters corresponding to a second load state; obtaining operating condition change trend information, control command change information for the environmental control equipment, a preset allowable transition duration, and historical transient behavior information of the environmental control equipment in the direction of change; determining adjustment advancement parameters for adjusting from the first normal value range to the second normal value range based on the operating condition change trend information, control command change information, and allowable transition duration; determining transient compensation parameters based on the control command change information and historical transient behavior information; and calculating the first normal value range and the second normal value range using the adjustment advancement parameters and transient compensation parameters to obtain the normal value range of equipment operating parameters at the current moment, which serves as the transition judgment benchmark.

[0047] Obtain the first normal value range and the second normal value range. These two ranges can serve as the starting point and the end point of the benchmark adjustment. The first normal value range and the second normal value range can be pre-stored in the stable operating condition benchmark table. This table can be initialized based on the equipment's design values ​​and, to a certain extent, corrected using health data from the normal operating period.

[0048] Acquire information on operating condition change trends, control command changes, allowable transition durations, and historical transient behavior. Operating condition change trends reflect the continuity and direction of load changes. Control command changes reflect the intensity of control system actions, such as the magnitude of changes in fan frequency commands or valve opening commands. Allowable transition durations constrain the maximum normal operating time of the equipment. Historical transient behavior information may include the normal deviation range or optimal transient duration exhibited by the equipment in similar past transitions, such as the normal current surge during load increase or the normal short lag time in speed during load decrease.

[0049] Determining the adjustment parameters involves considering the direction and continuity of changes reflected in the operating condition trend information, the intensity of actions reflected in the control command changes, and the relationship between the current transition time and the allowable transition duration. These three dimensions collectively determine the progress of the benchmark towards the second normal value range. For example, the operating condition continuity and control action intensity can be normalized to a zero-to-one range and weighted together with the ratio of the transition duration to the total allowable duration to obtain the adjustment parameters. As the time slice progresses, new data can be used to redetermine the adjustment parameters, allowing the benchmark to continuously adjust with the transition process.

[0050] Transient compensation parameters are determined to temporarily add tolerance to a baseline, covering the normal transient behavior of the equipment. These parameters can be determined based on the change in control commands and historical transient behavior information of the equipment in that direction. For example, transient compensation parameters can be obtained by combining empirical coefficients based on the amplitude of control command changes and transient overshoots or hysteresis extracted from historical healthy operating data. This allows the baseline to reasonably accommodate normal overshoots or short-term hysteresis that occur during the initial stages of load increase or decrease.

[0051] By using adjusted propulsion parameters and transient compensation parameters for calculation, a transient compensation parameter can be superimposed on an intermediate benchmark obtained by shifting the adjusted propulsion parameters from the first normal value range to the second normal value range, without compensation. For example, a transient compensation parameter is superimposed positively on the upper limit and negatively on the lower limit to obtain the normal value range of the equipment operating parameters at the current moment, i.e., the transition judgment benchmark. In the load increase direction, the upper limit will gradually widen from the previous stable upper limit to the target stable upper limit; in the load decrease direction, the range will naturally shift towards a narrower direction. To prevent jitter caused by an excessively narrow benchmark in the later stage of the transition, a minimum bandwidth can be set. If the calculated upper and lower limit distance is less than this minimum bandwidth, it will be expanded according to the minimum bandwidth.

[0052] Assuming the off-peak stable reference current range is 50 to 80 amps, and the peak stable reference is 95 to 130 amps, a load increase transition occurs during the morning peak. At this time, the upper limit of the chiller unit current is no longer fixed at 80 amps, but is relaxed towards 130 amps based on the current adjustment parameters and transient compensation parameters. If the adjustment parameter is currently 0.4, and transient compensation temporarily increases the upper limit by 5 amps, then the upper limit of the current transition reference can be shifted and corrected to a specific intermediate value to some extent. If the unit current rises to 120 amps within a few seconds, and the direction is consistent and the duration does not exceed the allowable transition time, it can be judged as operating condition following, and no alarm will be triggered.

[0053] Through the above scheme, the judgment criteria during the transition period can, to a certain extent, avoid being too lenient in masking anomalies and also avoid being too strict in triggering false alarms. Adjusting the propulsion parameters in conjunction with the operating condition trend, control actions, and timing ensures that the stringency of the criteria follows the actual progress of load changes in real time. Transient compensation parameters incorporate historical transient behavior information, allowing the criteria to reasonably accommodate the inherent response delays of the equipment. The combination of these two approaches ensures that the judgment criteria during the transition period remain consistent with the real-time operating condition, providing a reasonable basis for subsequent multi-state diagnostic classification. This historical transient behavior information can also be dynamically corrected in subsequent operation using samples confirmed to be following normally.

[0054] In actual operating environments, station passenger flow or environmental parameters may experience short-term fluctuations, and sensor-collected data may also exhibit instantaneous jumps. If the transition judgment benchmark is updated solely based on the trend of operating condition changes and control commands, the adjustment of propulsion parameters may rapidly increase or decrease, causing the value of the transition judgment benchmark to fluctuate within a short period. This inherent fluctuation in the benchmark itself can easily lead to deviations in subsequent diagnostic comparisons. To address this, this application further proposes an anti-sway and update boundary mechanism.

[0055] Furthermore, in some preferred methods, generating a transition judgment benchmark also includes: acquiring operational feedback change information of the environmental control equipment; when there is inconsistency between the direction of change indicated by the operating condition change trend information, the direction of change indicated by the control command change information, and the direction of change indicated by the operational feedback change information, restricting the adjustment of the propulsion parameters to update in the direction that makes the transition judgment benchmark approach the second normal value range; when the direction of change indicated by the operating condition change trend information reverses during the process of generating transition judgment benchmarks multiple times consecutively, freezing the adjustment of the propulsion parameters to keep the transition judgment benchmark unchanged.

[0056] Operational feedback change information can be obtained by extracting the changes and directions of operational data such as current, speed, or valve opening of the environmental control equipment within adjacent time slots. After obtaining the operational feedback change information, the directions of change indicated by the operating condition change trend information, the control command change information, and the operational feedback change information are compared. These three directions can be compared using a sign function. When at least two of the three are opposite in direction or exhibit lag, inconsistency can be identified. This inconsistency, to some extent, indicates that at least one information source cannot indicate the true direction of the operating condition transition, which may be caused by short-term disturbances or signal asynchrony.

[0057] When a directional inconsistency is detected, updates to the adjustment propulsion parameters are restricted. This can be achieved by setting the increment of the adjustment propulsion parameters to zero, or by multiplying the calculated adjustment propulsion parameters by a suppression coefficient, allowing only small-scale updates. This restriction prevents the transitional judgment benchmark from moving towards a second normal value range when multi-source information is inconsistent, avoiding changes in the benchmark due to individual anomalous data points. If the inconsistency is resolved within a set number of time slices, normal updates can resume.

[0058] When the direction indicated by the operating condition trend information reverses during multiple consecutive benchmark generation processes, it indicates that the transition direction itself experiences short-term fluctuations. In this case, the adjustment parameters are frozen, locked to their current value, and not updated for a specified period. The freeze is lifted only after the direction is confirmed to be continuously stable. This reversal-freezing mechanism can handle short-term fluctuations in operating condition trends and prevent the transition judgment benchmark from jumping between two directions.

[0059] By introducing operational feedback change information and performing multi-source directional consistency judgment, combined with update restrictions and reversal freeze mechanisms, the propulsion parameters are conditionally updated based on directional consistency and stability. This keeps the adjustment curve of the transition judgment benchmark smooth, which can, to a certain extent, resist disturbances such as short-term passenger flow fluctuations, sensor jumps, and instantaneous asynchrony between control commands and equipment responses, preventing the benchmark itself from becoming a source of misjudgment, thus providing a reliable basis for subsequent equipment status diagnosis.

[0060] Furthermore, in some priority methods, the equipment operating data is compared with the normal value range of the equipment operating parameters corresponding to the transition judgment benchmark; information on the change direction of the equipment operating data, the change direction of the station operating conditions, and the change direction of the control commands for the environmental control equipment are obtained; when the equipment operating data falls within the normal value range, the change direction of the equipment operating data is consistent with the change direction of the station operating conditions and the change direction of the control commands, and the current duration of the equipment operating data exceeding the first normal value range does not exceed the preset allowable transition duration, the equipment status is determined to be the operating condition following state, and a first diagnostic record is generated. The first diagnostic record does not trigger an equipment fault alarm; otherwise, the equipment status is determined to be an abnormal state, and a second diagnostic record is generated. The second diagnostic record is used to trigger equipment fault handling.

[0061] During the transition phase of operating condition switching, relying solely on whether the values ​​fall within the relaxed or tightened ranges can still lead to misjudgments. For example, when a surge in passenger flow at a station causes an increase in heat load, the upper limit of the transition benchmark will be relaxed accordingly. If a chiller unit experiences an abnormal current spike due to an internal fault, and the current value happens to fall within the relaxed upper limit, simply comparing the values ​​could mistakenly identify it as a normal response. Conversely, during the load reduction phase after peak hours, if equipment cannot reduce its speed due to mechanical jamming, its operating data may still fall within the old peak benchmark that has not yet been fully tightened, thus masking the true equipment malfunction. To address this, this application, based on numerical comparison, further introduces judgment logic regarding the consistency of the direction of change and the allowable transition time.

[0062] Specifically, after obtaining the currently effective transition judgment benchmark and its corresponding normal value range, the real-time collected equipment operation data is compared with this normal value range to confirm whether the current value exceeds the limit. Based on this, further information on the direction of change of equipment operation data, the direction of change of station operating conditions, and the direction of change of control commands for environmental control equipment is obtained. The direction of change of equipment operation data can be determined by comparing the numerical trend of diagnostic quantities within a continuous sampling period, such as whether the current is continuously rising or falling. The direction of change of station operating conditions can be obtained based on the positive or negative value of the operating condition change index calculated above or the trend of the regional operating condition label, reflecting whether the external load is trending upward or downward. The direction of change of control commands can be determined by comparing the difference between the current command value and the previous stable operating condition command value, such as whether the controller is issuing a load increase or load decrease command.

[0063] It is worth noting that this application requires consistency in these three directions because, in a real physical environment, the normal response of equipment is necessarily the result of the coordination of external load demand, system control intent, and equipment actions. If the station's operating conditions are increasing, and the control commands are also increasing, but the equipment's operating current is decreasing, this largely indicates that the equipment itself has lost its responsiveness. Through this multi-source consistency verification, it is possible to effectively avoid misjudging reverse changes or irrelevant fluctuations caused by control failures as normal following behavior after the transition period benchmark is relaxed.

[0064] After confirming the direction is consistent, the current duration of the equipment operating data exceeding the first normal value range is calculated. When the equipment operating data first exceeds the first normal value range, it can be recorded as the transition timing start point, and a timer is maintained in the transition state context of the corresponding diagnostic quantity of the equipment, accumulating in each sampling period. If the equipment operating data falls within the normal value range, and the above three change directions are consistent, and the current duration does not exceed the preset allowable transition duration, then the equipment state is determined to be in the condition following state. The preset allowable transition duration can be read from the transition rule table, which reflects the reasonable time window required for this type of equipment to complete a normal physical response under the current change direction.

[0065] For equipment identified as being in a condition-following state, a first diagnostic record is generated. This record may include the equipment number, current diagnostic value, transition direction, duration, and diagnostic conclusion, used for subsequent archiving and transition process tracking. Crucially, the first diagnostic record is marked as not triggering equipment fault alarms, meaning it will not be pushed to the dispatch terminal's alarm list or generate a maintenance work order. Taking the morning peak chiller unit load increase as an example, if the unit current rapidly rises from a stable peak value to a higher level, as long as the current transition benchmark has been broadened to cover this range, and the current increase direction is consistent with the passenger flow increase trend and the load increase command direction, and the duration is within the allowable range, it will be classified as a condition-following unit. This approach directly cuts off the path for false alarms to be transmitted from normal high-load operations to the dispatch terminal, reducing the burden on maintenance personnel from unnecessary on-site troubleshooting to some extent.

[0066] Conversely, if all of the above conditions are not met—that is, if the equipment operating data exceeds the normal range of the transition judgment benchmark, or if the direction of change is inconsistent, or if the current duration exceeds the preset allowable transition duration—then the equipment is determined to be in an abnormal state, and a second diagnostic record is generated. This second diagnostic record, while containing basic operating information, is also marked as a trigger for equipment fault handling, thereby triggering alarm push notifications or maintenance work order generation.

[0067] This logic for determining abnormal states is particularly crucial during peak-hour withdrawal. As passenger flow disperses and the control system issues load reduction commands, the transition baseline begins to tighten. If a wind turbine maintains high speed for an extended period due to control failure, its operating data may temporarily fall within a wider transition baseline. However, because its direction of change is inconsistent with the load reduction command, or the high-speed dwell time exceeds the allowable transition duration, it will still be identified as an abnormal state and a second diagnostic record will be generated. This exposes the real hidden dangers that were easily masked by overly wide baselines, ensuring that maintenance personnel can intervene promptly.

[0068] In some specific implementations, abnormal states that do not meet the operating condition tracking conditions can be further distinguished into suspected abnormalities and actual abnormalities based on the degree of exceeding limits, the degree of directional deviation, or the duration. For suspected abnormalities, a shortened observation window and continuous tracking across multiple sampling periods can be adopted; for actual abnormalities, the fault handling process is directly triggered. Furthermore, for the same equipment, if any key diagnostic quantity is determined to be in an abnormal state, the entire equipment can be marked as abnormal, thereby reducing the risk of missed reports to some extent.

[0069] Through the above multi-dimensional diagnostic classification logic, combined with the dynamic adjustment of the transition benchmark, the diagnostic conclusions can accurately reflect the actual operating status of the equipment at various points in the transition period.

[0070] During the transition period, as the load decreases from the second load state to the first load state, the transition judgment criteria are gradually tightening. At this time, simply relying on numerical range comparison, directional consistency checks, and conventional duration judgments may still be insufficient to distinguish between equipment that is normally reducing load and equipment that has failed to reduce load due to control failure. Therefore, additional conditions are introduced to determine the operating condition following state during the transition period of load reduction direction.

[0071] Furthermore, in some priority modes, when the transition period is the transition period for the station's operating conditions to change from the second load state to the first load state, the conditions for determining the equipment's state as the operating condition following state also include: the rate of decline of the equipment's operating data reaches the preset minimum decline rate, and within the preset allowable decline lag time, the equipment's operating data shows a decline change that meets the preset decline amplitude requirements.

[0072] After the peak load ends, the station's operating conditions enter a transition period from the second load state to the first load state, and the transition judgment criteria are gradually tightening. If diagnosis relies solely on general numerical ranges, directional consistency, and duration conditions, equipment that failed to reduce load in time due to control failure, transmission jamming, or local faults may not have exceeded the current transition criteria in terms of operating data. Furthermore, small fluctuations in the data may be misjudged as directional consistency, thus still being judged as operating condition following, causing equipment anomalies to be masked.

[0073] To address the above situation, during the transition period in the load reduction direction, in addition to the completed transition benchmark comparison, directional consistency check, and duration check, two additional judgment conditions are introduced. In each sampling period, the current rate of decline in equipment operating data is calculated, which can be obtained through differential calculation of equipment operating data over continuous time slices. The calculation result is compared with the minimum decline rate threshold for that diagnostic quantity stored in the transition rule table. The minimum decline rate can be a preset value for different equipment types and diagnostic quantities; for example, the fan speed decline rate should not be lower than a specific speed change rate, and the chiller current decline rate should not be lower than a specific current change rate. These preset values ​​can be derived from equipment technical specifications or statistical values ​​from health history data.

[0074] Simultaneously, continuously record the cumulative lag time since the start of the transition or the issuance of the load reduction command, and check whether at least one data change meeting the lag requirement has occurred within this time window. The allowable lag time can be set according to the equipment type and load reduction response characteristics. For example, after receiving a load reduction command, a fan is allowed to begin to decrease in speed within a specific time, and a chiller is allowed to experience a current drop within a specific time. The lag requirement can be a relative or absolute value to avoid misinterpreting data fluctuations as valid lags.

[0075] Only if both of the above checks pass will the equipment whose operating data meets the general conditions be classified as being in the operating condition following state. Otherwise, even if the general conditions are met, the equipment will be excluded from the operating condition following state and will be transferred to the abnormal state branch for processing. Equipment that might have been included in the operating condition following state but failed to meet the fallback rate condition or failed to fall back effectively within the allowable hysteresis time will have its status label changed from operating condition following to abnormal, generating a second diagnostic record and triggering the equipment fault handling process.

[0076] By introducing the rate of change of fallback and the condition of allowable fallback hysteresis time, the ability to judge the dynamic quality of equipment load reduction action was obtained during the load reduction transition period. This solved the problem that equipment anomalies during the peak exit phase were masked by the old benchmark being too high. It changed the judgment result of the load reduction failure equipment in the diagnostic logic, so that equipment that should have been reduced but failed to be reduced normally can be identified in a timely manner and anomaly conclusions can be output. This ensures that maintenance personnel can intervene in maintenance in the early stage of equipment failure deterioration.

[0077] Furthermore, in some preferred approaches, the method also includes: after the transition period, determining that the station operating conditions have stabilized to the second load state based on the station operating condition data; when the equipment operating data meets the second normal value range within a continuous stable time slice, ending the transition period, and using the second normal value range as the judgment criterion for the equipment under the second load state.

[0078] After completing the diagnostics during the transition period, it is necessary to determine the timing for exiting the transition state. Without an exit mechanism, the transition baseline may remain in effect after the load stabilizes, leading to excessive diagnostic tolerance that masks potential equipment problems, or premature switching of the baseline before the equipment has adapted to the target load state, causing misjudgments. Therefore, exiting the transition period requires simultaneously meeting two conditions: stable external operating conditions and the equipment's own adaptation.

[0079] Specifically, by continuously monitoring station operating condition data, when the operating condition change indicators no longer show continuous unidirectional deviations within multiple consecutive sampling periods and fall within the stable range of the target load state, the station operating condition can be considered to have stabilized at the second load state. Simultaneously, for managed environmental control equipment, it is necessary to examine whether the equipment operating data falls within the second normal value range within consecutive stable time slices. These two conditions ensure, to a certain extent, that only equipment adapted to the target load state will switch benchmarks, avoiding misjudgments of unstable equipment due to premature benchmark switching.

[0080] When both of the above conditions are met, the transition period will end, the corresponding transition status flag will be cleared, the generation and updating of the transition judgment benchmark will stop, and the second normal value range will be set as the new, currently effective judgment benchmark for the equipment under the current stable operating conditions. Upon ending the transition period, a record of this transition can be generated and written to the database. The record can include the transition start and end times, changes in control commands, and actual feedback curves, thus providing data support for benchmark parameter correction.

[0081] It is worth noting that for equipment that has met the continuous stability conditions within the region, the transition period will be terminated and a stable baseline will be adopted. However, for individual equipment that has not yet met the continuous stability conditions, it may not be forced to exit the transition state and can continue to maintain transition monitoring or anomaly identification. This, to a certain extent, ensures the accuracy of diagnosis.

[0082] Once the transition period ends, if the station's operating conditions change again, a new transition period can be triggered based on the aforementioned method, thus maintaining the method's continued applicability over time.

[0083] Through the aforementioned state transition, the diagnostic benchmark changes from a variable transitional benchmark to a stable benchmark that matches the operating condition, enabling operation and maintenance management to maintain consistency between the diagnostic basis and the equipment operating status throughout the operating condition change cycle.

[0084] During the long-term operation of an environmental control system, mechanical wear of equipment or changes in the environment may cause its actual response characteristics to deviate from the initial set value. If the transition judgment benchmark is continuously generated by relying on fixed initial parameters, it may cause the diagnostic margin to be too tight or too loose to a certain extent.

[0085] Furthermore, in some preferred approaches, the method also includes: acquiring records identified as being in a normal following state during the historical transition period, as well as historical equipment operation data of the environmental control equipment as the source of the records during the historical transition period; determining the allowable range of transient fluctuations and the threshold for normal transition duration exhibited by the environmental control equipment during the historical transition period based on the acquired historical equipment operation data; and using the determined allowable range of transient fluctuations and the threshold for normal transition duration to correct the historical transient behavior information and allowable transition duration used in the subsequent generation of transition judgment benchmarks.

[0086] In the initial stage of equipment operation, historical transient behavior information and allowable transition duration can be initialized based on healthy operation samples collected during the commissioning period. As the number of operating condition switching increases during daily operation, a large number of transition period records will accumulate. During this process, only historical records that have been confirmed as normal following states and ultimately did not trigger fault maintenance work orders can be extracted as the basis for correction, thereby avoiding the mislearning of abnormal behavior as a normal baseline.

[0087] From these normal tracking records, historical equipment operation data, such as the actual current surge amplitude or speed drop time of the corresponding environmental control equipment in a specific load change direction, can be extracted. Based on the extracted actual fluctuation amplitude and actual time, the allowable range of transient fluctuations and the threshold for normal transition duration can be updated.

[0088] To prevent individual extreme samples from excessively lengthening the allowable transition time, reasonable upper and lower limits can be set for the update amplitude, and a smooth update strategy can be adopted. When the accumulation of normal following samples available for correction is insufficient, the original parameters can be maintained unchanged until the sample size condition is met before performing correction.

[0089] Through this closed-loop correction based on real normal operation records, historical transient behavior information and allowable transition duration are gradually updated from initial settings to calibration values ​​that match the actual response capabilities of the field equipment. This allows the subsequently generated transition judgment benchmarks to dynamically adapt to the evolution of equipment characteristics, avoiding the trend of false alarms rising due to overly tight benchmarks and abnormal missed alarms due to overly loose benchmarks, thereby ensuring the continuous stability of diagnostic accuracy throughout the entire equipment lifecycle.

[0090] Furthermore, in some preferred methods, the station includes multiple areas, and the station operating condition data includes regional operating condition data corresponding to each of the multiple areas; when it is identified that the station operating condition is in a transition period from a first load state to a second load state based on the station operating condition data, the method includes: for each area, identifying whether the area is in a transition period based on the regional operating condition data of that area; when it is identified that the area is in a transition period, the second load state is gradually locked from a predetermined load state level sequence based on the continuity of the operating condition change trend of that area, and the initially locked second load state is the load state adjacent to the first load state in the sequence.

[0091] In actual stations, there are time differences in heat load changes across different areas. If the station is treated as a whole for transition period assessment, when passenger flow changes in some areas while other areas remain stable, the stable areas will be included in the transition assessment, causing adjustments to the diagnostic benchmark. Furthermore, if the target load status is set to the final level all at once, the transition benchmark may be relaxed in the initial transition phase, potentially including abnormal equipment data.

[0092] To address this, the station can be divided into multiple areas, such as the concourse, platform, and ventilation sections between stations. For each area, an operational context can be maintained, including the current stable operating condition label, the previous stable operating condition label, candidate target operating condition labels, transition status, and transition direction. In each time slice, transition period identification steps are performed separately using the area's operating condition data, ensuring that the transition status of different areas is independent.

[0093] When a region is identified as being in a transition period, a predetermined load status level sequence can be queried. This sequence can include low nighttime load, weekday off-peak, general passenger flow, and weekend peak. In the initial phase, the level adjacent to the first load status in the sequence is designated as the second load status. In subsequent time slices, if the region's load condition change trend continues to meet enhancement conditions, such as if the load condition change indicators maintain the same direction across multiple consecutive time slices, the second load status is adjusted and locked to a load status one level further back in the sequence. If the trend does not continue, the current locked state is maintained or a reversal is implemented.

[0094] The regional independence mechanism avoids diagnostic benchmark mismatch caused by asynchronous load changes in different regions, ensuring that regional benchmark changes respond to the dynamic heat load of that region. The gradual target locking mechanism prevents a one-time relaxation of the transition benchmark from masking equipment anomalies in the early stages of the transition, while also preventing high target settings due to short-term fluctuations, ensuring that the transition benchmark remains within the range covering normal changes.

[0095] This application also proposes an operation and maintenance management system for environmental control systems in urban rail transit, capable of executing any of the method steps described above. The system may include a data acquisition module for acquiring equipment operation data of the environmental control equipment and station operating condition data; a benchmark generation module for generating a transition judgment benchmark when the station operating condition is identified as being in a transition period from a first load state to a second load state based on the station operating condition data. The normal value range of the equipment operating parameters corresponding to the transition judgment benchmark is adjusted from a first normal value range corresponding to the first load state to a second normal value range corresponding to the second load state; and a diagnosis module for comparing the equipment operation data with the transition judgment benchmark to obtain the equipment's status diagnosis results during the transition period.

[0096] The aforementioned system can, to some extent, generate dynamically changing judgment criteria during the transition period, which helps to improve the problem of diagnostic distortion.

[0097] In some specific implementations, refer to Figure 2This environmental control system operation and maintenance management system for urban rail transit mainly consists of a data acquisition module, a baseline generation module, and a diagnostic module. The data acquisition module is responsible for collecting equipment operation data and equipment operation feedback change data from environmental control equipment, and simultaneously acquiring station operating condition data, passenger flow change data, and environmental parameter change data from stations encompassing multiple areas. The data acquisition module transmits this data to the baseline generation module. The baseline generation module contains two main processing stages: transition period identification and dynamic generation of transition judgment baselines. In the transition period identification stage, the system determines whether the station is in a transition period from a first load state to a second load state and calculates the operating condition change indicators. In the dynamic generation of transition judgment baselines stage, the system comprehensively calculates parameters based on the first normal value range, the second normal value range, operating condition change trend information, control command change information, allowable transition duration, historical transient behavior information, and operation feedback change information to derive adjustment propulsion parameters and transient compensation parameters. Then, these parameters are used for baseline calculation and adjustment, during which directional restrictions and freeze judgments are performed. For example, restrictions are imposed when the change direction is inconsistent, and freeze is imposed when the phase transition direction reverses, ultimately outputting a dynamic transition judgment baseline. The diagnostic module receives the transition judgment benchmark and, in conjunction with information on the direction of change and duration of changes in equipment, stations, and instructions, performs comparison and status determination. When the determination meets the conditions, the equipment is determined to be in a condition-following state, and a first diagnostic record is generated; when the determination does not meet the conditions, the equipment is determined to be in an abnormal state, and a second diagnostic record is generated. In addition, the system includes a parameter correction mechanism. The diagnostic module feeds back the allowable range of transient fluctuations and the normal transition duration threshold to the parameter correction stage. Combined with historical transition records extracted from the historical database, the parameters used in the benchmark generation module are corrected, thus forming a closed-loop optimization.

[0098] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. All modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for operation and maintenance management of environmental control systems for urban rail transit, characterized in that, include: Acquire equipment operation data of environmental control equipment and station operating condition data; When the station operating conditions are identified as being in a transition period from a first load state to a second load state based on the station operating condition data, a transition judgment benchmark is generated. The normal value range of the equipment operating parameters corresponding to the transition judgment benchmark is adjusted from the first normal value range corresponding to the first load state to the second normal value range corresponding to the second load state. The equipment operation data is compared with the transition judgment benchmark to obtain the status diagnosis result of the environmental control equipment during the transition period; The generation of transition judgment criteria includes: Obtain a first normal range of values ​​for the equipment operating parameters corresponding to the first load state, and a second normal range of values ​​for the equipment operating parameters corresponding to the second load state; Acquire information on the trend of operating conditions, information on the change of control commands for the environmental control equipment, the preset allowable transition time, and the historical transient behavior information of the environmental control equipment in the direction of change; Based on the operating condition change trend information, the control command change information, and the allowable transition duration, determine the adjustment advancement parameters for adjusting from the first normal value range to the second normal value range; Based on the control command change information and the historical transient behavior information, the transient compensation parameters are determined; Using the adjusted propulsion parameters and the transient compensation parameters, the first normal value range and the second normal value range are calculated to obtain the normal value range of the equipment operating parameters at the current moment, which serves as the transition judgment benchmark; The generation of transition judgment criteria also includes: Obtain the operational feedback change information of the environmental control equipment; When there is an inconsistency between the direction of change indicated by the operating condition change trend information, the direction of change indicated by the control command change information, and the direction of change indicated by the operation feedback change information, the adjustment of the propulsion parameter is restricted from being updated in the direction that makes the transition judgment benchmark approach the second normal value range. When the direction of change indicated by the operating condition change trend information reverses during the process of generating the transition judgment benchmark multiple times, the adjustment and advancement parameters are frozen to keep the transition judgment benchmark unchanged.

2. The method according to claim 1, characterized in that, When identifying, based on the station operating condition data, that the station operating condition is in a transition period from a first load state to a second load state, the following is included: Acquire at least one of the following: passenger flow change data, environmental parameter change data, equipment control command change data, and equipment operation feedback change data; Based on at least one of the acquired passenger flow change data, environmental parameter change data, equipment control command change data, and equipment operation feedback change data, calculate a condition change index to characterize the trend of the station's operating conditions changing towards the second load state. When the operating condition change indicators all indicate the same direction of change within a continuous time slice set, the station operating condition is identified as being in the transition period.

3. The method according to claim 1, characterized in that, The step of comparing the equipment operating data with the transition judgment benchmark to obtain the status diagnosis result of the environmental control equipment during the transition period includes: The device operating data is compared with the normal range of device operating parameters corresponding to the transition judgment benchmark; Acquire information on the direction of change of the equipment operating data, the direction of change of the station operating conditions, and the direction of change of the control commands for the environmental control equipment; When the equipment operating data falls within the normal value range, the direction of change of the equipment operating data is consistent with the direction of change of the station operating condition and the direction of change of the control command, and the current duration of the equipment operating data exceeding the first normal value range does not exceed the preset allowable transition duration, the state of the environmental control equipment is determined to be the operating condition following state, and a first diagnostic record is generated. The first diagnostic record does not trigger an equipment fault alarm. Otherwise, the environmental control equipment is determined to be in an abnormal state, and a second diagnostic record is generated. The second diagnostic record is used to trigger equipment fault handling.

4. The method according to claim 3, characterized in that, When the transition period is the transition period during which the station's operating conditions change from the second load state to the first load state, the condition for determining that the environmental control equipment is in the operating condition following state further includes: The rate of decline in the device operating data reaches the preset minimum decline rate, and within the preset allowable decline lag time, the device operating data shows a decline that meets the preset decline amplitude requirement.

5. The method according to claim 1, characterized in that, The method further includes: After the transition period, the station operating conditions are determined to have stabilized at the second load state based on the station operating condition data; When the equipment operating data meets the second normal value range within a continuous stable time slice, the transition period ends, and the second normal value range is used as the judgment criterion for the environmental control equipment under the second load state.

6. The method according to claim 1, characterized in that, The method further includes: Acquire records that were identified as being in a normal following state during the historical transition period, as well as historical equipment operation data of the environmental control equipment during the historical transition period, which are the source of the records; Based on the acquired historical equipment operation data, determine the allowable range of transient fluctuations and the threshold of normal transition duration exhibited by the environmental control equipment during the historical transition period; Using the determined allowable range of transient fluctuations and the normal transition duration threshold, the historical transient behavior information and the allowable transition duration used in the subsequent generation of transition judgment benchmarks are corrected.

7. The method according to claim 1, characterized in that, The station includes multiple areas, and the station operating data includes regional operating data corresponding to each of the multiple areas. When identifying, based on the station operating condition data, that the station operating condition is in a transition period from a first load state to a second load state, the following is included: For each region, based on the region's operating condition data, it is determined whether the region is in the transition period. When the region is identified as being in the transition period, the second load state is gradually locked from a predetermined load state level sequence based on the continued trend of the region's operating conditions, and the initially locked second load state is the load state adjacent to the first load state in the sequence.

8. A system operation and maintenance management system for urban rail transit, used to execute the method according to any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire equipment operation data of the environmental control equipment and station operating data; The benchmark generation module is used to generate a transition judgment benchmark when the station operating conditions are identified as being in a transition period from a first load state to a second load state based on the station operating condition data. The normal value range of the equipment operating parameters corresponding to the transition judgment benchmark is adjusted from the first normal value range corresponding to the first load state to the second normal value range corresponding to the second load state. The diagnostic module is used to compare the equipment operation data with the transition judgment benchmark to obtain the status diagnostic results of the environmental control equipment during the transition period.

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