Cloud computing evaluation method and system for comprehensive power transmission efficiency of compressed air station

Abstract

The invention relates to the technical field of energy efficiency evaluation, in particular to a cloud computing evaluation method and system for the comprehensive power transmission efficiency of a compressed air station, and the method comprises the steps: obtaining the operation information of the compressed air station, the operation information comprising equipment parameters, pipe network data and environment parameters; obtaining an initial power transmission efficiency value based on the equipment parameters, the pipe network data and the environment parameters; if the initial power transmission efficiency value is lower than a first efficiency threshold value, the compressed air station is marked as a low-efficiency operation station; based on the compressed air station, acquiring system configuration information corresponding to the low-efficiency operation station; based on the system configuration information, analyzing the operation information and obtaining an efficiency correction coefficient; obtaining a target power transmission efficiency value based on the initial power transmission efficiency value and the efficiency correction coefficient; and if the target power transmission efficiency value is lower than the first efficiency threshold value, generating an efficiency optimization suggestion. According to the method, the completeness, the adaptability and the accuracy of comprehensive power transmission efficiency evaluation of the compressed air station can be improved, and the pertinence of optimization suggestions is enhanced.

Description

Technical Field

[0001] This application relates to the field of energy efficiency assessment technology, and in particular to a cloud computing assessment method and system for the comprehensive power transmission efficiency of a compressed air station. Background Technology

[0002] In industrial production, compressed air stations are core infrastructure providing power sources, and their overall power transmission efficiency directly affects a company's energy consumption costs, production continuity, and equipment lifespan. High overall power transmission efficiency not only reduces industrial energy consumption and carbon emissions but also ensures the stable operation of downstream air-consuming equipment. Therefore, scientifically and accurately assessing the overall power transmission efficiency of compressed air stations has become a crucial aspect of industrial energy efficiency management. Currently, the overall power transmission efficiency of compressed air stations is mainly assessed through manual statistical analysis of equipment parameters and monitoring of pipeline pressure. This determines whether the station's operating efficiency meets standards, providing a basis for subsequent energy efficiency optimization.

[0003] However, due to the complex operating environment of compressed air stations, involving multiple devices operating in tandem, differences in pipeline layout, and fluctuations in ambient temperature and humidity, traditional assessment methods have significant limitations. Some assessments rely solely on the operating data of a single device, ignoring the combined impact of pipeline transmission losses and environmental factors. Other assessments fail to consider the system configuration characteristics of compressed air stations, such as the differences between continuous and intermittent operation modes and the rationality of device linkage logic, leading to significant discrepancies between assessment results and actual operating efficiency. In such cases, traditional assessments may not only misidentify inefficient operating sites and fail to accurately pinpoint the root causes of energy efficiency problems, but also lack a scientific efficiency correction mechanism, resulting in untargeted subsequent optimization suggestions and ultimately exacerbating energy waste and increasing equipment maintenance costs. Summary of the Invention

[0004] To help improve the completeness, adaptability, and accuracy of the comprehensive power output efficiency assessment of compressed air stations, and to enhance the pertinence of optimization recommendations, this application provides a cloud computing assessment method and system for the comprehensive power output efficiency of compressed air stations.

[0005] Firstly, this application provides a cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station, which adopts the following technical solution: A cloud computing evaluation method for the overall power transmission efficiency of a compressed air station includes: Obtain the operating information of the compressed air station, including equipment parameters, pipeline data, and environmental parameters; Based on the equipment parameters, the pipeline data, and the environmental parameters, the initial power transmission efficiency value is obtained; If the initial power output efficiency value is lower than the first efficiency threshold, the compressed air station will be marked as an inefficient operating station. Based on the compressed air station, obtain the system configuration information corresponding to the inefficient operating station; Based on the system configuration information, the operating information is analyzed and an efficiency correction coefficient is obtained; Based on the initial power transmission efficiency value and the efficiency correction coefficient, the target power transmission efficiency value is obtained; If the target power output efficiency value is lower than the first efficiency threshold, then efficiency optimization suggestions are generated.

[0006] Optionally, obtaining the initial power transmission efficiency value based on the equipment parameters, the pipeline data, and the environmental parameters includes: Based on the pipeline network data, key pipeline network nodes are identified; Pressure and flow monitoring is performed on the key pipeline nodes to obtain node characteristic parameters; Based on the key pipeline nodes and their characteristic parameters, abnormal nodes are identified; (nodes with characteristic parameters exceeding the normal range are considered abnormal nodes). Based on the key pipeline nodes and the abnormal nodes, calculate the proportion of pipeline abnormalities. Based on the aforementioned equipment parameters, calculate the equipment operating load rate; Based on the aforementioned environmental parameters, determine the environmental temperature and humidity influence coefficients; The initial power transmission efficiency value is obtained by combining the equipment operating load rate, the influence coefficient of ambient temperature and humidity, and the proportion of pipeline anomalies.

[0007] Optionally, the step of analyzing the operating information and obtaining the efficiency correction coefficient based on the system configuration information includes: Based on the system configuration information, determine whether there are any inefficient correlation factors; If the aforementioned inefficient correlation factors exist, then obtain the feature data corresponding to the inefficient correlation factors; If the feature data exceeds the corresponding feature threshold, then based on the device parameters, it is determined whether the inefficient operating site is in a full-load operating state; If the inefficient operating site is operating at full capacity, then obtain the equipment linkage information; Based on the device linkage information, determine whether efficiency correction is needed; If efficiency correction is needed, obtain the efficiency correction coefficient.

[0008] Optionally, determining whether inefficient correlation factors exist based on the system configuration information includes: Obtain the historical operating data corresponding to the inefficient operating sites; Based on the historical operating data, the reasons for efficiency fluctuations in different time periods are extracted; The frequency of occurrence corresponding to the causes of the aforementioned efficiency fluctuations is statistically analyzed; If the occurrence frequency exceeds a preset frequency threshold, then the existence of the inefficient correlation factor is determined.

[0009] Optionally, after calculating the frequency of occurrence corresponding to the causes of the efficiency fluctuations, the method further includes: If no occurrence frequency exceeds the preset frequency threshold, then based on the system configuration information, it is determined whether the inefficient operating site is in continuous operation mode or intermittent operation mode; If the inefficient operating site is in the continuous operation mode, then obtain the equipment aging degree and maintenance cycle; If the aging degree of the equipment and the maintenance cycle have a significant impact on the operating information, then it is determined that there are inefficient correlation factors. If the inefficient operating site is in the intermittent operating mode, then obtain the start-stop transition time corresponding to the inefficient operating site; Based on the start-stop transition time, calculate the energy loss rate during the transition phase; If the energy loss rate exceeds the second efficiency threshold, it is determined that there are inefficient related factors.

[0010] Optionally, the equipment linkage information includes equipment start-up and shutdown sequence, pressure matching degree, and energy consumption coordination; the step of determining whether efficiency correction is needed based on the equipment linkage information includes: Based on the equipment parameters, the rated output power and actual output power of each device are obtained, and the target deviation rate is calculated based on the rated output power and the actual output power. If the target deviation rate is within the preset deviation range, then determine whether the pressure matching degree is the first matching level; If the first matching level is not changed to the second matching level within the preset time, it is determined that efficiency correction is needed. If it is the first matching level and changes to the second matching level within a preset time, then analyze the device start-up and shutdown sequence and obtain the response time difference of adjacent devices; If the response time difference is less than the preset time difference threshold and the energy consumption coordination is in the preset coordination state, then it is determined that no efficiency correction is needed; If the target deviation rate exceeds the preset deviation range, perform conflict analysis on the equipment linkage information and establish the correspondence between the equipment linkage information and the efficiency impact. Based on the correspondence, determine the efficiency impact of all linked information and obtain conflict relationships; If the conflict is an intra-device conflict, obtain the conflict coefficient; The conflict coefficient is compared with the preset coefficient threshold to generate a comparison result, and the efficiency correction is determined based on the comparison result.

[0011] Optionally, after determining the efficiency impact of all linkage information based on the correspondence and obtaining the conflict relationship, the method further includes: If the conflict is between devices, the operating priority of each device is determined based on the device parameters, and the influence weight corresponding to the linkage information is calculated based on the operating priority of the devices. If there is a primary target device with the greatest impact weight and the highest operational stability, then determine whether efficiency correction is needed based on the linkage information of the primary target device. If not, calculate the comprehensive score for each device by combining the impact weight and operational stability; Based on the linkage information of the device with the highest comprehensive score, determine whether efficiency correction is needed; If there are multiple second target devices with the highest comprehensive scores and they have the same score, then obtain the operating efficiency curve of the second target device after the most recent maintenance. Based on the slope trend of the efficiency curve, obtain the linkage information of the target equipment; Based on the target device linkage information, determine whether efficiency correction is needed.

[0012] Optionally, if efficiency correction is required, obtaining the efficiency correction coefficient includes: If efficiency correction is required, the key influencing parameters in the equipment linkage information are screened based on the efficiency correction judgment results, including start-stop response delay value, pressure fluctuation amplitude and energy consumption deviation. Obtain historical high-efficiency operation records of inefficient operating sites under the same environmental parameters, and extract the parameter baseline values ​​from the records; Calculate the percentage of deviation based on key influencing parameters and parameter baselines; Obtain the sensitivity coefficients of key influencing parameters to power transmission efficiency; The efficiency correction coefficient is obtained based on the percentage deviation and the sensitivity coefficient.

[0013] Optionally, the efficiency optimization suggestions include targeted pipeline optimization suggestions, equipment dynamic adjustment suggestions, and system reconfiguration suggestions; if the target power output efficiency value is lower than the first efficiency threshold, the generation of efficiency optimization suggestions includes: If the target power output efficiency value is lower than the first efficiency threshold, the absolute value of the difference between the target power output efficiency value and the first efficiency threshold is calculated, and the optimization level is divided according to the difference range. If it is a low-level optimization, extract the anomaly type with the highest proportion in the pipeline anomaly percentage data and generate targeted pipeline optimization suggestions; If it is a medium-level optimization, then dynamic adjustment suggestions for the equipment will be generated by combining the peak fluctuation period of the equipment's operating load rate and the changing pattern of the influence coefficient of ambient temperature and humidity. For high-level optimization, the conflict analysis results of equipment linkage information and the feasibility assessment of timing optimization are integrated to generate system refactoring suggestions.

[0014] Secondly, this application also discloses a cloud computing evaluation system for the comprehensive power transmission efficiency of a compressed air station, which adopts the following technical solution: A cloud computing evaluation system for the overall power transmission efficiency of a compressed air station includes: The information acquisition module is used to acquire the operating information of the compressed air station, including equipment parameters, pipeline data and environmental parameters; The initial evaluation module is used to obtain an initial power output efficiency value based on the equipment parameters, the pipeline data, and the environmental parameters. If the initial power output efficiency value is lower than the first efficiency threshold, the site marking module is used to mark the compressed air station as an inefficient operating site. The configuration acquisition module is used to acquire the system configuration information corresponding to the inefficient operating site based on the compressed air station; The correction calculation module is used to analyze the operating information based on the system configuration information and obtain the efficiency correction coefficient; The target evaluation module is used to obtain the target power output efficiency value based on the initial power output efficiency value and the efficiency correction coefficient; The suggestion generation module is used to generate efficiency optimization suggestions if the target power output efficiency value is lower than the first efficiency threshold.

[0015] In summary, this application includes the following beneficial technical effects: First, by integrating multi-dimensional operational information such as equipment parameters, pipeline data, and environmental parameters, the system overcomes the limitations of traditional assessments that rely on single data points, achieving a comprehensive depiction of the compressed air station's operational status and improving the completeness of the assessment. Second, by introducing a two-level assessment mechanism of initial and target power output efficiency values, combined with dynamic adjustment of efficiency correction coefficients, the system effectively adapts to the efficiency characteristics under different system configurations, solving the bias problem caused by neglecting system differences in traditional assessments and enhancing the adaptability of the assessment. Finally, through a closed-loop process of marking inefficient sites, analyzing system configurations, and generating targeted optimization suggestions, the system not only achieves accuracy in efficiency assessment but also provides a clear direction for energy efficiency optimization, helping to reduce energy waste and equipment maintenance costs, and improving the economy and stability of compressed air station operation. Attached Figure Description

[0016] Figure 1 This is a main flowchart of a cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to an embodiment of this application; Figure 2This is a flowchart of the steps to obtain the initial power output efficiency value; Figure 3 This is a flowchart of the steps involved in analyzing operational information and obtaining efficiency correction coefficients. Figure 4 This is a flowchart of the steps to generate efficiency optimization suggestions; Figure 5 This is a block diagram of a cloud computing evaluation system for the comprehensive power transmission efficiency of a compressed air station according to an embodiment of this application.

[0017] Explanation of reference numerals in the attached figures: 1. Information collection module; 2. Initial assessment module; 3. Site marking module; 4. Configuration acquisition module; 5. Correction calculation module; 6. Target assessment module; 7. Suggestion generation module. Detailed Implementation

[0018] In the first aspect, this application discloses a cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station.

[0019] Reference Figure 1 A cloud computing evaluation method for the overall power transmission efficiency of a compressed air station, comprising steps S101 to S107: Step S101: Obtain the operating information of the compressed air station, including equipment parameters, pipeline data and environmental parameters.

[0020] Specifically, in this embodiment, the operational information refers to three types of core data that reflect the actual working status of the compressed air station, including equipment parameters, pipeline data, and environmental parameters, which need to be collected through IoT sensors and cloud computing platforms.

[0021] In this embodiment, the equipment parameters are the core operating data of the air compressor, including the real-time power, cumulative running time, outlet pressure, and cooling water temperature of the air compressor. This data is directly synchronized from the air compressor's control system to the cloud. The pipeline data are the status data of the compressed air transmission pipeline network, including the pipeline diameter, real-time pressure of preset monitoring nodes, and hourly flow rate, which are collected by pressure sensors and flow sensors along the pipeline network. The environmental parameters are the environmental data around the station that affect the operation of the equipment, including the real-time temperature and relative humidity inside the station building, which are collected every 5 minutes by temperature and humidity sensors inside the station building and synchronously uploaded to the cloud computing platform.

[0022] Step S102: Obtain the initial power transmission efficiency value based on equipment parameters, pipeline data, and environmental parameters.

[0023] Specifically, in this embodiment, the initial power output efficiency value refers to the efficiency value after initially integrating the effects of equipment parameters, pipeline data, and environmental parameters. It does not consider personalized modifications to the system configuration and is the first step result of efficiency evaluation.

[0024] Step S103: If the initial power output efficiency value is lower than the first efficiency threshold, the compressed air station is marked as an inefficient operating station.

[0025] Specifically, in this embodiment, the first efficiency threshold refers to a preset efficiency qualification line (set according to industry standards or enterprise historical data, which is 85% in this embodiment); an inefficient operating site refers to a site whose initial efficiency is lower than this threshold and requires further analysis.

[0026] Step S104: Based on the compressed air station, obtain the system configuration information corresponding to the inefficient operating station.

[0027] Specifically, in this embodiment, the system configuration information refers to the inherent operating settings of the compressed air station, which determines the direction of efficiency analysis, including operating mode, equipment linkage logic, and maintenance cycle.

[0028] Step S105: Based on the system configuration information, analyze the operating information and obtain the efficiency correction coefficient.

[0029] Specifically, in this embodiment, the efficiency correction coefficient refers to the parameter used to calibrate the initial efficiency value (positive values ​​increase efficiency, negative values ​​decrease efficiency), which is used to eliminate evaluation bias caused by differences in system configuration.

[0030] Step S106: Obtain the target power transmission efficiency value based on the initial power transmission efficiency value and the efficiency correction coefficient.

[0031] Specifically, in this embodiment, the target power output efficiency value refers to the true efficiency value after correcting the initial power output efficiency with an efficiency correction coefficient. It integrates the initial assessment and the influence of system configuration and serves as the basis for the final efficiency determination.

[0032] Step S107: If the target power output efficiency value is lower than the first efficiency threshold, then generate efficiency optimization suggestions.

[0033] Specifically, in this embodiment, the efficiency optimization suggestions refer to solutions to inefficiency problems, and are divided into targeted pipeline network optimization suggestions, equipment dynamic adjustment suggestions, and system reconfiguration suggestions according to the efficiency gap.

[0034] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment firstly overcomes the limitations of traditional evaluations that rely on single data by integrating multi-dimensional operational information such as equipment parameters, pipeline data, and environmental parameters, thus achieving a comprehensive characterization of the compressed air station's operating status and improving the completeness of the evaluation. Secondly, it introduces a two-level evaluation mechanism of initial power transmission efficiency value and target power transmission efficiency value, combined with dynamic adjustment of efficiency correction coefficients, effectively adapting to the efficiency characteristics under different system configurations and solving the deviation problem caused by neglecting system differences in traditional evaluations, thereby enhancing the adaptability of the evaluation. Finally, through a closed-loop process of marking inefficient sites, analyzing system configurations, and generating targeted optimization suggestions, it not only achieves the accuracy of efficiency evaluation but also provides a clear direction for energy efficiency optimization, helping to reduce energy waste and equipment maintenance costs, and improving the economy and stability of compressed air station operation.

[0035] Reference Figure 2 In one embodiment of this invention, step S102, based on equipment parameters, pipeline data, and environmental parameters, obtains the initial power transmission efficiency value, including steps S201 to S207: Step S201: Based on the pipeline network data, obtain the key pipeline network nodes.

[0036] Specifically, in this embodiment, the key pipeline nodes refer to the nodes that have the greatest impact on compressed air transmission efficiency, which are usually the starting point (air compressor outlet), branch point, and ending point (air-using equipment inlet).

[0037] Step S202: Monitor the pressure and flow of key pipeline nodes to obtain node characteristic parameters.

[0038] Specifically, in this embodiment, node characteristic parameters refer to the core indicators that reflect the operating status of key pipeline nodes, mainly pressure and flow data.

[0039] Step S203: Based on key pipeline nodes and node characteristic parameters, obtain abnormal nodes.

[0040] Specifically, in this embodiment, nodes whose node characteristic parameters exceed the normal range (the normal range is set according to the rated parameters of the equipment, and the allowable pressure deviation in this embodiment is ±5%) are designated as abnormal nodes.

[0041] Step S204: Calculate the percentage of abnormal pipeline nodes based on key pipeline nodes and abnormal nodes.

[0042] Specifically, in this embodiment, the pipeline anomaly percentage data refers to the proportion of the number of abnormal nodes to the total number of key pipeline nodes, which quantifies the overall degree of pipeline anomaly. The formula is: Pipeline anomaly percentage data = number of abnormal nodes ÷ total number of key nodes × 100%.

[0043] Step S205: Calculate the equipment operating load rate based on the equipment parameters.

[0044] Specifically, in this embodiment, the equipment operating load rate refers to the ratio of the actual operating power of the air compressor to the rated power, reflecting the equipment load level. The formula is: Equipment operating load rate = actual power ÷ rated power × 100%. Both excessively high and low load rates will affect efficiency.

[0045] Step S206: Determine the influence coefficients of environmental temperature and humidity based on environmental parameters.

[0046] Specifically, in this embodiment, the environmental temperature and humidity influence coefficient refers to the degree of influence of temperature and humidity on the efficiency of the air compressor (the closer the coefficient is to 1, the smaller the influence). It is obtained by fitting historical data. The higher the temperature / humidity, the smaller the coefficient. The fitting formula is coefficient = 1 - ((temperature - 25℃) × 0.3% + (humidity - 50%) × 0.1%). Substituting the temperature of 27℃ and the humidity of 58%, the coefficient is calculated to be 1 - (2 × 0.3% + 8 × 0.1%) = 1 - 1.4% = 0.986.

[0047] Step S207: Combine the equipment operating load rate, the influence coefficient of ambient temperature and humidity, and the proportion of pipeline anomalies to obtain the initial power transmission efficiency value.

[0048] Specifically, the initial efficiency is calculated using a weighted formula (the weights are set according to the company's needs; in this embodiment, the weights are 40% for equipment, 30% for environment, and 30% for pipeline network). Then, the initial power transmission efficiency value is calculated using the formula "Initial power transmission efficiency value = (Equipment operating load rate × 0.4 + Ambient temperature and humidity influence coefficient × 0.3 + (1 - Pipeline network abnormality percentage data) × 0.3) × 100%".

[0049] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment overcomes the limitations of simplified processing of pipeline network influence in traditional evaluations by focusing on the monitoring and anomaly analysis of key pipeline network nodes, and achieves accurate quantification of efficiency loss in the transmission process. By integrating multiple factors such as equipment operating load rate and environmental temperature and humidity influence coefficients, an efficiency evaluation model that is more in line with actual working conditions is constructed, avoiding the one-sidedness of single-parameter evaluation. At the same time, through the collaborative calculation of pipeline network anomaly ratio data and equipment and environmental factors, the independent influence weight of each element on efficiency is preserved, while also reflecting the coupling effect between multiple factors. This makes the calculation of the initial power transmission efficiency value more scientific and accurate, providing reliable basic data support for subsequent inefficient site identification and helping to more accurately identify key links in energy efficiency loss.

[0050] Reference Figure 3 In one embodiment of this example, step S105, which analyzes the operating information and obtains the efficiency correction coefficient based on the system configuration information, includes steps S301 to S306: Step S301: Based on the system configuration information, determine whether there are inefficient correlation factors.

[0051] Specifically, in this embodiment, inefficient correlation factors refer to systemic reasons that continuously lead to reduced efficiency (such as equipment aging, pipeline leakage, and unreasonable operation mode), which need to be analyzed in conjunction with historical data and system configuration; for example, if the historical database of the cloud computing platform (data from the past 3 months) is called and it is found that the pipeline pressure drops by 0.02 MPa per month and has not been detected for 2 months beyond the deadline, then it is determined that there is an inefficient correlation factor of pipeline leakage.

[0052] Step S302: If there are inefficient correlation factors, obtain the feature data corresponding to the inefficient correlation factors.

[0053] Specifically, characteristic data refers to specific indicators that describe inefficient correlation factors and are used to quantify the degree of impact of the factor on efficiency (such as leakage amount, pressure drop rate).

[0054] Step S303: If the feature data exceeds the corresponding feature threshold, then based on the equipment parameters, determine whether the inefficient operating site is in a full-load operating state.

[0055] Specifically, in this embodiment, the feature threshold is used as the standard for determining whether inefficient correlation factors require special attention (in this embodiment, the monthly leakage threshold for the pipeline network is 0.3m). 3 / min); Full load operation status refers to the actual power of the air compressor being ≥90% of the rated power, reflecting whether the equipment is working at full load.

[0056] Step S304: If the inefficient operating site is operating at full load, obtain the equipment linkage information.

[0057] Specifically, in this embodiment, equipment linkage information refers to key data on the coordinated operation of multiple devices, including equipment start-up / stop sequence (the order in which devices start / stop), pressure matching degree (the degree of compatibility between the main pipeline and the terminal pressure), and energy consumption coordination (the degree of matching between equipment energy consumption and load).

[0058] Step S305: Based on the equipment linkage information, determine whether efficiency correction is needed.

[0059] Step S306: If efficiency correction is required, obtain the efficiency correction coefficient.

[0060] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment identifies inefficient related factors and characteristic data, and determines whether correction is needed by combining equipment operating status and linkage information, so that the efficiency correction coefficient is more in line with the actual system. The correction mechanism takes into account both equipment load and linkage logic, improves the dynamic adaptability of efficiency evaluation, and lays the foundation for accurately obtaining the target power transmission efficiency value.

[0061] In one embodiment of this example, step S301, based on system configuration information, determines whether there are inefficient correlation factors, including steps S401 to S404: Step S401: Obtain historical operation data corresponding to inefficient operating sites.

[0062] Specifically, in this embodiment, historical operating data refers to the operating records of the compressed air station over a past period (such as 3 months or 6 months), including efficiency values, pressure, flow rate, and energy consumption, which are used to trace the reasons for efficiency fluctuations.

[0063] Step S402: Based on historical operating data, extract the reasons for efficiency fluctuations in different time periods.

[0064] Specifically, in this embodiment, the cause of efficiency fluctuation is the specific factors that cause efficiency to change over time (such as high efficiency during the day when the load is high and low efficiency at night when the load is low; or a continuous decline in efficiency due to pipeline leakage). For example, if historical data is analyzed and it is found that efficiency decreases by 1% per month and is synchronized with the decrease in pipeline pressure, then the cause is extracted as "efficiency fluctuation caused by pipeline leakage".

[0065] Step S403: The frequency of occurrence corresponding to the causes of statistical efficiency fluctuations.

[0066] Specifically, in this embodiment, the occurrence frequency refers to the proportion of the number of times the cause of efficiency fluctuation occurs to the total number of monitoring times, which is used to determine whether the factor is a persistent problem; for example, if there are 90 days in the past 3 months, and pipeline leakage caused efficiency decline for 90 days, then the occurrence frequency = 90 / 90 × 100% = 100%.

[0067] Step S404: If the frequency of occurrence exceeds the preset frequency threshold, it is determined that there is an inefficient correlation factor.

[0068] Specifically, in this embodiment, the preset frequency threshold refers to the standard for judging whether the cause of efficiency fluctuation is an "inefficient related factor" (in this embodiment, it is set to 80%, that is, factors that appear continuously are judged as related factors).

[0069] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment can accurately locate high-frequency inefficiency-related factors by mining historical operating data of inefficient stations, extracting the causes of efficiency fluctuations in different time periods and statistically analyzing the frequency of occurrence. At the same time, it avoids subjective judgment of inefficiency causes, making the determination of inefficiency-related factors more objective and targeted, and providing precise problem guidance for subsequent efficiency correction and optimization.

[0070] In one embodiment of this example, after step S403, which determines the frequency of occurrence corresponding to the cause of statistical efficiency fluctuations, steps S501 to S506 are further included: Step S501: If there is no occurrence frequency exceeding the preset frequency threshold, then based on the system configuration information, determine whether the inefficient operating site is in continuous operation mode or intermittent operation mode.

[0071] Specifically, in this embodiment, continuous operation mode refers to a mode of uninterrupted gas supply 24 hours a day; intermittent operation mode refers to a mode of phased gas supply according to production needs.

[0072] Step S502: If the inefficient operating site is in continuous operation mode, then obtain the equipment aging level and maintenance cycle.

[0073] Specifically, in this embodiment, the degree of equipment aging refers to the ratio of the equipment's usage time to its designed lifespan (e.g., if the designed lifespan is 20,000 hours and it has been used for 10,000 hours, then the degree of aging is 50%); the maintenance cycle refers to the time interval for regular equipment maintenance (e.g., once every 6 months).

[0074] Step S503: If the aging of the equipment and the maintenance cycle have a significant impact on the operating information, then it is determined that there are inefficient correlation factors.

[0075] Specifically, in this embodiment, a significant impact refers to a decrease in efficiency caused by equipment aging and overdue maintenance that is greater than or equal to the standard value (the standard value in this embodiment is 2%). If a significant impact exists, it is determined that there is an inefficient correlation factor. For example, if aging causes an efficiency decrease of 1.5% and overdue maintenance causes a decrease of 0.8%, the total is 2.3% ≥ 2%. Therefore, the degree of equipment aging and maintenance cycle have a significant impact on the operating information, and it is determined that there is an inefficient correlation factor.

[0076] Step S504: If the inefficient operating site is in intermittent operation mode, then obtain the start-stop transition time corresponding to the inefficient operating site.

[0077] Specifically, in this embodiment, the start-stop transition time refers to the time it takes for the equipment to go from startup to stable operation in intermittent operation mode (e.g., it takes 5 minutes for an air compressor to start and reach the required pressure). The longer the transition time, the greater the energy loss.

[0078] Step S505: Calculate the energy loss rate during the transition phase based on the start-stop transition time.

[0079] Specifically, in this embodiment, the energy loss rate refers to the proportion of the extra energy consumption during the transition phase to the normal operating energy consumption. In this embodiment, the energy loss rate = (transition energy consumption - normal energy consumption) ÷ normal energy consumption × 100%.

[0080] Step S506: If the energy loss rate exceeds the second efficiency threshold, it is determined that there are inefficient related factors.

[0081] Specifically, the second efficiency threshold refers to the standard for judging whether the start-stop transition time is an inefficient related factor in the intermittent operation mode. In this embodiment, the second efficiency threshold can be set to 12%.

[0082] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment, for scenarios without high-frequency inefficiency factors, makes up for the shortcomings of a single evaluation logic by distinguishing between continuous and intermittent operation modes for analysis. For continuous operation mode, it focuses on the impact of equipment aging and maintenance cycle, and for intermittent operation mode, it calculates the energy consumption loss during start-stop transition. This achieves accurate capture of inefficiency-related factors under different operating characteristics, and further improves the comprehensiveness and adaptability of inefficiency cause identification.

[0083] In one embodiment of this example, step S305, based on device linkage information, determines whether efficiency correction is needed, including steps S601 to S609: Step S601: Based on the equipment parameters, obtain the rated output power and actual output power of each device, and calculate the target deviation rate based on the rated output power and actual output power.

[0084] Specifically, in this embodiment, the rated output power refers to the standard output power designed for the equipment; the actual output power refers to the power of the equipment in real time; the target deviation rate reflects the degree of deviation between the actual power and the rated power, and the formula is: target deviation rate = |actual output power - rated output power| ÷ rated output power × 100%, which is used to determine whether the power of the equipment is within a reasonable range.

[0085] Step S602: If the target deviation rate is within the preset deviation range, determine whether the pressure matching degree is the first matching level.

[0086] Specifically, in this embodiment, the preset deviation range refers to the pre-set acceptable range of power deviation (±5% in this scenario). If it exceeds this range, it indicates that the power is abnormal. The pressure matching degree refers to the degree of compatibility between the main pipeline pressure and the end gas consumption node pressure. In this embodiment, the pressure matching degree is divided into the first matching level (pressure loss ≤8%), the second level (pressure loss between 8% and 15%), and the third level (pressure loss >15%). The smaller the loss, the higher the matching degree.

[0087] Step S603: If it is the first matching level and has not changed to the second matching level within the preset time, it is determined that efficiency correction is needed.

[0088] Specifically, in this embodiment, the preset duration refers to the pre-set stable observation period of pressure matching degree (set to 30 minutes in this scenario), which is used to determine whether the matching degree is consistently qualified. For example, if the pressure matching degree remains at the first level (loss of 7.1%) for 30 minutes, it indicates that the equipment power deviation (e.g., 5.5%) is not caused by pressure problems, but may be due to equipment aging or load calculation deviation, and it needs to be determined that "efficiency correction is required". If the pressure loss rises to 9% within 30 minutes (becoming the second level), it indicates that the deviation is caused by pressure fluctuations and will not be corrected for the time being.

[0089] Step S604: If it is the first matching level and changes to the second matching level within a preset time, then analyze the device start-up and shutdown sequence and obtain the response time difference of adjacent devices.

[0090] Specifically, in this embodiment, the response time difference between adjacent devices refers to the response time difference of one device (adjacent device) after the other device triggers a start / stop signal (such as the interval between A stopping and B starting), reflecting the timeliness of device linkage.

[0091] Step S605: If the response time difference is less than the preset time difference threshold and the energy consumption coordination is in the preset coordination state, then it is determined that no efficiency correction is needed.

[0092] Specifically, in this embodiment, if the response time difference is greater than a preset time difference threshold and the device start-up and shutdown sequence is a non-optimal sequence, the optimal timing scheme is obtained and the feasibility of adjusting the current timing of the device is evaluated. If the feasibility matches the optimal scheme, it is determined that no efficiency correction is needed. If the response time difference of adjacent devices is greater than a preset time difference threshold, and the device start-up and shutdown sequence is compared with the standard timing of a high-efficiency operating site under the same operating conditions and the rated operating timing of the device, it is determined that the current device start-up and shutdown sequence is a non-optimal sequence. Then, the optimal device start-up and shutdown sequence scheme that adapts to the device configuration and load requirements of the inefficient operating site is obtained. Based on the current hardware performance, control module compatibility, and operating load status of the device, the feasibility of adjusting the current timing to adapt to the optimal timing scheme is evaluated. If the evaluation result shows that the device currently has the conditions for timing adjustment, and the adjustment can meet the operating requirements of the optimal timing scheme, it is determined that no efficiency correction is needed.

[0093] Step S606: If the target deviation rate exceeds the preset deviation range, perform conflict analysis on the equipment linkage information and establish the correspondence between the equipment linkage information and the efficiency impact.

[0094] Specifically, in this embodiment, conflict analysis refers to investigating the problems of efficiency reduction caused by contradictions in equipment linkage information (equipment start-up and shutdown sequence, pressure matching degree, energy consumption coordination) (such as reasonable sequence but excessive energy consumption); correspondence refers to clarifying the specific impact of a certain linkage information anomaly on efficiency (such as a 20-second response time difference → 3% efficiency reduction).

[0095] Step S607: Determine the efficiency impact of all linkage information based on the correspondence relationship, and obtain the conflict relationship.

[0096] Specifically, conflict relationships refer to contradictory states in linkage information where there is both an increase in efficiency and a decrease in efficiency. In this embodiment, conflict relationships are divided into two categories: intra-equipment conflict (parameter contradictions of a single device, such as A having a power deviation but a qualified pressure) and inter-equipment conflict (linkage contradictions of multiple devices, such as A stopping and B starting out asynchronously).

[0097] Step S608: If the conflict relationship is an intra-device conflict, obtain the conflict coefficient.

[0098] Specifically, the conflict coefficient is an indicator that quantifies the degree of conflict within equipment, ranging from 0 to 1. The calculation formula is: Conflict Coefficient = Adverse Efficiency Impact Value ÷ (Favorable Efficiency Impact Value + Adverse Efficiency Impact Value). The closer the coefficient is to 1, the greater the negative impact of the conflict on efficiency. In this embodiment, the adverse efficiency impact value refers to the specific extent to which the power transmission efficiency decreases due to an abnormality in a certain linkage information of a single device (such as air compressor A) (such as unreasonable equipment start-up and shutdown sequence or pipeline leakage). The favorable efficiency impact value refers to the specific extent to which the power transmission efficiency is improved by optimizing another linkage information of the same device (air compressor A) (such as qualified pressure matching or good energy consumption coordination). By first obtaining the baseline efficiency, in this embodiment, the average value of the three records with the highest power transmission efficiency is taken as the baseline efficiency. Then, the favorable efficiency impact value and the adverse efficiency impact value are calculated separately. Favorable efficiency impact value = Power transmission efficiency - Baseline efficiency; Adverse efficiency impact value = Baseline efficiency - Power transmission efficiency.

[0099] Step S609: Compare the conflict coefficient with the preset coefficient threshold, generate the comparison result, and determine whether efficiency correction is needed based on the comparison result.

[0100] Specifically, the preset coefficient threshold refers to the critical value for determining whether a conflict within the equipment needs to be addressed through efficiency correction. In this embodiment, the preset coefficient threshold can be set to 0.6. If the conflict coefficient is greater than or equal to the preset threshold, it indicates that an adverse effect within the equipment is dominating efficiency, and efficiency correction is required. If the conflict coefficient is less than the preset coefficient threshold, it means that the beneficial effects can offset some of the adverse effects, the efficiency deviation is within an acceptable range, and it is determined that no efficiency correction is needed.

[0101] The cloud computing evaluation method for the comprehensive power output efficiency of compressed air stations provided in this embodiment refines equipment linkage information into start-stop sequence, pressure matching degree, and energy consumption coordination. Combined with the target deviation rate calculated from rated and actual output power, it constructs a multi-dimensional correction judgment logic. It considers the stability of pressure matching level and equipment response time difference under normal operating conditions, performs conflict analysis and establishes the corresponding impact for deviations exceeding the range, and distinguishes different handling rules for intra-equipment conflicts and inter-equipment conflicts. This helps to comprehensively cover the complex scenarios of equipment linkage, making the necessity judgment of efficiency correction more in line with the actual operating state, greatly improving the accuracy and applicability of correction judgment, and laying the foundation for the reasonable acquisition of subsequent correction coefficients.

[0102] In one embodiment of this example, after determining the efficiency impact of all linkage information based on the correspondence in step S607 and obtaining the conflict relationship, the method further includes steps S701 to S707: Step S701: If the conflict relationship is an inter-device conflict, then determine the operating priority of each device based on the device parameters, and calculate the influence weight corresponding to the linkage information based on the device operating priority.

[0103] Specifically, in this embodiment, the equipment operation priority is divided according to the "importance of the equipment to the stability of the gas supply". The rule in this embodiment is "core gas supply equipment (main air compressor) > auxiliary regulating equipment (air tank pressure valve) > standby equipment (standby air compressor)", with corresponding priority coefficients of 1.0, 0.7 and 0.5 respectively. The influence weight is calculated by "priority × linkage information influence degree", where the linkage information influence degree is the proportion of the information's impact on efficiency (such as start-stop timing influence degree 0.4, pressure matching degree 0.3, energy consumption synergy 0.3).

[0104] Step S702: If there is a first target device with the largest impact weight and the highest operational stability, then determine whether efficiency correction is needed based on the linkage information of the first target device.

[0105] Specifically, in this embodiment, operational stability is evaluated by the number of equipment failures (≤1 failure is considered excellent) and parameter fluctuation range (≤3% is considered excellent) over the past 30 days. The higher the stability, the stronger the reliability of the equipment data. The equipment that simultaneously meets the criteria of having the greatest impact weight and the highest operational stability is the first target equipment. This equipment is the core contributor to the efficiency deviation, and its data is highly reliable, so there is no need to worry about abnormal data interfering with the judgment. If the linkage information of the first target equipment is abnormal, it indicates that the linkage problem of the equipment is the main cause of the efficiency decline, and the initial power output efficiency value needs to be corrected, so the judgment requires efficiency correction. If the linkage information of the first target equipment is normal, it indicates that the conflict between the equipment has a small impact on efficiency, the initial power output efficiency value does not need to be adjusted, and the judgment does not require efficiency correction.

[0106] Step S703: If it does not exist, calculate the comprehensive score of each device by combining the influence weight and the operational stability.

[0107] Specifically, in this embodiment, the comprehensive score = influence weight × 0.6 + operational stability score × 0.4.

[0108] Step S704: Based on the linkage information of the device with the highest comprehensive score, determine whether efficiency correction is needed.

[0109] Specifically, in this embodiment, if the linkage information of the device with the highest comprehensive score has one or more abnormalities, it indicates that the linkage problem has significantly affected efficiency, the initial power output efficiency value needs to be corrected, and efficiency correction is required; if the linkage information of the device has no abnormalities, it indicates that the negative impact of the linkage information on efficiency is within an acceptable range, the initial power output efficiency value does not need to be adjusted, and efficiency correction is not required.

[0110] Step S705: If there are multiple second target devices with the highest comprehensive score and the scores are the same, then obtain the operating efficiency curve of the second target device after the most recent maintenance.

[0111] Specifically, the second target device is the device with the highest comprehensive score. If two or more second target devices have completely identical comprehensive scores, the device that best reflects the efficiency deviation is further screened through the operating efficiency curve after the most recent maintenance. In this embodiment, the operating efficiency curve is a chart plotted with "operating time (hours / day)" as the horizontal axis and "power output efficiency (%)" as the vertical axis. It is used to visually display the decay trend of device efficiency over time. The larger the absolute value of the slope, the faster the efficiency drops and the higher the possibility of abnormal linkage information.

[0112] Step S706: Based on the slope change trend of the efficiency curve, obtain the linkage information of the target equipment.

[0113] Specifically, the slope change trend refers to the direction of the efficiency change rate of the operating efficiency curve over time. By calculating the slope of the curve at different time periods, it is determined whether the power output efficiency of the equipment is steadily declining, accelerating, or slowly declining, and then it is determined whether the efficiency decline is abnormally related to linkage information (such as pressure fluctuations, start-stop delays). In this embodiment, the second target device with the largest absolute value of the slope is selected as the final target device, and its linkage information directly related to the efficiency decline is extracted, namely the target device linkage information.

[0114] Step S707: Based on the target device linkage information, determine whether efficiency correction is needed.

[0115] Specifically, in this embodiment, if the abnormality of the target device linkage information exceeds the allowable range (e.g., pressure fluctuation amplitude 0.08MPa > 0.05MPa), it indicates that the linkage information is the core cause of efficiency decay, the initial power output efficiency value does not reflect this impact, and needs to be corrected to reflect the true efficiency; therefore, efficiency correction is required. If the abnormality of the target device linkage information is within the allowable range (e.g., start / stop response delay value 8 seconds ≤ 10 seconds), it indicates that the impact of the linkage information on efficiency is within the acceptable range, the initial power output efficiency value does not need to be adjusted, and efficiency correction is not required.

[0116] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment addresses the conflict scenarios between equipment by prioritizing the calculation of influence weights, screening key equipment through comprehensive scoring, or judging based on the slope of the efficiency curve. This forms a hierarchical and progressive judgment logic, which avoids the one-sidedness of single-dimensional judgment and can accurately locate the linkage information that has the greatest impact on efficiency. This makes the efficiency correction judgment more in line with the actual equipment collaboration and further improves the scientificity and adaptability of the correction judgment.

[0117] In one embodiment of this example, if efficiency correction is required in step S306, obtaining the efficiency correction coefficient includes steps S801 to S805: Step S801: If efficiency correction is required, based on the efficiency correction determination result, filter the key influencing parameters in the equipment linkage information, including start-stop response delay value, pressure fluctuation amplitude and energy consumption deviation.

[0118] Specifically, key influencing parameters refer to the core linkage parameters that cause efficiency to need to be corrected. They are selected from the equipment linkage information, and different key parameters correspond to different correction reasons. In this embodiment, the selection logic of key influencing parameters is as follows: if the correction reason is abnormal start-stop timing, the key parameter is the start-stop response delay value; if the reason is unstable pressure, the key parameter is the pressure fluctuation amplitude; if the reason is energy waste, the key parameter is the energy consumption deviation.

[0119] Step S802: Obtain historical high-efficiency operation records of inefficient operating sites under the same environmental parameters, and extract the parameter baseline values ​​from the records.

[0120] Specifically, in this embodiment, the historical high-efficiency operation record refers to the record of the top 10% of power output efficiency under the same environmental parameters in the historical operation data (stored on the cloud computing platform) of the site over the past 3-6 months; the parameter benchmark value is the average value of the key influencing parameters in the historical high-efficiency operation record.

[0121] Step S803: Calculate the percentage deviation based on the key influencing parameters and the parameter baseline values.

[0122] Specifically, in this embodiment, the deviation percentage is the relative difference between the key influencing parameter and the benchmark value, and the calculation formula is: deviation percentage = |key influencing parameter value - parameter benchmark value| ÷ parameter benchmark value × 100%.

[0123] Step S804: Obtain the sensitivity coefficient of key influencing parameters to power transmission efficiency.

[0124] Specifically, the sensitivity coefficient is a coefficient obtained in advance through industrial experiments and fitting with historical data. It reflects the impact of a 1% change in key influencing parameters on power transmission efficiency. The fitting process needs to be based on at least one year of historical operating data of the site. The correlation between each parameter and power transmission efficiency is determined through linear regression analysis, and finally a fixed coefficient table is formed and stored in the cloud computing platform for direct use during calculation. In this embodiment, the sensitivity coefficients for start-stop response delay, pressure fluctuation amplitude, and energy consumption deviation are 0.3, 0.45, and 0.5, respectively.

[0125] Step S805: Based on the deviation percentage and the sensitivity coefficient, obtain the efficiency correction coefficient.

[0126] Specifically, in this embodiment, the efficiency correction coefficient = Σ (percentage of deviation × sensitivity coefficient) ÷ 100.

[0127] The cloud computing evaluation method for the comprehensive power output efficiency of compressed air stations provided in this embodiment screens key influencing parameters such as start-stop response delay, determines benchmark values ​​by combining historical high-efficiency records under the same environment, and then calculates correction coefficients by using deviation percentage and sensitivity coefficient. This method not only anchors to objective reference standards but also considers the actual impact of parameters on efficiency, making the acquisition of correction coefficients more accurate and realistic. It effectively avoids deviations caused by subjective estimation and provides reliable support for the accurate calculation of target power output efficiency values.

[0128] Reference Figure 4 In one embodiment of this example, if the target power output efficiency value is lower than the first efficiency threshold in step S107, then generating efficiency optimization suggestions includes steps S901 to S904: Step S901: If the target power output efficiency value is lower than the first efficiency threshold, calculate the absolute value of the difference between the target power output efficiency value and the first efficiency threshold, and divide the optimization level according to the difference range.

[0129] Specifically, the absolute value of the difference = |target power output efficiency value - first efficiency threshold|; in this embodiment, when dividing the optimization level according to the interval of the absolute value of the difference, the following division criteria are used: Low-level optimization: Absolute difference < 5% (e.g., 3% difference) → Minor inefficiency issues, requiring only local adjustments (e.g., pipeline cleaning); Medium-level optimization: 5% ≤ absolute difference ≤ 10% (e.g., 7% difference) → Moderate inefficiency issues, requiring optimized operating strategies (e.g., dynamic equipment adjustment); High-level optimization: Absolute difference > 10% (e.g., 13% difference) → Severe inefficiency issues, requiring systemic restructuring (e.g., updating equipment linkage logic).

[0130] Step S902: If it is a low-level optimization, extract the anomaly type with the highest proportion in the pipeline anomaly proportion data and generate targeted pipeline optimization suggestions.

[0131] Specifically, in this embodiment, the targeted pipeline optimization suggestions include pressure compensation schemes for abnormal nodes and pipeline cleaning cycle settings.

[0132] Step S903: If it is a medium-level optimization, then combine the peak fluctuation period of the equipment operating load rate and the changing pattern of the environmental temperature and humidity influence coefficient to generate dynamic adjustment suggestions for the equipment.

[0133] Specifically, in this embodiment, the equipment dynamic adjustment suggestions include a dynamic adjustment range for the load rate and an adaptive temperature and humidity control strategy.

[0134] Step S904: If it is a high-level optimization, integrate the conflict analysis results of equipment linkage information and the feasibility assessment of timing optimization to generate system refactoring suggestions.

[0135] Specifically, in this embodiment, the system reconstruction suggestion includes an update scheme for the device linkage logic and an upgrade path for the intelligent control module.

[0136] The cloud computing evaluation method for the comprehensive power transmission efficiency of compressed air stations provided in this embodiment divides the optimization levels by calculating the difference between the target efficiency and the threshold, and matches differentiated suggestions to different levels—low level focuses on pipeline anomalies, medium level combines equipment load and environmental patterns, and high level integrates linkage conflicts and timing optimization. Through the hierarchical approach, the optimization suggestions are no longer general but can accurately correspond to different efficiency loss scenarios, which greatly improves the practicality and operability of the suggestions and helps compressed air stations to more efficiently locate and solve energy efficiency problems.

[0137] Secondly, this application also discloses a cloud computing evaluation system for the comprehensive power transmission efficiency of a compressed air station.

[0138] Reference Figure 5 A cloud computing evaluation system for the comprehensive power transmission efficiency of a compressed air station, comprising: The information acquisition module is used to acquire the operating information of the compressed air station, including equipment parameters, pipeline data, and environmental parameters. The initial evaluation module is used to obtain the initial power output efficiency value based on equipment parameters, pipeline data, and environmental parameters; If the initial power output efficiency value is lower than the first efficiency threshold, the site marking module is used to mark the compressed air station as an inefficient operating site. The configuration acquisition module is used to acquire system configuration information corresponding to inefficient operating sites based on compressed air stations; The correction calculation module is used to analyze the operating information based on system configuration information and obtain efficiency correction coefficients; The target evaluation module is used to obtain the target power efficiency value based on the initial power efficiency value and the efficiency correction coefficient; The suggestion generation module is used to generate efficiency optimization suggestions if the target power output efficiency value is lower than the first efficiency threshold.

[0139] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A cloud computing evaluation method for the overall power transmission efficiency of a compressed air station, characterized in that, include: Obtain the operating information of the compressed air station, including equipment parameters, pipeline data, and environmental parameters; Based on the equipment parameters, the pipeline data, and the environmental parameters, the initial power transmission efficiency value is obtained; If the initial power output efficiency value is lower than the first efficiency threshold, the compressed air station will be marked as an inefficient operating station. Based on the compressed air station, obtain the system configuration information corresponding to the inefficient operating station; Based on the system configuration information, the operating information is analyzed and an efficiency correction coefficient is obtained; Based on the initial power transmission efficiency value and the efficiency correction coefficient, the target power transmission efficiency value is obtained; If the target power output efficiency value is lower than the first efficiency threshold, then efficiency optimization suggestions are generated.

2. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 1, characterized in that, The process of obtaining the initial power transmission efficiency value based on the equipment parameters, the pipeline data, and the environmental parameters includes: Based on the pipeline network data, key pipeline network nodes are identified; Pressure and flow monitoring is performed on the key pipeline nodes to obtain node characteristic parameters; Based on the key pipeline nodes and the node characteristic parameters, abnormal nodes are identified; Based on the key pipeline nodes and the abnormal nodes, calculate the proportion of pipeline abnormalities. Based on the aforementioned equipment parameters, calculate the equipment operating load rate; Based on the aforementioned environmental parameters, determine the environmental temperature and humidity influence coefficients; The initial power transmission efficiency value is obtained by combining the equipment operating load rate, the influence coefficient of ambient temperature and humidity, and the proportion of pipeline anomalies.

3. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 1, characterized in that, The step of analyzing the operational information and obtaining the efficiency correction coefficient based on the system configuration information includes: Based on the system configuration information, determine whether there are any inefficient correlation factors; If the aforementioned inefficient correlation factors exist, then obtain the feature data corresponding to the inefficient correlation factors; If the feature data exceeds the corresponding feature threshold, then based on the device parameters, it is determined whether the inefficient operating site is in a full-load operating state; If the inefficient operating site is operating at full capacity, then obtain the equipment linkage information; Based on the device linkage information, determine whether efficiency correction is needed; If efficiency correction is needed, obtain the efficiency correction coefficient.

4. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 3, characterized in that, The determination of whether there are inefficient correlation factors based on the system configuration information includes: Obtain the historical operating data corresponding to the inefficient operating sites; Based on the historical operating data, the reasons for efficiency fluctuations in different time periods are extracted; The frequency of occurrence corresponding to the causes of the aforementioned efficiency fluctuations is statistically analyzed; If the occurrence frequency exceeds a preset frequency threshold, then the existence of the inefficient correlation factor is determined.

5. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 4, characterized in that, After calculating the frequency of occurrence corresponding to the causes of the efficiency fluctuations, the method further includes: If no occurrence frequency exceeds the preset frequency threshold, then based on the system configuration information, it is determined whether the inefficient operating site is in continuous operation mode or intermittent operation mode; If the inefficient operating site is in the continuous operation mode, then obtain the equipment aging degree and maintenance cycle; If the aging degree of the equipment and the maintenance cycle have a significant impact on the operating information, then it is determined that there are inefficient correlation factors. If the inefficient operating site is in the intermittent operating mode, then obtain the start-stop transition time corresponding to the inefficient operating site; Based on the start-stop transition time, calculate the energy loss rate during the transition phase; If the energy loss rate exceeds the second efficiency threshold, it is determined that there are inefficient related factors.

6. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 3, characterized in that, The equipment linkage information includes equipment start-up and shutdown sequence, pressure matching degree, and energy consumption coordination; the determination of whether efficiency correction is needed based on the equipment linkage information includes: Based on the equipment parameters, the rated output power and actual output power of each device are obtained, and the target deviation rate is calculated based on the rated output power and the actual output power. If the target deviation rate is within the preset deviation range, then determine whether the pressure matching degree is the first matching level; If the first matching level is not changed to the second matching level within the preset time, it is determined that efficiency correction is needed. If it is the first matching level and changes to the second matching level within a preset time, then analyze the device start-up and shutdown sequence and obtain the response time difference of adjacent devices; If the response time difference is less than the preset time difference threshold and the energy consumption coordination is in the preset coordination state, then it is determined that no efficiency correction is needed; If the target deviation rate exceeds the preset deviation range, perform conflict analysis on the equipment linkage information and establish the correspondence between the equipment linkage information and the efficiency impact. Based on the correspondence, determine the efficiency impact of all linked information and obtain conflict relationships; If the conflict is an intra-device conflict, obtain the conflict coefficient; The conflict coefficient is compared with the preset coefficient threshold to generate a comparison result, and the efficiency correction is determined based on the comparison result.

7. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 6, characterized in that, After determining the efficiency impact of all linkage information based on the correspondence and obtaining the conflict relationships, the process further includes: If the conflict is between devices, the operating priority of each device is determined based on the device parameters, and the influence weight corresponding to the linkage information is calculated based on the operating priority of the devices. If there is a primary target device with the greatest impact weight and the highest operational stability, then determine whether efficiency correction is needed based on the linkage information of the primary target device. If not, calculate the comprehensive score for each device by combining the impact weight and operational stability; Based on the linkage information of the device with the highest comprehensive score, determine whether efficiency correction is needed; If there are multiple second target devices with the highest comprehensive scores and they have the same score, then obtain the operating efficiency curve of the second target device after the most recent maintenance. Based on the slope trend of the efficiency curve, obtain the linkage information of the target equipment; Based on the target device linkage information, determine whether efficiency correction is needed.

8. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 3, characterized in that, If efficiency correction is required, the efficiency correction coefficient is obtained by: If efficiency correction is required, the key influencing parameters in the equipment linkage information are screened based on the efficiency correction judgment results, including start-stop response delay value, pressure fluctuation amplitude and energy consumption deviation. Obtain historical high-efficiency operation records of inefficient operating sites under the same environmental parameters, and extract the parameter baseline values ​​from the records; Calculate the percentage of deviation based on key influencing parameters and parameter baselines; Obtain the sensitivity coefficients of key influencing parameters to power transmission efficiency; The efficiency correction coefficient is obtained based on the percentage deviation and the sensitivity coefficient.

9. The cloud computing evaluation method for the comprehensive power transmission efficiency of a compressed air station according to claim 1, characterized in that, The efficiency optimization suggestions include targeted pipeline optimization suggestions, equipment dynamic adjustment suggestions, and system reconfiguration suggestions; if the target power output efficiency value is lower than the first efficiency threshold, the efficiency optimization suggestions generated include: If the target power output efficiency value is lower than the first efficiency threshold, the absolute value of the difference between the target power output efficiency value and the first efficiency threshold is calculated, and the optimization level is divided according to the difference range. If it is a low-level optimization, extract the anomaly type with the highest proportion in the pipeline anomaly percentage data and generate targeted pipeline optimization suggestions; If it is a medium-level optimization, then dynamic adjustment suggestions for the equipment will be generated by combining the peak fluctuation period of the equipment's operating load rate and the changing pattern of the influence coefficient of ambient temperature and humidity. For high-level optimization, the conflict analysis results of equipment linkage information and the feasibility assessment of timing optimization are integrated to generate system refactoring suggestions.

10. A cloud computing evaluation system for the comprehensive power transmission efficiency of a compressed air station, characterized in that, include: The information acquisition module is used to acquire the operating information of the compressed air station, including equipment parameters, pipeline data and environmental parameters; The initial evaluation module is used to obtain an initial power output efficiency value based on the equipment parameters, the pipeline data, and the environmental parameters. If the initial power output efficiency value is lower than the first efficiency threshold, the site marking module is used to mark the compressed air station as an inefficient operating site. The configuration acquisition module is used to acquire the system configuration information corresponding to the inefficient operating site based on the compressed air station; The correction calculation module is used to analyze the operating information based on the system configuration information and obtain the efficiency correction coefficient; The target evaluation module is used to obtain the target power output efficiency value based on the initial power output efficiency value and the efficiency correction coefficient; The suggestion generation module is used to generate efficiency optimization suggestions if the target power output efficiency value is lower than the first efficiency threshold.

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