Air compression station unattended operation multi-working-condition anomaly detection and regulation and control system and method

Abstract

The invention discloses an air compression station unattended multi-working-condition anomaly detection and regulation system and method, and relates to the field of anomaly detection and regulation, and the system comprises an anomaly monitoring module, a control module, a conflict detection module and a self-adaptive compensation module. By monitoring the operation parameters of the equipment in real time, the abnormity is automatically identified and the remote intervention request is triggered, so that the requirement of manual intervention is reduced. The conflict between the intervention control instruction and the automatic adjustment system is identified and adjusted, stable operation of the equipment is ensured, and equipment damage caused by excessive adjustment or conflict adjustment is avoided. A dynamic remote correction coefficient is generated based on the hardware state of equipment by combining an adaptive compensation module, the delay influence of a control instruction is effectively eliminated, accurate execution of a remote intervention instruction is ensured, and the response speed and the adjustment precision of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of anomaly detection and regulation, and particularly to an unmanned multi-condition anomaly detection and regulation system and method for an air compression station. BACKGROUND

[0002] In an air compression station, automatic control systems (such as PID controllers, fuzzy control systems, etc.) and remote operator instructions often take effect simultaneously. Automatic control systems adjust device operating parameters (such as compressor speed, load, etc.) based on real-time data (such as load, current, pressure, etc.). However, remote operator instructions may sometimes conflict with automatic control system adjustments, causing devices to frequently change operating states and thus affecting device stability and efficiency. Automatic control systems may adjust compressor operating loads based on real-time load changes, while remote operators may require increased or decreased device loads to respond to external demand changes. If automatic system adjustments and remote instructions conflict at this time, devices may be forced to make excessive adjustments (such as frequent start-up, stoppage, acceleration, or deceleration), resulting in excessive load fluctuations and even device malfunctions or reduced efficiency. Assuming that automatic control systems adjust compressor loads based on real-time pressure, flow, and temperature changes, remote operators may issue instructions to adjust device loads. If the targets of the two differ, devices may first be adjusted to the state required by remote instructions and then adjusted back to an inappropriate state by automatic control systems, resulting in mechanical stress, excessive energy consumption, and efficiency loss.

[0003] Prior art such as the invention patent with publication number CN118151634B, an intelligent monitoring method and system for industrial control system device operating states, relates to the technical field of industrial device monitoring, comprising: real-time collection of operating states and working condition parameters of the industrial control system device; real-time transmission of the operating states and working condition parameters to a data processing layer, which performs marking processing on the operating states and working condition parameters; analysis of the marked operating states and working condition parameters; during analysis, determination of whether the industrial control system device is in a low-efficiency operating state; during analysis, advance determination of fault hazards and predictive maintenance of existing fault hazards; during analysis, assessment of the stability of the industrial control system device, and issuance of a warning and repair when the stability of the industrial control system device is abnormal.

[0004] The prior art such as the patent for invention with announcement number CN103309347B is a multi-working condition process monitoring method based on sparse representation, and belongs to the technical field of industrial process monitoring and diagnosis. It is assumed that the normal running data of a working condition of a process is the same as the historical data distribution of the working condition. First, a dictionary is established according to the historical data of each working condition. Then, the sparse representation of online data on the dictionary is calculated, and whether the process is abnormal is judged according to the concentration degree of the representation coefficient. In addition, for normal data, it can also be identified that the process is currently in a single working condition or a transition process, so as to ensure that the product meets the production requirements.

[0005] Based on the above scheme, it can be seen that the prior art focuses on the prediction of fault hazards in this field, but the adaptability to complex systems is weak. In addition, the prior art expands the application scene through sparse representation, but still has certain limitations in the face of dynamic changes and multi-working condition transitions, thereby affecting the process of abnormal monitoring and detection. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides an air compression station unattended multi-working condition abnormality detection and regulation system and method. To achieve the above purpose, the present application is implemented by the following technical scheme: an air compression station unattended multi-working condition abnormality detection and regulation system, comprising: An abnormality monitoring module is used to collect the running parameters of each device of the air compression station in real time, monitor whether each device of the air compression station is abnormal, and if it is identified that a device has a demand intervention treatment abnormality, the device is recorded as an abnormal device, a remote intervention request is triggered, and the information and running parameters of the abnormal device are sent to the control module.

[0007] A control module is used to transmit the information and running parameters of the abnormal device to a remote intervention operation point through a communication interface after triggering the remote intervention request, and the remote intervention operation point issues an intervention control instruction.

[0008] A conflict detection module is used to identify the conflict between the intervention control instruction and the self-buffering action of the built-in automatic control system of the air compression station based on a conflict detection algorithm, and implement corresponding adjustment.

[0009] An adaptive compensation module is used to generate a dynamic remote correction coefficient based on the hardware state parameters of the abnormal device, and to perform adaptive compensation correction on the intervention control instruction.

[0010] As a preferred technical scheme, if it is identified that a device has a demand intervention treatment abnormality, the specific process is as follows: The demand intervention treatment abnormality refers to an abnormality of the device that cannot be automatically solved by the automatic adjustment system inside the air compression station. The specific determination conditions include: When any operating parameter of a device exceeds the set corresponding safety threshold, a self-resolution process is initiated. The operating parameter exceeding the corresponding safety threshold is extracted, and the operating deviation index between the operating parameter and the corresponding safety threshold is obtained. The operating deviation index is input into the automatic control system to drive the automatic control system to perform self-resolution. After the self-resolution process is completed, the device is tested and the operating parameters of the device are continuously monitored. If any operating parameter still exceeds the set corresponding safety threshold, the device is determined to require intervention and is recorded as an abnormal device.

[0011] The information and operating parameters of the abnormal device are sent to the control module. The information of the abnormal device includes the abnormal device model and abnormal device number.

[0012] As a preferred technical solution, a conflict detection algorithm is used to identify conflicts between intervention control commands and the self-buffering actions of the air compressor station's built-in automatic control system. The specific process is as follows: The system captures the adjustment actions of the built-in automatic control system of the air compressor station. The conflict detection algorithm analyzes the automatic adjustment target of the adjustment action and compares it with the intervention adjustment target of the remote intervention command. If the automatic adjustment target is the same as the intervention adjustment target, the system enters the conflict secondary judgment process. If the automatic adjustment target is different from the intervention adjustment target, the system is determined to be conflict-free.

[0013] The conflict secondary judgment process includes: The adjustment value in the automatic adjustment command of the adjustment action is retrieved, and the intervention value in the remote intervention command is obtained. The directionality of the values ​​is compared. If the directionality of the adjustment value in the automatic adjustment command of the adjustment action is different from that in the remote intervention command, it is determined that there is a conflict in the adjustment direction.

[0014] If the adjustment value in the automatic adjustment command of the adjustment action and the intervention value in the remote intervention command have the same directionality, then the adjustment value in the automatic adjustment command and the intervention value in the remote intervention command are coupled to obtain the total adjustment value of the abnormal device. The total adjustment value of the abnormal device is compared with a preset adjustment value threshold. If the total adjustment value of the abnormal device is greater than or equal to the preset adjustment value threshold, it is determined to be an overlap conflict. If the total adjustment value of the abnormal device is less than the preset adjustment value threshold, it is determined to be no conflict.

[0015] As a preferred technical solution, adjustments to conflicting directions are made, specifically including: The comprehensive adjustment range is obtained by combining the adjustment value in the automatic adjustment command of the adjustment action, the intervention value in the remote intervention command, and the real-time load, real-time temperature, and real-time current of the abnormal equipment. The adjustment rate is set based on the dynamic response parameters of the equipment. The adjustment rate is used to avoid drastic fluctuations in the equipment status. The dynamic response parameters include the load change rate and temperature response of the abnormal equipment.

[0016] Based on the overall adjustment range and adjustment rate, the abnormal equipment enters the buffer phase. The duration of the buffer phase is determined based on the response time of the abnormal equipment and the current load. During the buffer phase, the abnormal equipment is gradually adjusted based on the adjustment rate until the overall adjustment range is completed.

[0017] During the buffer phase, the key operating status parameters of the abnormal equipment are monitored in real time. If any key operating status parameter exceeds the preset normal operating status range, the adjustment is suspended and an emergency shutdown is initiated.

[0018] After the buffer phase is completed, the abnormal equipment enters the key monitoring cycle.

[0019] As a preferred technical solution, the key monitoring period also includes setting the length of the key monitoring period based on the difference between the key operating status parameters and the standard operating status parameters of the abnormal equipment during the buffer phase. Specifically, this includes: Extract the mean values ​​of key operating status parameters of the abnormal equipment during the buffer phase, compare the mean values ​​of each key operating status parameter with the preset corresponding standard values ​​of key operating status parameters, and obtain the key operating deviation index between the mean values ​​of each key operating status parameter and the corresponding standard values ​​of key operating status parameters.

[0020] The weighted factors of each key operating status parameter are extracted from the database. The weighted factors are used to characterize the degree of impact of deviation of a key operating status parameter on abnormal equipment.

[0021] The key operational deviation indices are fitted to their corresponding weighting factors and then superimposed to obtain the comprehensive key operational deviation index.

[0022] The critical operational deviation index of the abnormal equipment during the buffer phase is entered into a lookup table in the database that matches the deviation index with the key monitoring period length to obtain the key monitoring period length of the abnormal equipment.

[0023] As a preferred technical solution, overlapping conflicts specifically include: The system obtains the total adjustment difference between the total adjustment value of the abnormal device and the preset adjustment value threshold, obtains the automatic adjustment ratio between the adjustment value in the automatic adjustment command and the total adjustment value, obtains the intervention adjustment ratio between the intervention value in the remote intervention command and the total adjustment value, fits the automatic adjustment ratio and the intervention adjustment ratio with the total adjustment difference to obtain the automatic adjustment demand reduction value and the intervention adjustment demand reduction value, and applies them.

[0024] As a preferred technical solution, dynamic remote correction coefficients are generated based on the hardware status parameters of the malfunctioning device, specifically including: Obtain the hardware status parameters of abnormal devices, including device load fluctuation rate, temperature fluctuation rate, and system delay duration.

[0025] Dynamic remote correction coefficients are used to quantify the impact of delay on control commands.

[0026] The equipment load fluctuation rate of the abnormal equipment is input into the pre-stored mapping set of equipment load fluctuation rate - remote correction first sub-index in the database and mapped and matched to obtain the remote correction first sub-index of the abnormal equipment.

[0027] The temperature fluctuation rate of the abnormal device is input into the pre-stored mapping set of temperature fluctuation rate - remote correction second sub-index in the database and mapped and matched to obtain the remote correction second sub-index of the abnormal device.

[0028] The system delay duration of the abnormal device is input into the pre-stored mapping set of system delay duration - remote correction third sub-index in the database and matched to obtain the remote correction third sub-index of the abnormal device.

[0029] The dynamic remote correction coefficient of the abnormal device is obtained by weighted coupling of the first, second, and third remote correction sub-indices of the abnormal device.

[0030] As a preferred technical solution, adaptive compensation and correction are performed on the intervention control commands, with the specific processing conditions being: The dynamic remote correction coefficient of the abnormal equipment is input into the automatic control system to make a preliminary correction to the intervention control command. The dynamic remote correction coefficient of the abnormal equipment is then fitted with the intervention value in the intervention control command to obtain the corrected intervention value.

[0031] The corrected intervention values ​​are applied to the target adjustment parameters of the abnormal equipment, and real-time dynamic correction is performed. Based on the key operational comprehensive deviation index in the post-application buffer stage, the corrected intervention values ​​are dynamically corrected in real time.

[0032] As a preferred technical solution, based on the key operational comprehensive deviation index in the post-application buffer phase, the corrected intervention values ​​are dynamically adjusted in real time, specifically including: The key operational comprehensive deviation index of the buffer phase is obtained. It is then input into the preset mapping set of key operational comprehensive deviation index-dynamic remote correction coefficient real-time dynamic adjustment value in the database to obtain the dynamic remote correction coefficient real-time dynamic adjustment value. Finally, it is fitted with the corrected intervention value to obtain the real-time corrected intervention value.

[0033] In addition, a method for unmanned multi-condition anomaly detection and control of air compressor stations is also provided, including: The system collects the operating parameters of each device in the air compressor station in real time, monitors whether any abnormality occurs in the device, and if an abnormality requiring intervention is detected, the device is marked as abnormal, a remote intervention request is triggered, and the information and operating parameters of the abnormal device are sent to the control module.

[0034] After a remote intervention request is triggered, the information and operating parameters of the abnormal device are transmitted to the remote intervention operation point through the communication interface, and the remote intervention operation point issues intervention control commands.

[0035] Based on the conflict detection algorithm, the conflict between the intervention control command and the self-buffering action of the built-in automatic control system of the air compressor station is identified, and corresponding adjustments are made.

[0036] Dynamic remote correction coefficients are generated based on the hardware status parameters of abnormal devices, and adaptive compensation corrections are performed on intervention control commands.

[0037] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects: (1) This invention provides an unattended multi-condition anomaly detection and control system for air compressor stations. Through an anomaly monitoring module, the system collects and analyzes the operating parameters of each piece of equipment in the air compressor station in real time, enabling rapid identification of equipment faults and anomalies. It automatically determines whether equipment has an anomaly that cannot be resolved independently and triggers a remote intervention request, greatly reducing the need for manual intervention and improving the automation level of the air compressor station. Simultaneously, the system ensures timely response and handling of anomalies through a remote operation module, thereby ensuring the continuous and stable operation of the air compressor station and avoiding equipment downtime or worsening of faults due to delayed manual response. This efficient and automated monitoring and control greatly enhances the intelligent operation of the air compressor station, reduces human error, and improves production efficiency.

[0038] (2) This invention uses a conflict detection algorithm to accurately determine the conflict between the automatic adjustment target and the remote intervention target, avoiding inconsistent adjustment commands from both to the equipment. This not only protects the equipment from mechanical damage caused by frequent adjustments, but also ensures the stability and efficiency of the air compressor station equipment. The conflict detection module ensures a smooth transition of the equipment during the buffer phase through gradual adjustment and real-time monitoring, avoiding the risk of equipment overload or excessive adjustment, and further improving the operational safety of the equipment.

[0039] (3) This invention, through an adaptive compensation module, can generate dynamic remote correction coefficients based on the hardware status parameters of the equipment. These correction coefficients effectively eliminate the potential delays between the issuance and execution of remote commands. Specifically, by quantifying the equipment load fluctuation rate, temperature fluctuation rate, and systemic delay duration, the system can finely correct intervention control commands, ensuring a high degree of consistency between the commands and the actual equipment state. This adaptive correction method can adjust intervention commands in real time, avoiding inaccurate or excessive equipment adjustments due to command delays, thereby improving the system's response speed and accuracy, and further reducing energy waste and equipment damage.

[0040] (4) This invention dynamically adjusts the monitoring cycle length based on the difference between the operating parameters of abnormal equipment and the standard operating state parameters. Through precise analysis of key operating state parameters, the system can ensure that the equipment status remains within a safe range when the equipment enters the key monitoring phase. The key monitoring cycle also includes a weighted analysis of the deviation of key operating parameters, thereby formulating the most suitable maintenance and adjustment strategy. This precise control and monitoring method can not only effectively extend the service life of the equipment, but also reduce downtime and maintenance costs caused by equipment failure. In addition, the system's dynamic correction and buffer phase adjustment mechanism ensures that the equipment is always in the optimal operating state, further reducing the overall operating cost of the air compressor station.

[0041] Of course, any product implementing this invention does not necessarily need to achieve all of the above advantages at the same time. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the system modules of the present invention.

[0043] Figure 2 This is a schematic diagram of the method flow of the present invention.

[0044] Figure 3 This is a schematic diagram of the logic flow of the present invention.

[0045] Figure 4 This is a continuation of the logical flow diagram of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see Figure 1 As shown, this embodiment of the invention provides an unattended multi-condition anomaly detection and control system for air compressor stations, comprising: like Figure 3 and Figure 4 The diagram shows the logic flowchart of an embodiment of the present invention. The system collects the operating parameters of each device in real time and monitors whether any device malfunctions according to a set safety threshold. If the operating parameters of a device exceed the preset safety threshold, an intervention process is initiated. At this time, the device is marked as an abnormal device, and the control module is triggered to transmit the device information and parameters to the remote intervention operation point. After receiving the device information, the remote intervention operation point generates an intervention control command based on the transmitted parameters and begins remote intervention. During this process, conflict detection is performed between the intervention command and the automatic control system built into the air compressor station. When a conflict is detected, a dynamic correction coefficient is generated through the adaptive compensation module to correct the intervention control command. After entering the buffer phase, the state of the device is gradually adjusted to avoid any sudden and drastic fluctuations. During this phase, the system continuously monitors the key operating parameters of the device to ensure that all parameters are within the normal range. If some parameters of the device are found to exceed the preset normal range during the adjustment process, the system will immediately trigger an emergency shutdown operation to ensure the safety of the device. After the buffer phase ends, if the device has returned to a stable state, the system will transfer it to the key monitoring cycle. During this cycle, the system will dynamically adjust the length of the monitoring cycle based on the differences between the key operating parameters and standard values ​​of the equipment during the buffer phase, and conduct long-term status monitoring to ensure that the equipment is always in a safe and efficient operating state throughout the entire cycle.

[0048] The anomaly monitoring module is used to collect the operating parameters of each device in the air compressor station in real time, monitor whether any abnormality occurs in the device, and if an abnormality requiring intervention is detected in a certain device, the device is recorded as an abnormal device, a remote intervention request is triggered, and the information and operating parameters of the abnormal device are sent to the control module.

[0049] An abnormality in the handling of a request for intervention was detected in a certain device. The specific process is as follows: Demand intervention handling anomaly refers to an anomaly in the equipment that cannot be automatically resolved by the automatic adjustment system inside the air compressor station. Specific judgment criteria include: When any operating parameter of a device exceeds a set safety threshold, a self-resolution process is initiated. This process extracts the operating parameter exceeding the safety threshold, calculates the difference between the parameter and the threshold to obtain an operating deviation index, and inputs this index into the automatic control system to drive self-resolution. For example, if the device's pressure exceeds a set upper limit (where the safety threshold is 10 MPa and the current pressure is 12 MPa), the deviation index is 2, reflecting the difference between the current pressure and the set value. After the self-resolution process, the device undergoes a trial run, continuously monitoring its operating parameters. If any operating parameter still exceeds the set safety threshold, the device is deemed to require intervention and is recorded as an abnormal device.

[0050] For example, suppose a compressor's temperature continues to rise during operation, exceeding the set safety limit (the threshold is 85°C, while the actual temperature is 90°C). The system initiates a self-resolution process, attempting to lower the temperature, such as by reducing the load or adjusting the cooling system. However, after adjustments, the temperature still fails to drop to a safe range and continues to exceed the set threshold. At this point, the system determines that the equipment requires manual intervention and marks it as an abnormal device.

[0051] The information and operating parameters of the abnormal device are sent to the control module. The information of the abnormal device includes the abnormal device model and abnormal device number.

[0052] The control module is used to transmit the information and operating parameters of the abnormal device to the remote intervention point through the communication interface after triggering a remote intervention request. The remote intervention point then issues intervention control commands.

[0053] In this embodiment of the invention, the system adopts a unified data encoding format (such as JSON, XML) and implements low-latency real-time communication in the system to ensure that the status of the device can be updated to the remote operator in real time.

[0054] After receiving information and operating parameters from the malfunctioning equipment, the remote intervention point issues intervention control commands. All operations are recorded in the operation log, serving as a basis for subsequent fault analysis and equipment maintenance. Command logging and fault analysis provide data support for the design and optimization of mapping sets within the system, helping to analyze equipment failure modes and propose optimization solutions to reduce the likelihood of future failures.

[0055] The conflict detection module is used to identify conflicts between intervention control commands and the self-buffering actions of the air compressor station's built-in automatic control system based on a conflict detection algorithm, and to implement corresponding adjustments.

[0056] The system captures the adjustment actions of the air compressor station's built-in automatic control system. These automatic adjustment actions are the behaviors of the system in automatically adjusting equipment operating parameters based on real-time data (such as pressure, temperature, and load). Through these automatic adjustment actions, the system ensures that the equipment can automatically adjust according to environmental changes and load demands. The automatic control system typically adjusts equipment operating parameters automatically based on real-time data (such as pressure, temperature, and load), for example, automatically adjusting the compressor's speed or load. A conflict detection algorithm analyzes the automatic adjustment target of the adjustment action and compares it with the intervention adjustment target of a remote intervention command. The remote intervention command is issued by the air compressor station operator via remote communication and is typically used to manually intervene in equipment operation in abnormal or complex situations that the automatic control system cannot handle. The command may involve adjusting parameters such as equipment load, temperature, and speed. If the automatic adjustment target and the intervention adjustment target are the same, a secondary conflict judgment process is initiated; if they are different, it is determined that there is no conflict.

[0057] The adjustment target is the value set by the automatic adjustment system or remote intervention commands. The automatic control system determines the adjustment target based on real-time data from the equipment, while remote operation sets the target value through intervention commands to make corresponding adjustments.

[0058] The conflict secondary judgment process is used to ensure coordination between remote intervention and automatic control systems for abnormal equipment, preventing equipment instability or damage caused by the same target but different adjustment directions or excessively high total adjustment values. Specifically, it includes: The system retrieves the adjustment value from the automatic adjustment command and the intervention value from the remote intervention command, and compares their directions. If the directions of the adjustment value in the automatic adjustment command and the intervention value in the remote intervention command are different, it is determined that there is a conflict in the adjustment direction. This comparison of numerical directionality is used to identify directional conflicts between different commands, preventing the equipment from making opposite adjustments in terms of load, speed, etc., which could lead to instability or damage.

[0059] If the adjustment value in the automatic adjustment command and the intervention value in the remote intervention command have the same directionality, then the adjustment value in the automatic adjustment command and the intervention value in the remote intervention command are coupled to obtain the total adjustment value of the abnormal equipment. The coupled adjustment value is the total adjustment value obtained by adding the adjustment value of the automatic adjustment system and the adjustment value of the remote intervention command. This represents the total adjustment amount of the equipment, combining the effects of automatic adjustment and remote intervention. By coupling the adjustment value, considering the combined effect of automatic control and remote intervention, it is determined whether further adjustments or measures are needed to prevent the total adjustment amount from being too large and causing equipment instability.

[0060] The total adjustment value of the abnormal device is compared with the preset adjustment value threshold. If the total adjustment value of the abnormal device is greater than or equal to the preset adjustment value threshold, it is determined to be an overlap conflict. If the total adjustment value of the abnormal device is less than the preset adjustment value threshold, it is determined to be no conflict.

[0061] For example, a conflict arises if a remote command requests an increase in load while the automatic control system is reducing the load, or if a remote command requests a change in equipment speed while the automatic control system is already adjusting the speed.

[0062] Adjusting directional conflicts, specifically including: Directional conflicts typically occur because the adjustment commands of the automatic control system and the remote intervention commands are inconsistent in their directions regarding the control objective. For example, when the automatic control system's adjustment command requires increasing the load, while the remote intervention command requires decreasing the load, or vice versa, this conflict can lead to frequent equipment fluctuations, decreased efficiency, or even damage. Therefore, when facing directional conflicts, certain strategies are needed to adjust and resolve them to ensure equipment stability.

[0063] Adjusting directional conflicts, specifically including: The comprehensive adjustment range is obtained by combining the adjustment values ​​in the automatic adjustment command and the intervention values ​​in the remote intervention command with the real-time load, real-time temperature, and real-time current of the abnormal equipment. Specifically, this includes: It should be noted that if the load on the malfunctioning equipment is too high, it means that the equipment needs more adjustment to reduce the load, and the adjustment range should be increased; if the load is low, the adjustment range should be appropriately reduced. When the temperature is too high, stronger cooling adjustment is needed, and the adjustment range should be increased; if the temperature is low, the adjustment range can be decreased. Excessive current means that the malfunctioning equipment is overloaded or has malfunctioned, requiring an increase in the adjustment range; if the current is low, the adjustment range may need to be reduced.

[0064] Retrieve lookup tables for the real-time load-comprehensive regulation amplitude load impact index, real-time temperature-comprehensive regulation amplitude temperature impact index, and real-time current-comprehensive regulation amplitude current impact index from the database. Based on the real-time load, real-time temperature, and real-time current of the abnormal equipment, find the comprehensive regulation amplitude load impact index, comprehensive regulation amplitude temperature impact index, and comprehensive regulation amplitude current impact index.

[0065] It should be noted that the comprehensive regulation amplitude load influence index reflects the impact of load on the regulation amplitude, and is usually converted into a regulation amplitude change using a lookup table. The comprehensive regulation amplitude temperature influence index reflects the impact of temperature on the regulation amplitude, and the lookup table provides the degree of influence of temperature on the regulation amplitude based on the real-time temperature value. The comprehensive regulation amplitude current influence index reflects the impact of current on the regulation amplitude, and the lookup table provides the direction and magnitude of the regulation amplitude adjustment based on the current value.

[0066] The comprehensive regulation amplitude influence index is obtained by superimposing the comprehensive regulation amplitude load influence index, the comprehensive regulation amplitude temperature influence index, and the comprehensive regulation amplitude current influence index. If the comprehensive regulation amplitude influence index exceeds the preset influence index threshold, it is multiplied by the intervention value in the remote intervention command to obtain the comprehensive regulation amplitude.

[0067] If the overall adjustment range influence index is less than or equal to the preset influence index threshold, it is multiplied by the adjustment value in the automatic adjustment command to obtain the overall adjustment range.

[0068] It should be noted that in cases of minor conflicts, the automatic adjustment command serves as the primary control signal, making minor adjustments to maintain stable equipment operation. In cases of severe conflicts, remote intervention commands are executed first to ensure that equipment problems are resolved promptly.

[0069] The adjustment rate is set based on the dynamic response parameters of the equipment. The adjustment rate is used to avoid drastic fluctuations in the equipment status. The dynamic response parameters include the load change rate and temperature response of abnormal equipment.

[0070] It should be noted that the adjustment value refers to the specific operational quantity required by the command, usually the adjustment range of a certain parameter of the equipment. Real-time load refers to the workload of the abnormal equipment at the current moment. Real-time temperature refers to the current operating temperature of the abnormal equipment. Real-time current refers to the current consumption of the abnormal equipment, reflecting the power consumption and load status of the equipment. The comprehensive adjustment range is a comprehensive adjustment amount calculated based on the adjustment value in the automatic adjustment command and the intervention value in the remote intervention command, combined with the real-time operating parameters of the abnormal equipment (such as load, temperature, current, etc.). It integrates the adjustment objectives of the two systems to ensure that the adjustment action does not lead to equipment instability. The calculation of the comprehensive adjustment range ensures that the equipment can make reasonable adjustments according to the common requirements of automatic adjustment and remote intervention commands. This can avoid equipment damage or efficiency reduction caused by over-adjustment or conflict. The adjustment rate refers to the speed at which the equipment responds to the adjustment command, representing the rate at which the equipment adjusts operating parameters (such as load, temperature, speed, etc.) within a certain period of time. The adjustment rate is set to avoid drastic fluctuations in equipment status, especially when dealing with dynamic parameter changes such as load or temperature. If the adjustment is too fast, the equipment may cause drastic fluctuations in load or temperature due to over-adjustment, thereby affecting equipment stability. By setting a reasonable adjustment rate, the equipment can be adjusted smoothly, ensuring that it gradually transitions to the new state during the adjustment process and reducing instability factors.

[0071] Dynamic response parameters describe the speed at which equipment responds to adjustments. Load change rate refers to the rate at which the equipment load changes, reflecting the speed at which the equipment transitions from one load state to another. If the load changes too quickly, it may lead to equipment overload or damage. Temperature response refers to the rate at which the equipment temperature changes, reflecting how the temperature changes over time during adjustment. Rapid temperature changes may cause the equipment to overheat, affecting its lifespan and stability. For example, if the equipment temperature rises rapidly, the system will adopt a gentler adjustment method to reduce drastic temperature changes. By setting an appropriate adjustment rate, the system can avoid drastic load or temperature fluctuations, thereby reducing equipment failures or efficiency losses caused by overly rapid adjustments. An appropriate adjustment rate ensures a smooth transition of the equipment to a new operating state, maintaining the long-term stability of the equipment.

[0072] Based on the overall adjustment range and adjustment rate, the abnormal equipment enters the buffer phase. The duration of the buffer phase is determined based on the response time of the abnormal equipment and the current load. During the buffer phase, the abnormal equipment is gradually adjusted based on the adjustment rate until the overall adjustment range is completed.

[0073] It should be noted that the buffer phase refers to a gradual adjustment phase adopted by the system during the adjustment process after the equipment enters an abnormal state. During the buffer phase, the equipment adjustment process is not carried out immediately but gradually to avoid drastic fluctuations in the equipment state. The purpose of the buffer phase is to gradually adjust the equipment to the predetermined normal state through a smooth transition, avoiding equipment instability, overload, or failure caused by too rapid adjustment.

[0074] During the buffer phase, the key operating status parameters of the abnormal equipment are monitored in real time. If any key operating status parameter exceeds the preset normal operating status range, the adjustment is suspended and an emergency shutdown is initiated.

[0075] After the buffer phase is completed, the abnormal equipment enters the key monitoring cycle.

[0076] The key monitoring period also includes setting the length of the key monitoring period based on the difference between the key operating status parameters and the standard operating status parameters of abnormal equipment during the buffer phase. Specifically, it includes: Extract the mean value of each key operating status parameter of the abnormal equipment during the buffer phase, and take the absolute value of the difference between the mean value of each key operating status parameter and the corresponding preset standard value of the key operating status parameter to obtain the key operating deviation index between the mean value of each key operating status parameter and the corresponding standard value of the key operating status parameter.

[0077] Weighting factors for each key operating status parameter are extracted from the database. These weighting factors characterize the impact of deviations in a key operating status parameter on abnormal equipment. Historical operating data of the air compressor station equipment is obtained, and regression analysis is performed to determine the weighting factors.

[0078] The key operational deviation indices are fitted to their corresponding weighting factors and then superimposed to obtain the comprehensive key operational deviation index.

[0079] The critical operational deviation index of the abnormal equipment during the buffer phase is entered into a lookup table in the database that matches the deviation index with the key monitoring period length to obtain the key monitoring period length of the abnormal equipment.

[0080] It should be noted that critical operating status parameters are essential indicators for equipment operation and vary depending on the specific equipment experiencing abnormalities. For example, critical operating status parameters for one piece of equipment may include load, temperature, and pressure, while for another equipment they may include voltage, current, and power. Quasi-operating status parameters are target values ​​or preset ideal values ​​that the equipment should achieve during normal and stable operation. These standard values ​​are usually obtained through manufacturer testing or accumulated experience and are used as a reference for the normal operation of the equipment.

[0081] The critical operational deviation index measures the deviation of critical operational parameters of equipment from standard values ​​during the buffer phase. A larger deviation index value indicates greater instability in the equipment's condition. The weighting factor is a coefficient used to reflect the degree of impact of a deviation of a critical operational parameter on the overall equipment condition. Each critical operational parameter has a different weighting factor. By quantifying the impact of each parameter and combining it with the deviation index, a weighted total deviation value is obtained.

[0082] In this embodiment of the invention, the lookup table is a database structure that stores the mapping relationship between the comprehensive deviation index and the corresponding monitoring period length.

[0083] Overlapping conflicts, specifically including: The system obtains the total adjustment difference between the total adjustment value of the abnormal device and the preset adjustment value threshold, obtains the automatic adjustment ratio between the adjustment value in the automatic adjustment command and the total adjustment value, obtains the intervention adjustment ratio between the intervention value in the remote intervention command and the total adjustment value, fits the automatic adjustment ratio and the intervention adjustment ratio with the total adjustment difference to obtain the automatic adjustment demand reduction value and the intervention adjustment demand reduction value, and applies them.

[0084] By acquiring the adjustment difference and fitting the ratio of automatic adjustment to intervention adjustment, conflicts between the automatic control system and remote intervention commands can be effectively avoided, ensuring a more stable and balanced equipment adjustment process. This prevents frequent ineffective adjustments, reduces mechanical wear, improves operating efficiency, and extends equipment lifespan. Precise adjustment strategies can also optimize energy consumption, reduce excessive energy consumption, and keep equipment operating at its best, thus lowering maintenance costs.

[0085] The adaptive compensation module is used to generate dynamic remote correction coefficients based on the hardware status parameters of abnormal devices and to adaptively compensate and correct intervention control commands.

[0086] It should be noted that during remote intervention at an air compressor station, there is typically a time delay when abnormal equipment is detected and corrective commands are issued. This delay may be caused by transmission latency, human decision-making delays, or communication network issues. Therefore, to ensure that remote control commands can accurately and promptly respond to changes in equipment status and to eliminate the impact of delays, a dynamic remote correction coefficient must be designed. This correction coefficient can be dynamically generated based on the equipment's hardware status parameters (such as load, temperature, and pressure) to refine control commands and ensure stable equipment operation.

[0087] Obtain the hardware status parameters of abnormal devices, including device load fluctuation rate, temperature fluctuation rate, and system delay duration.

[0088] Dynamic remote correction coefficients are used to quantify the impact of delay on control commands.

[0089] The equipment load fluctuation rate of the abnormal equipment is input into the pre-stored mapping set of equipment load fluctuation rate - remote correction first sub-index in the database and mapped and matched to obtain the remote correction first sub-index of the abnormal equipment.

[0090] The temperature fluctuation rate of the abnormal device is input into the pre-stored mapping set of temperature fluctuation rate - remote correction second sub-index in the database and mapped and matched to obtain the remote correction second sub-index of the abnormal device.

[0091] The system delay duration of the abnormal device is input into the pre-stored mapping set of system delay duration - remote correction third sub-index in the database and matched to obtain the remote correction third sub-index of the abnormal device.

[0092] The dynamic remote correction coefficient of the abnormal device is obtained by weighted coupling of the first, second, and third remote correction sub-indices of the abnormal device.

[0093] The dynamic remote correction coefficient of the abnormal equipment is input into the automatic control system to make a preliminary correction to the intervention control command. The dynamic remote correction coefficient of the abnormal equipment is then fitted with the intervention value in the intervention control command to obtain the corrected intervention value.

[0094] The corrected intervention values ​​are applied to the target adjustment parameters of the abnormal equipment, and real-time dynamic correction is performed. Based on the key operational comprehensive deviation index in the post-application buffer stage, the corrected intervention values ​​are dynamically corrected in real time.

[0095] The key operational comprehensive deviation index of the buffer phase is obtained. It is then input into the preset mapping set of key operational comprehensive deviation index-dynamic remote correction coefficient real-time dynamic adjustment value in the database to obtain the dynamic remote correction coefficient real-time dynamic adjustment value. Finally, it is fitted with the corrected intervention value to obtain the real-time corrected intervention value.

[0096] Please see Figure 2 A method for unmanned multi-condition anomaly detection and control of air compressor stations, specifically including: The system collects the operating parameters of each device in the air compressor station in real time, monitors whether any abnormality occurs in the device, and if an abnormality requiring intervention is detected, the device is marked as abnormal, a remote intervention request is triggered, and the information and operating parameters of the abnormal device are sent to the control module.

[0097] After a remote intervention request is triggered, the information and operating parameters of the abnormal device are transmitted to the remote intervention operation point through the communication interface, and the remote intervention operation point issues intervention control commands.

[0098] Based on the conflict detection algorithm, the conflict between the intervention control command and the self-buffering action of the built-in automatic control system of the air compressor station is identified, and corresponding adjustments are made.

[0099] Dynamic remote correction coefficients are generated based on the hardware status parameters of abnormal devices, and adaptive compensation corrections are performed on intervention control commands.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementation methods. Clearly, many modifications and variations can be made based on the content of this specification. The selection and detailed description of these embodiments in this specification are intended to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. Any modifications or variations that do not deviate from the structure of the invention or exceed the scope defined by the invention should fall within the protection scope of the invention.

Claims

1. A multi-condition anomaly detection and control system for an unattended air compressor station, characterized in that, include: The anomaly monitoring module is used to collect the operating parameters of each device in the air compressor station in real time, monitor whether any abnormality occurs in the device, and if an abnormality requiring intervention is detected in a certain device, the device is recorded as an abnormal device, a remote intervention request is triggered, and the information and operating parameters of the abnormal device are sent to the control module. The control module is used to transmit the information and operating parameters of the abnormal device to the remote intervention point through the communication interface after triggering the remote intervention request. The remote intervention point then issues intervention control commands. The conflict detection module is used to identify conflicts between intervention control commands and the self-buffering actions of the air compressor station's built-in automatic control system based on a conflict detection algorithm, and to implement corresponding adjustments. The adaptive compensation module is used to generate dynamic remote correction coefficients based on the hardware status parameters of abnormal devices and to adaptively compensate and correct intervention control commands.

2. The unmanned multi-condition anomaly detection and control system for air compressor stations according to claim 1, characterized in that: The specific process for identifying an abnormality in the intervention handling of a certain device is as follows: The aforementioned demand intervention handling anomaly refers to an anomaly occurring in the equipment that cannot be automatically resolved by the automatic adjustment system within the air compressor station. Specific judgment criteria include: When any operating parameter of a device exceeds the set corresponding safety threshold, a self-resolution process is initiated. The operating parameter that exceeds the corresponding safety threshold is extracted, and the operating deviation index between the operating parameter and the corresponding safety threshold is obtained. The operating deviation index is input into the automatic control system to drive the automatic control system to perform self-resolution. After the self-resolution process is completed, the device is tested and the operating parameters of the device are continuously monitored. If any operating parameter still exceeds the set corresponding safety threshold, the device is determined to require intervention and is recorded as an abnormal device. The information and operating parameters of the abnormal device are sent to the control module. The information of the abnormal device includes the abnormal device model and abnormal device number.

3. The unmanned multi-condition anomaly detection and control system for air compressor stations according to claim 1, characterized in that: The conflict detection algorithm identifies conflicts between intervention control commands and the self-buffering actions of the air compressor station's built-in automatic control system. The specific process is as follows: The system captures the adjustment actions of the built-in automatic control system of the air compressor station. The conflict detection algorithm analyzes the automatic adjustment target of the adjustment action and compares it with the intervention adjustment target of the remote intervention command. If the automatic adjustment target is the same as the intervention adjustment target, the system enters the conflict secondary judgment process. If the automatic adjustment target is different from the intervention adjustment target, the system is determined to be conflict-free. The conflict secondary judgment process includes: The adjustment value in the automatic adjustment command of the adjustment action is retrieved, and the intervention value in the remote intervention command is obtained at the same time. The directionality of the values ​​is compared. If the directionality of the adjustment value in the automatic adjustment command of the adjustment action is different from that of the intervention value in the remote intervention command, it is determined that there is a conflict in the adjustment direction. If the adjustment value in the automatic adjustment command of the adjustment action and the intervention value in the remote intervention command have the same directionality, then the adjustment value in the automatic adjustment command and the intervention value in the remote intervention command are coupled to obtain the total adjustment value of the abnormal device. The total adjustment value of the abnormal device is compared with a preset adjustment value threshold. If the total adjustment value of the abnormal device is greater than or equal to the preset adjustment value threshold, it is determined to be an overlap conflict. If the total adjustment value of the abnormal device is less than the preset adjustment value threshold, it is determined to be no conflict.

4. The unmanned multi-condition anomaly detection and control system for air compressor stations according to claim 3, characterized in that: The aforementioned conflict in adjustment direction specifically includes: The comprehensive adjustment range is obtained by combining the adjustment value in the automatic adjustment command of the adjustment action, the intervention value in the remote intervention command, and the real-time load, real-time temperature, and real-time current of the abnormal equipment. The adjustment rate is set based on the dynamic response parameters of the equipment. The adjustment rate is used to avoid drastic fluctuations in the equipment status. The dynamic response parameters include the load change rate and temperature response of the abnormal equipment. Based on the comprehensive adjustment range and adjustment rate, the abnormal equipment enters the buffer phase. The duration of the buffer phase is obtained based on the response time of the abnormal equipment and the current load. During the buffer phase, the abnormal equipment is gradually adjusted based on the adjustment rate until the comprehensive adjustment range is completed. During the buffer phase, the key operating status parameters of the abnormal equipment are monitored in real time. If any key operating status parameter exceeds the preset normal operating status range, the adjustment is suspended and an emergency shutdown is initiated. After the buffer phase is completed, the abnormal equipment enters the key monitoring cycle.

5. The unmanned multi-condition anomaly detection and control system for air compressor stations according to claim 4, characterized in that: The key monitoring period also includes setting the length of the key monitoring period based on the difference between the key operating status parameters and the standard operating status parameters of the abnormal equipment during the buffer phase. Specifically, it includes: Extract the mean value of each key operating status parameter of the abnormal equipment during the buffer phase, compare the mean value of each key operating status parameter with the preset corresponding standard value of the key operating status parameter, and obtain the key operating deviation index between the mean value of each key operating status parameter and the corresponding standard value of the key operating status parameter. The weighting factors of each key operating status parameter are extracted from the database. The weighting factors are used to characterize the degree of impact of the deviation of a key operating status parameter on the abnormal equipment. The key operational deviation indices are fitted to their corresponding weighting factors and then superimposed to obtain the comprehensive key operational deviation index. The critical operational deviation index of the abnormal equipment during the buffer phase is entered into a lookup table in the database that matches the deviation index with the key monitoring period length to obtain the key monitoring period length of the abnormal equipment.

6. The unmanned multi-condition anomaly detection and control system for air compressor stations according to claim 3, characterized in that: The overlapping conflicts specifically include: The total adjustment difference between the total adjustment value of the abnormal device and the preset adjustment value threshold is obtained. The automatic adjustment ratio between the adjustment value in the automatic adjustment command of the adjustment action and the total adjustment value is obtained. At the same time, the intervention adjustment ratio between the intervention value in the remote intervention command and the total adjustment value is obtained. The automatic adjustment ratio and the intervention adjustment ratio are fitted with the total adjustment difference to obtain the automatic adjustment demand reduction value and the intervention adjustment demand reduction value, and then applied.

7. The unmanned multi-condition anomaly detection and control system for an air compressor station according to claim 1, characterized in that: The generation of dynamic remote correction coefficients based on the hardware status parameters of the faulty device specifically includes: Obtain the hardware status parameters of abnormal devices, including device load fluctuation rate, temperature fluctuation rate, and system delay duration; The dynamic remote correction coefficient is used to quantify the impact of delay on control commands; The equipment load fluctuation rate of abnormal equipment is input into the pre-stored mapping set of equipment load fluctuation rate-remote correction first sub-index in the database and the mapping is matched to obtain the remote correction first sub-index of abnormal equipment. The temperature fluctuation rate of the abnormal device is input into the pre-stored mapping set of temperature fluctuation rate-remote correction second sub-index in the database and matched to obtain the remote correction second sub-index of the abnormal device. The system delay duration of the abnormal device is input into the pre-stored mapping set of system delay duration - remote correction third sub-index in the database and the mapping is matched to obtain the remote correction third sub-index of the abnormal device. The dynamic remote correction coefficient of the abnormal device is obtained by weighted coupling of the first, second, and third remote correction sub-indices of the abnormal device.

8. The unmanned multi-condition anomaly detection and control system for air compressor stations according to claim 1, characterized in that: The specific processing conditions for adaptive compensation and correction of the intervention control command are as follows: The dynamic remote correction coefficient of the abnormal equipment is input into the automatic control system to make a preliminary correction to the intervention control command. The dynamic remote correction coefficient of the abnormal equipment is then fitted with the intervention value in the intervention control command to obtain the corrected intervention value. The corrected intervention values ​​are applied to the target adjustment parameters of the abnormal equipment, and real-time dynamic correction is performed. Based on the key operational comprehensive deviation index in the post-application buffer stage, the corrected intervention values ​​are dynamically corrected in real time.

9. The unmanned multi-condition anomaly detection and control system for an air compressor station according to claim 8, characterized in that: The key operational deviation index based on the post-application buffer phase is used to dynamically correct the intervention values ​​in real time, specifically including: The key operational comprehensive deviation index of the buffer phase is obtained. It is then input into the preset mapping set of key operational comprehensive deviation index-dynamic remote correction coefficient real-time dynamic adjustment value in the database to obtain the dynamic remote correction coefficient real-time dynamic adjustment value. Finally, it is fitted with the corrected intervention value to obtain the real-time corrected intervention value.

10. A method for an unattended multi-condition anomaly detection and control system for an air compressor station as described in any one of claims 1-9, characterized in that: The system collects the operating parameters of each device in the air compressor station in real time, monitors whether any abnormality occurs in the device, and if an abnormality is detected in a device that requires intervention, the device is marked as abnormal, a remote intervention request is triggered, and the information and operating parameters of the abnormal device are sent to the control module. After a remote intervention request is triggered, the information and operating parameters of the abnormal device are transmitted to the remote intervention operation point through the communication interface, and the remote intervention operation point issues intervention control commands. Based on the conflict detection algorithm, the conflict between the intervention control command and the self-buffering action of the built-in automatic control system of the air compressor station is identified, and corresponding adjustments are made. Dynamic remote correction coefficients are generated based on the hardware status parameters of abnormal devices, and adaptive compensation corrections are performed on intervention control commands.

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