Distributed valve body remote control system

Abstract

The invention relates to the technical field of distributed control systems, and discloses a distributed valve body remote control system which comprises a valve body state acquisition module, a remote communication analysis module and the like. The valve body state acquisition module acquires parameters such as opening, fluid pressure and surface temperature of a multi-area valve body according to a monitoring period, and a high-frequency monitoring mode can be started; the remote communication analysis module evaluates communication delay; the distributed coordination module synchronizes parameters when communication is normal, and calculates a dynamic regulation factor through a formula to generate a regulation instruction; the instruction distribution module determines an instruction distribution sequence according to a priority weight formula, and is provided with delay and forced distribution strategies; and the abnormal response module gives an alarm and marks the position code of the valve body when the communication delay exceeds the limit or the weight is continuously too low. And the system also checks and complements the acquired data. The system improves the monitoring control precision and reliability, and is suitable for remote monitoring of multi-area valve bodies.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed control systems, and in particular to a distributed valve body remote control system. Background Art

[0002] In the field of industrial automation, distributed valve systems are widely used in scenarios such as petrochemicals, energy and power generation, and water conservancy projects. Their operational stability and control efficiency directly impact production safety and economic benefits. Traditional valve control systems typically employ a centralized architecture, with each valve connected to the control center via an independent communication link. This model presents numerous drawbacks when dealing with large-scale, cross-regional valve networks.

[0003] Centralized architectures suffer from low communication efficiency. As the number of valves increases and their distribution expands, the transmission of large amounts of status data over a single link can easily lead to communication congestion, significantly increasing data synchronization delays and making it difficult to reflect the actual operating status of each valve in real time. This is especially true in multi-region collaborative control scenarios, where communication delays between adjacent valves can cause asynchronous execution of control commands, impacting the overall coordination of the system. For example, in an oil pipeline network, if valves in different regions cannot be adjusted synchronously due to communication delays, this can lead to pipeline pressure imbalances and even safety incidents.

[0004] Traditional systems lack monitoring capabilities. Existing monitoring methods often rely on fixed-cycle sampling, which is unable to dynamically adjust the monitoring frequency based on the valve's actual operating status. When individual valve parameters exhibit abnormalities, fixed-cycle sampling may not capture subtle changes in a timely manner, resulting in delayed detection of potential faults. For example, a slowly rising valve surface temperature could be a precursor to wear on internal components. However, with fixed-cycle monitoring, if the abnormality is not significant, it may go unnoticed and eventually develop into a serious fault.

[0005] The control command distribution strategy lacks flexibility. Traditional systems typically distribute commands using fixed priorities or a round-robin approach, failing to fully consider multiple factors such as the valve's real-time operating status, regional load, and historical fault conditions. This can delay the execution of commands for critical valves, while non-critical valves consume excessive resources, impacting overall system control efficiency. For example, in a power system, if valves responsible for important transmission lines fail to adjust in a timely manner due to improper priority settings, this can cause local grid overload and expand the scope of the fault.

[0006] Abnormal response mechanisms are inadequate. Traditional systems often only issue simple alarms when detecting communication failures or valve anomalies. They lack the ability to accurately locate the abnormal valve and record its status, making troubleshooting and repair difficult. For example, in a large-scale water conservancy project, if a valve loses control due to a communication interruption, operators cannot quickly determine its location and the type of abnormality. This can lead to delayed emergency response and increase the risk of disasters such as floods.

[0007] Inadequate data reliability. Traditional systems lack effective verification and repair mechanisms for collected state parameters. Sensor failures or interference during data transmission can cause erroneous data to enter the control system, leading to malfunctions. For example, a fluid pressure sensor failure can cause abnormal collected data. If not discovered and corrected promptly, the control system may generate adjustment commands based on this erroneous data, causing valve malfunction and compromising production safety. Summary of the Invention

[0008] The object of the present invention is to provide a distributed valve body remote control system to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solutions: a distributed valve body remote control system, the system comprising: The valve status acquisition module is used to set each monitoring period and then collect the corresponding status parameters of the valves distributed in multiple areas during each monitoring period. The status parameters include valve opening, fluid pressure and valve surface temperature; The remote communication analysis module is used to analyze the real-time operating status of each valve body based on the status parameters corresponding to each monitoring period, and to evaluate whether the communication delay between the valve bodies exceeds a preset threshold; A distributed coordination module is used to synchronize the status parameters of each valve body to the control center when the communication delay does not exceed the preset threshold, thereby generating dynamic adjustment instructions for each valve body; The instruction distribution module is used to calculate the priority weight corresponding to each valve body according to the dynamic adjustment instruction, thereby determining the order of instruction distribution. When the priority weight of a valve body is lower than a preset critical value, the adjustment instruction of the valve body is delayed. The abnormal response module is used to generate an abnormal alarm signal and mark the position code of the corresponding valve body when the communication delay exceeds a preset threshold or the priority weight of a valve body is continuously lower than a preset critical value.

[0010] Preferably, the state parameters corresponding to the valve bodies distributed in multiple areas in each monitoring period are collected, and the specific collection process is as follows: Multiple monitoring nodes are divided into spatial distribution within the designated area. Each monitoring node corresponds to a valve body. Multiple sets of sensors are set in each monitoring node to record the opening change curve, fluid pressure fluctuation data and surface temperature change curve of each valve body respectively. According to the set monitoring cycles, the standard operating parameter range of each valve body is retrieved from the historical database, and the lower limit of the standard range is used as the initial monitoring benchmark. When the real-time parameters of a valve body deviate from the initial monitoring benchmark, the high-frequency monitoring mode is started, and the state parameters of the valve body are continuously collected in the subsequent monitoring cycles to calculate its opening deviation rate, pressure fluctuation amplitude and temperature change gradient.

[0011] Preferably, the evaluation of whether the communication delay between the valve bodies exceeds a preset threshold is performed in the following manner: Based on the synchronized timestamps of the status parameters of each valve body within each monitoring cycle, the data transmission time difference between adjacent valve bodies is calculated. If the average time difference of a group of valve bodies is greater than the preset threshold, it is determined that the communication delay exceeds the limit; otherwise, it is determined that the communication link is normal.

[0012] Preferably, the dynamic adjustment instructions for each valve body are generated in the following specific generation process: Obtain the operation mode template of each valve body from the configuration library, which includes the opening adjustment coefficient, pressure balance factor and temperature compensation parameters; By formula Calculate the dynamic adjustment factor of each valve body, where is the opening deviation rate, is the pressure fluctuation amplitude, is the temperature gradient, is a dynamic adjustment factor, They are the correction coefficients corresponding to opening, pressure and temperature respectively.

[0013] Preferably, the priority weight corresponding to each valve body is calculated, and the specific calculation process is as follows: By formula Determine the priority weights, where is a dynamic adjustment factor, is the load ratio of the valve body area, is the historical failure frequency, is the priority weight, are the normalization coefficients of dynamic adjustment factor, load proportion and fault frequency respectively.

[0014] Preferably, the delay in distributing the regulating instruction of the valve body has a specific delay strategy as follows: The valve bodies with priority weights lower than the preset critical value are placed in the queue to be processed and arranged in ascending order according to the weight value. The weights of the valve bodies in the queue are recalculated at fixed time intervals. If the weight returns to above the critical value, the instruction is issued immediately. Otherwise, the delay time is accumulated until the forced issuance condition is triggered.

[0015] Preferably, the abnormal alarm signal is generated and the position code of the corresponding valve body is marked. The specific marking process is as follows: The spatial coordinates of the abnormal valve body are extracted from the topological map, and a unique location code is generated by combining it with the area number. The code is associated with the alarm signal and stored in the log database. At the same time, an alarm notification containing the location code is sent to the control center.

[0016] Preferably, the start and exit conditions of the high-frequency monitoring mode are as follows: When the opening deviation rate of a valve body exceeds the upper limit of the standard range for three consecutive monitoring cycles, or the temperature change gradient reaches the preset warning value, high-frequency monitoring will be automatically started; if the parameters of the valve body return to the standard range for five consecutive monitoring cycles, high-frequency monitoring will be exited.

[0017] Preferably, the triggering rules of the mandatory distribution conditions are as follows: When the cumulative delay time of a valve body reaches the maximum allowable threshold, or the system detects an emergency condition in the area where the valve body is located, its adjustment instructions are immediately and forcibly distributed, ignoring the priority weight limit.

[0018] Preferably, after collecting the status parameters, a data verification process is also included, which is as follows: The valve body opening, fluid pressure and surface temperature collected in each monitoring cycle are detected for abnormal values. If the fluctuation amplitude of a parameter exceeds twice the historical average for three consecutive monitoring cycles, it is judged as invalid data and interpolated from the data of adjacent monitoring nodes.

[0019] Compared with the prior art, the present invention has the following beneficial effects: In terms of monitoring capabilities, the system uses the valve status acquisition module to achieve dynamic monitoring of multi-dimensional status parameters such as valve opening, fluid pressure, and surface temperature. By setting up multiple sets of sensors at each monitoring node, it can record the valve opening change curve, fluid pressure fluctuation data, and surface temperature change curve in real time, providing the system with rich operating status information. At the same time, the system introduces a high-frequency monitoring mode. When the real-time parameters of a valve body deviate from the initial monitoring benchmark, it can automatically start high-frequency monitoring, continuously collect the status parameters of the valve body, and calculate the opening deviation rate, pressure fluctuation amplitude, and temperature change gradient. This effectively improves the ability to capture abnormal conditions, can promptly detect subtle abnormalities of the valve body, and provide strong support for fault warning and early processing. For example, when the valve body temperature change gradient reaches the preset warning value, the high-frequency monitoring mode can be immediately activated to continuously track temperature changes, helping operators to promptly detect potential equipment failures.

[0020] In terms of communication and coordination, the remote communication analysis module can accurately assess the communication delay by analyzing the synchronization timestamps of the status parameters of each valve body in each monitoring cycle and calculating the data transmission time difference between adjacent valve bodies. When the communication delay does not exceed the preset threshold, the distributed coordination module can synchronize the status parameters of each valve body to the control center, ensuring that the control center can grasp the operating status of each valve body in real time, providing a reliable data basis for generating dynamic adjustment instructions. This mechanism effectively guarantees the communication stability and data synchronization between valve bodies in multiple regions, improves the overall coordination of the system, and avoids the problem of asynchronous execution of control instructions due to communication delays. Especially in the multi-valve collaborative control scenario, it can ensure that each valve body is adjusted according to the expected timing and parameters, thereby improving the collaborative work efficiency of the system.

[0021] Regarding control command generation and distribution, the system utilizes a dynamic adjustment factor formula and a priority weight formula to achieve intelligent generation and distribution of control commands. The dynamic adjustment factor comprehensively considers real-time operating parameters such as opening deviation rate, pressure fluctuation amplitude, and temperature gradient. Combined with the adjustment coefficient in each valve's operating mode template, it generates personalized dynamic adjustment commands for each valve, ensuring that the control commands are more closely aligned with the valve's actual operating requirements and improving control accuracy. The command distribution module prioritizes command distribution based on priority weights, which take into account factors such as the dynamic adjustment factor, the load percentage of the valve's region, and historical fault frequency. This ensures that commands for critical valves are prioritized and optimizes system resource allocation. Furthermore, for valves with priority weights below a preset threshold, the system implements a delayed dispatch strategy, placing them in a pending queue and periodically recalculating their weights. This prevents non-critical valves from consuming excessive resources while ensuring timely execution of control commands once their status recovers, improving overall system control efficiency and resource utilization. In addition, the setting of forced distribution conditions ensures reliable control of the system in emergency situations. When the delay time reaches the maximum allowable threshold or an emergency condition is detected, instructions can be forced to be distributed immediately to ensure that the system can maintain stable operation even in extreme situations.

[0022] In terms of abnormal response and fault handling, the abnormal response module can generate an abnormal alarm signal and mark the position code of the corresponding valve body when it detects that the communication delay exceeds the preset threshold or the priority weight of a valve body is continuously lower than the preset critical value. By extracting the spatial coordinates of the abnormal valve body from the topological map and generating a unique position code in combination with the area number, the abnormal valve body can be accurately located. At the same time, the code and the alarm signal are associated and stored in the log database, and an alarm notification containing the position code is sent to the control center, which facilitates the operator to quickly troubleshoot and handle the fault. This mechanism significantly shortens the fault location and processing time and improves the reliability and safety of the system. For example, in a large industrial pipeline network, when a valve body has a communication failure, the operator can quickly find the specific location of the faulty valve body based on the position code, and perform repairs in time to avoid the expansion of the fault.

[0023] In terms of data reliability assurance, the system adds a data verification process after collecting status parameters. By detecting outliers on the parameters collected in each monitoring cycle, it can promptly detect invalid data and interpolate and supplement it from the data of adjacent monitoring nodes, effectively improving the reliability and accuracy of the data, avoiding erroneous data caused by sensor failure or data transmission interference that affects the normal operation of the control system, and further improving the stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a working principle diagram of the distributed valve body remote control system of the present invention; Figure 2 Flow chart of valve body status parameter collection; Figure 3 Flowchart for evaluating inter-valve body communication delay; Figure 4 A flowchart for adjusting the instruction delay distribution strategy; Figure 5 This is the control logic diagram for the start and exit conditions of the high-frequency monitoring mode. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1-Figure 5 The present invention relates to a distributed valve body remote control system, which includes: Valve status acquisition module: The system pre-sets each monitoring cycle (e.g., 30 minutes is the default basic cycle), and collects the status parameters of each valve body within the corresponding monitoring cycle through monitoring nodes distributed in multiple areas. The status parameters include valve body opening, fluid pressure, and valve body surface temperature.

[0027] Remote communication analysis module: Based on the status parameters collected in each monitoring cycle, it analyzes the real-time operating status of each valve body (such as normal operation, parameter fluctuations, etc.), and at the same time evaluates whether the communication delay between each valve body exceeds the preset threshold (such as 50ms).

[0028] Distributed coordination module: When the communication delay does not exceed the preset threshold, the state parameters of each valve body are synchronized to the control center, and the control center generates dynamic adjustment instructions for each valve body according to the preset algorithm.

[0029] Instruction distribution module: Based on the dynamic adjustment instructions, the priority weight corresponding to each valve body is calculated to determine the order of instruction distribution; if the priority weight of a valve body is lower than the preset critical value (such as 0.3), the adjustment instruction of the valve body is delayed.

[0030] Abnormal response module: When the communication delay exceeds the preset threshold or the priority weight of a valve body continues to be lower than the preset critical value, an abnormal alarm signal is generated and the position code of the corresponding valve body is marked for positioning and troubleshooting.

[0031] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1: Multiple monitoring nodes are spatially distributed within a designated area, with each monitoring node uniquely corresponding to a valve. Taking an industrial pipeline network as an example, monitoring nodes are deployed in different functional areas (e.g., raw material input, process processing, and product output). Each node is equipped with multiple sensors, including potentiometer sensors for measuring valve opening, piezoresistive transmitters for monitoring fluid pressure, and thermocouples for detecting valve surface temperature. These sensors collect data in real time at a set sampling frequency (e.g., 10 times per second), generating continuous curves of valve opening variation, fluid pressure fluctuations, and surface temperature variation. The data is then packaged according to the monitoring period (e.g., a default 30-minute basic period).

[0032] Based on each preset monitoring cycle, the system retrieves the standard operating parameter range for the corresponding valve body from the historical database. This standard range is pre-set based on the valve body type, process requirements, and safety regulations. For example, the standard opening range for a certain type of control valve is 20%-80%, the standard fluid pressure range is 0.5-1.2MPa, and the standard surface temperature range is 20-60°C. The system uses the lower limit of the standard range (such as 20% opening, 0.5MPa pressure, and 20°C temperature) as the initial monitoring benchmark. When the real-time parameters of a valve body (such as 15% opening, 0.4MPa pressure, or 18°C temperature) deviate from the initial monitoring benchmark, the judgment logic of the high-frequency monitoring mode is triggered.

[0033] The activation condition for high-frequency monitoring mode is when real-time parameters deviate from the initial monitoring baseline. To determine this, the system first compares the real-time parameters of the current monitoring cycle with the initial baseline values. If the opening is less than 20%, the pressure is less than 0.5 MPa, or the temperature is less than 20°C, it is considered a parameter deviation. Upon detecting a parameter deviation, the system automatically adjusts the acquisition frequency during the subsequent monitoring cycle, shortening the original 30-minute base cycle to 5 minutes (i.e., the high-frequency monitoring cycle) and continuously acquiring the valve body's status parameters. During this process, the system simultaneously calculates three key indicators: Opening Deviation Rate: Calculated as (Real-Time Opening - Standard Range Lower Limit) ÷ (Standard Range Upper Limit - Standard Range Lower Limit) × 100%. For example, if the standard opening range is 20%-80% and the real-time opening is 15%, the opening deviation rate is (15%-20%) ÷ (80%-20%) × 100% = -8.33%.

[0034] Pressure fluctuation amplitude: Calculated as |Real-time pressure - Historical pressure average|. Assuming the historical pressure average is 0.8 MPa and the real-time pressure is 0.6 MPa, the pressure fluctuation amplitude is 0.2 MPa.

[0035] Temperature gradient: Calculated as (current temperature - previous monitoring period temperature) ÷ time interval. If the temperature rises from 25°C to 35°C in 10 minutes, the time interval is 1 / 6 hour, and the temperature gradient is (35°C - 25°C) ÷ (1 / 6 hour) = 60°C / hour.

[0036] The data collection and calculation processes described above are automatically performed by the valve status acquisition module. The collected raw data and calculation results are transmitted in real time to the remote communication analysis module via an encrypted communication link, providing basic data support for subsequent operational status assessment and the generation of adjustment instructions. Throughout this process, the system timestamps the parameters of each monitoring cycle (with millisecond accuracy) to ensure data timeliness and traceability. Once high-frequency monitoring mode is activated, the system continuously tracks parameter changes until an exit condition is met (such as parameter return to the standard range described in the subsequent examples) or an abnormal response process is triggered.

[0037] In addition, after collecting status parameters, a data verification process is required: valve opening, fluid pressure, and surface temperature data within each monitoring cycle are checked for outliers, and statistical methods (such as the 3σ principle) are used to identify invalid data. If the fluctuation amplitude of a parameter exceeds twice the historical average for three consecutive monitoring cycles (for example, if the pressure parameter has a historical average of 0.8 MPa and the fluctuation amplitude exceeds 1.6 MPa three times in a row), it is considered invalid data. The system automatically fills the missing value using interpolation algorithms (such as linear interpolation and polynomial interpolation) from the data of adjacent monitoring nodes during the same period to ensure data integrity and the accuracy of subsequent analysis.

[0038] Example 2: During each monitoring cycle, after the valve status acquisition module completes state parameter collection, the remote communication analysis module adds a millisecond-accurate synchronization timestamp to each set of parameters. This timestamp corresponds to the moment when parameter collection is completed and the data enters the transmission link. For example, using adjacent valves located in the same pipe network (e.g., upstream valve A and downstream valve B, physically separated by L meters, with a fiber optic or wireless communication link), the module compares the timestamp difference between the two and calculates the data transmission time between them. The specific steps are as follows: For each set of adjacent valve bodies (such as A and B), within N consecutive monitoring cycles (such as N=10), record the parameter timestamp of valve body A in each cycle. and valve body B parameter timestamp ( ), calculate the absolute value of the time difference between the two in the same period: ,This time difference reflects the time required for data to be transmitted from one valve body to another valve body,,which includes the combined influence of factors such as the signal propagation time in the physical medium,,device processing delay, and network congestion.

[0039] The time difference of the module for N consecutive cycles Perform statistical analysis and calculate the arithmetic mean: The preset threshold is determined based on factors such as the type of communication link and the real-time requirements of the system (e.g., the preset threshold is set to 50ms when using optical fiber communication and 100ms when using wireless communication). If the value is greater than the preset threshold, the communication delay between the valve bodies in this group is determined to be excessive, and the abnormal response module is triggered to generate an alarm signal; if If the value is less than or equal to the preset threshold, the communication link is determined to be normal and the status parameters are allowed to be synchronized to the control center for subsequent processing.

[0040] For example, in a liquid pipeline network at a chemical park, valve body X (located at the pump station outlet) is connected to valve body Y (located at the tank inlet, 2 km from the pump station) via a fiber optic link. The system sets N = 10 monitoring cycles (each 30 minutes), and the time stamp differences between the two are recorded as follows (in milliseconds): 45, 48, 52, 49, 51, 47, 53, 50, 46, 49. The average value is calculated as: If the preset threshold is 50ms, the communication delay of the valve body group does not exceed the threshold, and the communication is determined to be normal; if the preset threshold is 48ms, the average value of 49ms exceeds the threshold, and the communication delay is determined to be excessive. The module immediately generates an abnormal alarm signal and marks the X and Y position codes of the valve body through the abnormal response module (such as generating a unique identifier based on the coordinates and area number in the topology map).

[0041] During the evaluation process, the module also needs to address possible timestamp synchronization errors. The system uses a global clock synchronization mechanism (such as the NTP protocol or the IEEE1588 precision clock protocol) to ensure that the clock deviation of each monitoring node is controlled to the microsecond level, avoiding time difference calculation errors caused by clock asynchrony. Furthermore, to address the potential packet loss in wireless communication scenarios, the module uses a retransmission mechanism and a sliding window protocol to ensure data integrity. However, communication delay evaluation is based only on the timestamps of successfully transmitted packets. Data from unsuccessfully transmitted periods is considered invalid and automatically skipped, and is not included in the average calculation.

[0042] The results of the communication delay assessment directly influence the subsequent data synchronization and command generation processes. If communication is normal, the distributed coordination module initiates the state parameter synchronization process, aggregating all valve data to the control center. If the communication delay exceeds the limit, the system suspends parameter synchronization for that group of valves and promptly notifies operations and maintenance personnel through the exception response module to troubleshoot the communication link failure, thereby preventing control command failures or system misjudgments caused by data lag. The entire assessment process is automatically executed by the remote communication analysis module, eliminating the need for human intervention and ensuring system real-time and reliability.

[0043] Example 3: The process of generating dynamic adjustment instructions for each valve body by the distributed coordination module is as follows: The system pre-stores the operation mode templates of various types of valve bodies through the configuration library. The templates contain parameter sets related to the valve body functions and process characteristics, including the opening adjustment coefficient, pressure balance factor and temperature compensation parameters. Taking the energy transmission system as an example, different types of valve bodies correspond to different templates: the shut-off valve template is used to control the flow of fluid, and its opening adjustment coefficient is 2. Set to 0.6, pressure balance factor is 0.3, temperature compensation parameter 0.1; the regulating valve template is used to accurately adjust the fluid parameters. , reflecting the dominant role of pressure fluctuations in regulating instructions. Template parameters can be configured and updated through the system background according to process requirements.

[0044] When the remote communication analysis module confirms that the communication link is normal, the distributed coordination module obtains the real-time status parameters of each valve body from the valve body status acquisition module and calculates the status of the valve body based on the opening deviation rate. , pressure fluctuation amplitude and temperature gradient , through the formula Calculate dynamic regulation factor .in, Take the absolute value to reflect the degree of deviation, which is calculated as (the absolute value of the difference between the real-time opening and the standard interval boundary) divided by (the difference between the upper and lower limits of the standard interval); is the actual amplitude of pressure fluctuation (in MPa), that is, the absolute value of the difference between the real-time pressure and the historical average; The absolute value is taken to reflect the severity of the temperature change (in °C / hour), which is obtained by dividing the temperature difference between adjacent monitoring periods by the time interval.

[0045] Take the regulating valve in a natural gas transmission and distribution system as an example: its standard opening range is 30%-70%, and the real-time opening is 78%. The opening deviation rate The historical average pressure is 2.0MPa, the real-time pressure is 2.5MPa, and the pressure fluctuation amplitude is ; The temperature gradient is 15℃ / hour, According to the control valve template parameters , calculate the dynamic regulation factor .

[0046] The control center calculates The value generates a specific adjustment instruction. The instruction content is the same as For example, when When , it is determined to be a slight parameter fluctuation and generates a "maintain current operating state" instruction; when When , it is determined to be a moderate deviation, and an instruction of "gradually adjusting the opening to the standard range" is generated, such as adjusting the opening by 5% per monitoring cycle; when When a fault occurs, it is determined to be a serious abnormality and generates an instruction to "immediately trigger the safety protection mechanism", such as emergency valve closure or start-up of the bypass system.

[0047] The generation of dynamic adjustment commands also needs to consider the valve's physical characteristics and process limitations. For example, for fast-response valves like butterfly valves, the command can be set to a higher adjustment rate. For valves that require slower movement, such as ball valves, the command must limit the single adjustment range, such as no more than 10% of the opening, to avoid fluid shock or equipment damage caused by violent movements. Furthermore, the module automatically filters out commands that exceed the valve's operating limits, such as commands outside the opening adjustment range, and generates an error log for operation and maintenance personnel to review.

[0048] The entire dynamic adjustment command generation process is based entirely on preset algorithms and real-time data, ensuring the scientific nature and consistency of the commands. Once generated, the commands are transmitted via an encrypted communication link to the command distribution module, which determines the execution order based on priority. The distributed coordination module also features historical command tracing, storing each valve's dynamic adjustment factors, template parameters, and generated command content, providing data support for system operation analysis and parameter optimization.

[0049] Example 4: The process of the instruction distribution module calculating the priority weight of each valve body and executing the delay strategy is as follows: The system uses the formula Determine the priority weight, where the parameters are defined as follows: It is a dynamic adjustment factor that reflects the current deviation of valve parameters and the comprehensive adjustment requirements. This factor is calculated based on the opening deviation rate, pressure fluctuation amplitude and temperature change gradient. The load ratio of the area where the valve body is located, that is, the proportion of the area in the total system load (for example, the percentage of water consumption in a certain area of the water supply network to the total water consumption of the system); The historical failure frequency of the valve body, the statistical period is the number of failures in the past 12 months; is the normalization coefficient, which is determined by parameter calibration during system initialization. Its value range is 0-1 and satisfies , for example, the common configuration is , reflecting the dominant influence of dynamic adjustment factors on priorities.

[0050] Taking the city gas transmission and distribution system as an example: the dynamic adjustment factor of the pressure regulating valve in a certain area , the load ratio of the area (i.e. 0.35), historical failure frequency times / year. According to the normalization coefficient , calculate the priority weight If the preset critical value is 1.0, the priority weight of the valve body is higher than the critical value, and its adjustment instruction enters the immediate distribution queue; the other pressure reducing valve times / year, calculated , below the critical value of 1.0, the valve body instruction enters the queue to be processed.

[0051] The delay strategy of the pending queue is as follows: valves with priority weights lower than the preset critical value are sorted in ascending order of weight (i.e., the smaller the weight, the higher the priority). The system triggers a queue refresh mechanism every fixed time interval (e.g., 10 minutes). When refreshing, the module recalculates the priority weights of all valves in the queue: if the weight of a valve is restored to above the critical value (e.g., the valve with the original weight of 0.8 is calculated after the parameters are adjusted), ), its instruction is removed from the queue and added to the immediate distribution queue, and the instruction is transmitted to the corresponding valve body through the communication link for execution; if the weight is still lower than the critical value, it will continue to stay in the queue and accumulate delay time.

[0052] The triggering rules for mandatory distribution conditions include: ① Delay Exceeded: When a valve's cumulative delay in the pending processing queue reaches the maximum allowed threshold (e.g., 60 minutes), the system automatically triggers forced dispatch, pushing the adjustment command to the valve for execution, regardless of whether its current weight meets the limit. This mechanism prevents the accumulation of system risks caused by long-term delays.

[0053] ② Emergency condition detection: If the system detects an emergency condition (such as a sudden pressure drop, temperature exceeding the limit, or equipment leakage) in the area where the valve body is located through a sensor or external interface (such as a fire alarm system), it immediately skips the priority weight calculation and forcibly distributes the adjustment instructions of the valve body to ensure the real-time emergency response.

[0054] During the command distribution process, the module attaches a unique identifier and timestamp to each command, recording the command generation time, distribution time, and execution feedback status. If the valve does not return an execution confirmation signal within a specified timeframe (e.g., 5 minutes), the system automatically reissues the command and marks it as "unconfirmed." A cumulative three unconfirmed responses triggers a device communication failure alarm. Furthermore, the command distribution module supports manual intervention. Operations and maintenance personnel can use the control center interface to forcibly adjust the priority weight of a valve or directly trigger command distribution, meeting the needs of manual control in special scenarios.

[0055] The entire priority weight calculation and delay strategy execution process is completed automatically by the system. By quantitatively analyzing multi-dimensional data such as dynamic adjustment factors, regional loads, and historical faults, it realizes intelligent and differentiated command distribution. While ensuring the timely response of critical valve bodies, it rationally allocates system resources to avoid overload impacts of non-critical commands on communication links and control centers.

[0056] Example 5: The process of the abnormal response module generating an abnormal alarm signal and marking the valve body position code, as well as the specific implementation of the high-frequency monitoring mode and data verification are as follows: When the remote communication analysis module determines that the communication delay exceeds the preset threshold, or the instruction distribution module detects that the priority weight of a valve body continues to be below the preset critical value (for example, it has not recovered to above the critical value for three consecutive refresh cycles), the abnormal response module initiates the alarm process. First, the module extracts the spatial coordinates of the abnormal valve body from the system topology map. These coordinates are determined based on the physical layout data of the geographic information system (GIS) or industrial automation system and include longitude and latitude (for outdoor scenarios) or floor and pipeline node number (for indoor scenarios). For example, an abnormal valve body is located on the second floor of Plant 3, Area A of the Chemical Park. Its spatial coordinates can be expressed as (A-3-2F, X=120.5°, Y=30.2°, Z=5.8m).

[0057] The module generates a unique location code based on the area number (e.g., "A" for Area A) and the monitoring date (e.g., May 18, 2025). This code follows the format "area number - node number - timestamp - serial number," for example, "A-01-20250518-001," where the "node number" uniquely identifies the monitoring node in the topology map, the "timestamp" is the year, month, and day when the alarm was triggered, and the "serial number" is the alarm sequence number for that day (starting at 001 and increasing in increments). This location code is associated with the abnormal alarm signal and stored in a log database via an encrypted protocol. The database records include information such as the alarm type (communication delay or low priority), alarm time, and a snapshot of valve parameters. It supports searches based on time, area, and valve type.

[0058] At the same time, the module sends alarm notifications to the control center through multiple channels, including but not limited to system interface pop-ups (displaying the location code, alarm type, and parameter anomaly details), SMS alerts (delivered to designated operations personnel's phones), and voice announcements (triggered on-site at the control center). The notifications include a description of the valve's physical location corresponding to the location code (e.g., "Steam Pipe Control Valve, 2nd Floor, Building 3, Area A"), enabling operations personnel to quickly locate the fault.

[0059] The activation and exit mechanism of the high-frequency monitoring mode is as follows: When the opening deviation rate of a valve body exceeds the upper limit of the standard range for three consecutive monitoring cycles (e.g., the standard range is 20% to 80%, and the opening is ≥85% for 90 consecutive minutes), or the temperature change gradient reaches the preset warning value (e.g., 30°C / hour), the system automatically shortens the monitoring cycle of the valve body from the basic cycle (e.g., 30 minutes) to the high-frequency cycle (e.g., 5 minutes), and activates the continuous acquisition mode in the status acquisition module to track parameter changes in real time. If the parameters of the valve body return to the standard range (e.g., the opening is stable at 75%-80%, and the temperature change gradient is ≤10°C / hour) for five consecutive high-frequency monitoring cycles (i.e., 25 minutes), the high-frequency monitoring mode is exited and the basic cycle is restored. During the high-frequency monitoring period, the storage accuracy of the collected data is improved to the millisecond level to facilitate analysis of the detailed characteristics of parameter fluctuations.

[0060] During the data verification process, the system detects abnormal values of valve body opening, fluid pressure and surface temperature in each monitoring cycle. Taking fluid pressure as an example, the module first calculates the historical mean value of this parameter in the past 24 hours. and standard deviation , use the 3σ principle to determine abnormal values: if the pressure value of a monitoring period exceeds If the parameter falls outside the specified range for three consecutive monitoring cycles, it is marked as suspicious data. If the parameter exceeds this range for three consecutive monitoring cycles, it is considered invalid data. In this case, the system uses linear interpolation to fill in the missing values from the contemporaneous data of adjacent monitoring nodes (such as upstream and downstream valve bodies). For example, if the pressure data for valve body C is invalid three times in a row, and the pressure values of upstream valve body B and downstream valve body D at the corresponding times are 0.9 MPa and 0.85 MPa, respectively, the interpolated value for valve body C is (0.9 + 0.85) ÷ 2 = 0.875 MPa. The interpolated data is then quality-marked for subsequent analysis.

[0061] The entire abnormal response process is closely coordinated with the monitoring mechanism. Unique position codes are used to precisely locate the faulty valve body. High-frequency monitoring and data verification ensure the reliability of abnormal data, providing comprehensive support for system fault diagnosis and operation and maintenance decision-making. Abnormal alarm information and processing records are fully documented, meeting the traceability requirements of industrial control systems.

[0062] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0063] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A distributed valve remote control system, characterized in that: include: The valve status acquisition module is used to set each monitoring period and then collect the corresponding status parameters of the valves distributed in multiple areas during each monitoring period. The status parameters include valve opening, fluid pressure and valve surface temperature; The remote communication analysis module is used to analyze the real-time operating status of each valve body based on the status parameters corresponding to each monitoring period, and to evaluate whether the communication delay between the valve bodies exceeds a preset threshold; A distributed coordination module is used to synchronize the status parameters of each valve body to the control center when the communication delay does not exceed the preset threshold, thereby generating dynamic adjustment instructions for each valve body; The instruction distribution module is used to calculate the priority weight corresponding to each valve body according to the dynamic adjustment instruction, thereby determining the order of instruction distribution. When the priority weight of a valve body is lower than a preset critical value, the adjustment instruction of the valve body is delayed. The abnormal response module is used to generate an abnormal alarm signal and mark the position code of the corresponding valve body when the communication delay exceeds a preset threshold or the priority weight of a valve body is continuously lower than a preset critical value.

2. A distributed valve body remote control system according to claim 1, characterized in that: The state parameters corresponding to the valve bodies distributed in multiple areas in each monitoring period are collected. The specific collection process is as follows: Multiple monitoring nodes are divided into spatial distribution within the designated area. Each monitoring node corresponds to a valve body. Multiple sets of sensors are set in each monitoring node to record the opening change curve, fluid pressure fluctuation data and surface temperature change curve of each valve body respectively. According to the set monitoring cycles, the standard operating parameter range of each valve body is retrieved from the historical database, and the lower limit of the standard range is used as the initial monitoring benchmark. When the real-time parameters of a valve body deviate from the initial monitoring benchmark, the high-frequency monitoring mode is started, and the state parameters of the valve body are continuously collected in the subsequent monitoring cycles to calculate its opening deviation rate, pressure fluctuation amplitude and temperature change gradient.

3. A distributed valve remote control system according to claim 2, characterized in that: The evaluation process of whether the communication delay between valve bodies exceeds the preset threshold is as follows: Based on the synchronized timestamps of the status parameters of each valve body within each monitoring cycle, the data transmission time difference between adjacent valve bodies is calculated. If the average time difference of a group of valve bodies is greater than the preset threshold, it is determined that the communication delay exceeds the limit; otherwise, it is determined that the communication link is normal.

4. A distributed valve remote control system according to claim 3, characterized in that: The specific generation process of the dynamic adjustment instructions for each valve body is as follows: Obtain the operation mode template of each valve body from the configuration library, which includes the opening adjustment coefficient, pressure balance factor and temperature compensation parameters; By formula Calculate the dynamic adjustment factor of each valve body, where is the opening deviation rate, is the pressure fluctuation amplitude, is the temperature gradient, is a dynamic adjustment factor, They are the correction coefficients corresponding to opening, pressure and temperature respectively.

5. A distributed valve remote control system according to claim 4, characterized in that: The specific calculation process for calculating the priority weight corresponding to each valve body is as follows: By formula Determine the priority weights, where is a dynamic adjustment factor, is the load ratio of the valve body area, is the historical fault frequency, is the priority weight, are the normalization coefficients of dynamic adjustment factor, load proportion and fault frequency respectively.

6. A distributed valve remote control system according to claim 5, characterized in that: The specific delay strategy for delaying the distribution of the regulating instruction of the valve body is as follows: The valve bodies with priority weights lower than the preset critical value are placed in the queue to be processed and arranged in ascending order according to the weight value. The weights of the valve bodies in the queue are recalculated at fixed time intervals. If the weight returns to above the critical value, the instruction is issued immediately. Otherwise, the delay time is accumulated until the forced issuance condition is triggered.

7. A distributed valve remote control system according to claim 6, characterized in that: The abnormal alarm signal is generated and the position code of the corresponding valve body is marked. The specific marking process is as follows: The spatial coordinates of the abnormal valve body are extracted from the topological map, and a unique location code is generated by combining it with the area number. The code is associated with the alarm signal and stored in the log database. At the same time, an alarm notification containing the location code is sent to the control center.

8. A distributed valve remote control system according to claim 7, characterized in that: The start and exit conditions of the high-frequency monitoring mode are as follows: When the opening deviation rate of a valve body exceeds the upper limit of the standard range for three consecutive monitoring cycles, or the temperature change gradient reaches the preset warning value, high-frequency monitoring will be automatically started; if the parameters of the valve body return to the standard range for five consecutive monitoring cycles, high-frequency monitoring will be exited.

9. A distributed valve remote control system according to claim 8, characterized in that: The triggering rules of the mandatory distribution conditions are as follows: When the cumulative delay time of a valve body reaches the maximum allowable threshold, or the system detects an emergency condition in the area where the valve body is located, its adjustment instructions are immediately and forcibly distributed, ignoring the priority weight limit.

10. A distributed valve remote control system according to claim 1, characterized in that: After collecting status parameters, the data verification process is also included, as follows: The valve body opening, fluid pressure and surface temperature collected in each monitoring cycle are detected for abnormal values. If the fluctuation amplitude of a parameter exceeds twice the historical average for three consecutive monitoring cycles, it is judged as invalid data and interpolated from the data of adjacent monitoring nodes.

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