Gas-water separation system for power generation of flexible low-concentration gas internal combustion engine

Abstract

The invention relates to the technical field of industrial automatic control, in particular to a gas-water separation system for flexible low-concentration gas internal combustion engine power generation, which comprises a signal acquisition module, a working condition identification module, a temperature zone control module, an abnormity monitoring module and a drift identification module. According to the method, mean value variation amplitude trend analysis between sampling sequences is introduced in a signal data processing link, the representativeness of a sampling result is improved by combining dynamic sliding window adjustment, and working condition identification is realized by utilizing the proportion difference of periodic methane and water concentration and heat and humidity distribution section matching; a temperature interval change direction and interval residence continuous parallel detection mode is adopted, a temperature control boundary approaching state is effectively judged, continuous adjustment of cold source output is realized, and a trend synchronous screening and linkage detection mode is adopted to dynamically adjust a control signal constraint range. Cold source starting temperature difference track contrastive analysis provides support for early recognition and feedback of drift behaviors, and safety and stability of the separation process are enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of industrial automatic control, and in particular to a gas-water separation system for flexible low-concentration gas internal combustion engine power generation. Background Art

[0002] The field of industrial automatic control technology includes technologies related to automatic measurement, automatic adjustment and intelligent control of mechanical equipment, energy systems, environmental parameters and other variables involved in the industrial production process. The process parameters such as temperature, pressure and flow are collected through the perception layer, and the actuators such as motors, pumps and valves are closed-loop controlled by the control layer to achieve automation, energy saving and stabilization of the process flow. It involves field data acquisition technology, programmable logic controller control technology, sensor signal processing technology, human-machine interface and remote monitoring integration technology. It is applied to energy, electricity, chemical industry, manufacturing and other industries to build intelligent and flexible process control systems. Among them, gas-water separation The system refers to a system with a programmable logic controller as the control core, which uses the sliding average filtering method to perform real-time processing of collected signals such as gas temperature and cold water inlet temperature. According to the range of gas temperature, it outputs control instructions to the inverter of the air-cooled chiller compressor and water pump to adjust its frequency, control the cold supply, and realize the separation of water in the gas. The system involves the water removal control link in the process of low-concentration gas transportation, including gas temperature signal acquisition, PLC digital processing, execution of AD conversion program, variable frequency speed control of compressor water pump, and obtains gas and environmental parameters through the signal acquisition unit and then hands them over to the PLC for programmed decision-making to adjust the power output of the cold source in the separation link.

[0003] Existing gas-water separation technology relies on a single parameter or a fixed sampling interval when processing signal fluctuations and identifying operating conditions, and lacks attention to data change trends and periodic dynamic characteristics, which leads to control delays and misjudgment of operating conditions under environmental disturbances or gas source fluctuations. In actual applications, abnormal signal fluctuations are difficult to capture in a timely manner. The lack of a linkage mechanism in the water separation link leads to large fluctuations in the separation effect. The response of the cold source equipment and long-term performance changes are difficult to accurately identify, resulting in a decrease in separation efficiency or an imbalance in the equipment operating status. Under actual operating conditions, over-cooling, increased energy consumption or safety hazards are prone to occur. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a gas-water separation system for flexible low-concentration gas internal combustion engine power generation.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a gas-water separation system for flexible low-concentration gas internal combustion engine power generation comprises:

[0006] The signal acquisition module calls the sampled signal sequence, analyzes the consistency of the change trend of the data in consecutive cycles according to the change amplitude between the mean values of adjacent cycles of each sampling channel, adjusts the sliding window length, updates the average data and enters it into the sliding queue, and generates a signal processing result;

[0007] The working condition identification module uses the signal processing result to calculate the proportional difference between the methane concentration and the moisture concentration in the current cycle, identifies the proportional structure, matches it with the preset thermal humidity distribution section, identifies the thermal humidity level of the current working condition and matches the control mode, and generates a thermal humidity status label;

[0008] The temperature zone control module calls the thermal humidity status label, analyzes the change direction of the current temperature data in consecutive cycles, detects the boundary approaching state according to the persistence of the transition region between the temperature control range and the buffer range of the temperature data, adjusts the cold source frequency setting command, and obtains the cold source frequency setting;

[0009] The abnormal monitoring module calls the cold source frequency setting, compares the consistency of the change direction of each sampled data in adjacent consecutive cycles, screens the combined results with consistent trends and fluctuation amplitudes, detects the moisture fluctuation linkage state, adjusts the constraint range of the control signal, and generates a linkage fluctuation determination result.

[0010] As a further solution of the present invention, the signal processing result is specifically the response adjustment amplitude, the window sliding structure, and the cycle change direction. The thermal humidity status label includes the thermal humidity distribution level, the methane moisture ratio label, and the working condition control mode. The cold source frequency setting is specifically the frequency adjustment gear, the boundary state mark, and the temperature control range index. The linkage fluctuation determination result includes the abnormal identification label, the synchronous trend index, and the moisture disturbance state.

[0011] As a further solution of the present invention, the signal acquisition module includes:

[0012] The difference calculation sub-module calls the sampled signal sequence, obtains the current cycle sampling values of the gas temperature sensor, the cooling water return temperature sensor, and the moisture concentration sensor, compares them with the mean value of the previous cycle, calculates the cycle difference of each item of data, and establishes a multi-source sensing difference sequence;

[0013] The trend evaluation sub-module extracts the change direction and amplitude of the gas temperature difference, the cooling water temperature difference, and the moisture concentration difference in consecutive cycles according to the multi-source sensing difference sequence, analyzes the consistency of the change trend of the data in consecutive cycles, and calculates the operation trend consistency score;

[0014] The window adjustment sub-module calls the trend consistency score to adjust the sliding window length, including shortening the sliding window length when the score is higher than the trend consistency critical reference value, and extending the sliding window length when it is lower than the trend consistency critical reference value, updating the average data into the sliding queue, and establishing the signal processing result.

[0015] As a further solution of the present invention, the operating condition identification module includes:

[0016] The ratio structure discrimination sub-module uses the signal processing result to calculate the ratio difference between the methane concentration and the moisture concentration in the current cycle, identifies the ratio structure, and generates ratio structure characteristic information;

[0017] The heat and humidity section identification sub-module compares the ratio structure characteristic information with a plurality of preset heat and humidity distribution sections, and according to the interval coincidence degree and the trend offset direction, identifies the heat and humidity level of the current operating condition to obtain the heat and humidity level identification result;

[0018] The control parameter configuration sub-module calls the heat and humidity level identification result, matches the control mode according to the heat and humidity level, and adjusts the control parameter configuration to obtain the heat and humidity status label.

[0019] As a further solution of the present invention, the temperature zone control module includes:

[0020] The temperature direction identification sub-module calls the heat and humidity status label, obtains the inlet temperature value and the temperature value of the previous cycle in the current cycle, calculates the temperature difference direction, and judges the temperature change trend direction according to the consistency ratio of the temperature difference directions in consecutive cycles to obtain the temperature change direction index;

[0021] The boundary trend evaluation sub-module identifies the temperature data in the middle of the temperature control interval and the buffer interval according to the temperature change direction index, analyzes the persistence according to the number of state maintenance cycles, and calculates the boundary approach status score;

[0022] The frequency command setting sub-module detects the boundary approach status according to the boundary approach status score and executes the interval identification mark update, retrieves the preset interval cold source frequency command parameter library, outputs the command parameter as the current control target, and obtains the cold source frequency setting.

[0023] As a further solution of the present invention, the anomaly monitoring module includes:

[0024] The fluctuation extraction sub-module calls the cold source frequency setting, extracts the gas temperature, moisture concentration, and cooling return water temperature values in consecutive cycles, analyzes the change direction of each data between adjacent cycles, and obtains the multi-parameter change direction trend sequence;

[0025] Based on the multi-parameter change direction trend sequence, the trend screening sub-module compares the consistency of the change directions of gas temperature, moisture concentration, and cooling return water temperature in the same cycle, combines the consistency of the change amplitudes, screens the parameter combinations with synchronized trends, calculates the linkage trend synchronization score, and generates a list of trend linkage combinations;

[0026] According to the list of trend linkage combinations, the linkage determination sub-module identifies and marks the moisture fluctuation linkage states in multiple cycles, identifies the abnormal accumulation events of condensate water, adjusts the constraint range of the control signal in the current operation stage, and obtains the linkage fluctuation determination result.

[0027] As a further solution of the present invention, the system further includes:

[0028] The drift identification module calls the linkage fluctuation determination result, extracts and analyzes the cold source start records, calculates the temperature difference trajectory between the start stage and the stable operation stage, compares the change directions and trends between the temperature difference trajectories in multiple start records, identifies the cold source heat response performance trend, and detects the drift behavior, generating an operation monitoring feedback result;

[0029] The operation monitoring feedback result specifically refers to the heat response change trend, the drift behavior discrimination result, and the stage performance record.

[0030] As a further solution of the present invention, the drift identification module includes:

[0031] The temperature difference trajectory extraction sub-module calls the linkage fluctuation determination result, extracts the gas temperature and cooling return water temperature data in each cold source start cycle, calculates the temperature mean values in the start stage and the stable operation stage, analyzes the temperature difference change trajectory between the stages and extracts the temperature difference data sequence, generating a stage temperature difference trajectory sequence;

[0032] Based on the stage temperature difference trajectory sequence, the response trend comparison sub-module calculates the trend characteristics of the temperature difference change trajectories in multiple start records, compares the consistency of the change directions and amplitudes between adjacent trajectories, screens the convergent trajectory combinations and identifies the consistent trend pattern, generating the cold source heat response performance trend;

[0033] The drift behavior detection sub-module calls the cold source heat response performance trend, calculates the deviation degree between the current trend and the reference trend, and based on the deviation degree of the temperature difference trajectory offset direction, amplitude, and the number of continuous cycles, identifies the cold source heat response performance trend, detects the drift behavior, and generates an operation monitoring feedback result.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are:

[0035] In the present invention, by introducing the analysis of the trend of the mean change amplitude between sampling sequences in the signal data processing link, combining with the adjustment of the dynamic sliding window to improve the representativeness of the sampling results, using the matching of the ratio difference between the periodic methane and moisture concentrations and the thermal and humidity distribution sections to identify the working conditions, and the parallel detection method of the temperature range change direction and the interval residence persistence to effectively judge the approaching state of the temperature control boundary, continuous adjustment of the cold source output is achieved. The trend synchronization screening and linkage detection method is used to dynamically adjust the constraint range of the control signal, and the comparison analysis of the cold source start temperature difference trajectory provides support for the early identification and feedback of the drift behavior, enhancing the safety and stability of the separation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is the system flow chart of the present invention;

[0037] Figure 2 is the flow chart of the signal acquisition module of the present invention;

[0038] Figure 3 is the flow chart of the working condition identification module of the present invention;

[0039] Figure 4 is the flow chart of the temperature zone control module of the present invention;

[0040] Figure 5 is the flow chart of the abnormal monitoring module of the present invention;

[0041] Figure 6 is the flow chart of the drift identification module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0043] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0044] Please refer to Figure 1 , the gas-water separation system for flexible low-concentration gas internal combustion engine power generation includes:

[0045] The signal acquisition module calls the sampled signal sequence, analyzes the consistency of the data change trend in consecutive cycles according to the change amplitude between the mean values of adjacent cycles for each sampling channel, adjusts the sliding window length, updates the average data and enters it into the sliding queue, and generates a signal processing result;

[0046] The working condition recognition module uses the signal processing result to calculate the proportional difference between the methane concentration and the moisture concentration in the current cycle, recognizes the proportional structure, matches it with the preset thermal and humidity distribution section, recognizes the thermal and humidity level of the current working condition and matches the control mode, and generates a thermal and humidity status label;

[0047] The temperature zone control module calls the thermal and humidity status label, analyzes the change direction of the current temperature data in consecutive cycles, detects the boundary approaching state according to the persistence of the transition area between the temperature control range and the buffer range of the temperature data, adjusts the cold source frequency setting instruction, and obtains the cold source frequency setting;

[0048] The abnormal monitoring module calls the cold source frequency setting, compares the consistency of the change direction of each sampled data in adjacent consecutive cycles, screens the combined results with consistent trends and fluctuation amplitudes, detects the moisture fluctuation linkage state, adjusts the constraint range of the control signal, and generates a linkage fluctuation determination result;

[0049] The drift identification module calls the linkage fluctuation determination result, extracts and analyzes the cold source start records, calculates the temperature difference trajectory between the start stage and the stable operation stage, compares the change direction and trend between the temperature difference trajectories in multiple start records, identifies the cold source thermal response performance trend, and detects the drift behavior, and generates an operation monitoring feedback result.

[0050] The signal processing result specifically includes the response adjustment amplitude, the window sliding structure, and the cycle change direction. The thermal and humidity status label includes the thermal and humidity distribution level, the methane moisture ratio label, and the working condition control mode. The cold source frequency setting specifically includes the frequency adjustment gear, the boundary status mark, and the temperature control range index. The linkage fluctuation determination result includes the abnormal identification label, the synchronous trend index, and the moisture disturbance state. The operation monitoring feedback result specifically refers to the thermal response change trend, the drift behavior discrimination result, and the stage performance record.

[0051] Please refer to Figure 2 , the signal acquisition module includes:

[0052] The difference calculation sub-module calls the sampled signal sequence, obtains the current cycle sampling values of the gas temperature sensor, the cooling water return temperature sensor, and the moisture concentration sensor, compares them with the mean value of the previous cycle, calculates the cycle difference of each data item, and establishes a multi-source sensing difference sequence;

[0053] For the process of calling the sampling signal sequence, the current real-time values of the gas temperature sensor, cooling water return temperature sensor and moisture concentration sensor are called at every set sampling cycle (for example, 1 minute) in units of cycles, and the average value of the sampling data of each sensor is calculated in the previous cycle. Taking the actual working condition of the gas internal combustion engine as an example, in the specific example, if the gas temperature collection value of the current cycle (such as the second cycle) is 36.2℃, the cooling water return temperature collection value is 32.5℃, and the moisture concentration collection value is 3.5%, and the sampling value groups of the first cycle are gas temperature [35.8℃, 36.0℃, 36.1℃], cooling water return temperature [35.8℃, 36.0℃, 36.1℃], respectively.

[0054] [32.0℃, 32.2℃, 32.3℃], water concentration [3.2%, 3.3%, 3.4%], then the mean calculation process of the first cycle is: gas temperature mean = (35.8+36.0+36.1) / 3 = 35.97℃, cooling water return temperature mean = (32.0+32.2+32.3) / 3 = 32.17℃, water concentration mean = (3.2+3.3+3.4) / 3 = 3.3%. Then calculate the cycle difference: gas temperature difference = 36.2℃-35.97℃ = 0.23℃, cooling water return temperature difference = 32.5℃-32.17℃ = 0.33℃, water concentration difference = 3.5%-3.3% = 0.2%, so the multi-source sensor difference sequence is [0.23, 0.33, 0.2].

[0055] The trend scoring submodule extracts the change direction and amplitude of the gas temperature difference, cooling water temperature difference, and water concentration difference in multiple consecutive cycles based on the multi-source sensor difference sequence, and analyzes the consistency of the data change trend in consecutive cycles through the formula:

[0056]

[0057] Calculate the operation trend consistency score;

[0058] Where C is the trend consistency score, is the normalized difference of the gas temperature in the ith cycle, obtained by dividing the difference between the gas temperature in the current cycle and the gas temperature in the previous cycle by the temperature reference variation range. is the normalized difference of the cooling water return temperature in the ith cycle, which is obtained by dividing the difference between the cooling water return temperature in the current cycle and the cooling water return temperature in the previous cycle by the reference range of the cooling water return temperature. is the normalized difference of moisture concentration in the ith cycle, obtained by dividing the difference between the moisture concentration in the current cycle and the moisture concentration in the previous cycle by the reference variation range of moisture concentration, n is the number of consecutive cycles, obtained by setting the sampling cycle window length, and i is the sampling cycle index number, obtained by incrementing the consecutive sampling cycle numbers;

[0059] In the trend evaluation sub-module, first, normalize each difference period by period. Taking the working normal parameter range of the gas internal combustion engine as a reference, for example, the reference change range of the gas temperature is 0.5°C, the reference change range of the return water temperature of the cooling water is 0.4°C, and the reference change range of the moisture concentration is 0.3%. Then, taking the calculation in the second period as an example, the normalized difference of the gas temperature is 0.23 / 0.5 = 0.46, the normalized difference of the return water temperature of the cooling water is 0.33 / 0.4 = 0.825, and the normalized difference of the moisture concentration is 0.2 / 0.3 ≈ 0.667. Process the normalized differences of three consecutive periods respectively to obtain the following data (shown in Table 1):

[0060] Table 1 Data Table of Multi-Source Sensing Difference Normalization

[0061] Cycle number Normalized value of gas temperature Normalized value of cooling water temperature Normalized value of moisture concentration 1 0.42 0.75 0.60 2 0.46 0.825 0.667 3 0.44 0.80 0.65

[0062] As shown in Table 1, substitute the data into the formula for calculation:

[0063]

[0064] Calculate the numerator:

[0065]

[0066] Calculate the denominator

[0067] (0.42 - 0.75) 2 +(0.42 - 0.60) 2 +(0.75 - 0.60) 2 = 0.1089 + 0.0324 +

[0068] 0.0225 = 0.1638;

[0069] (0.46 - 0.825) 2 +(0.46 - 0.667) 2 +(0.825 - 0.667) 2 = 0.1332 +

[0070] 0.0428 + 0.0250 = 0.2010;

[0071] (0.44 - 0.80) 2 +(0.44 - 0.65) 2 +(0.80 - 0.65) 2 = 0.1296 + 0.0441 +

[0072] 0.0225 = 0.1962;

[0073] 0.1638 + 0.2010 + 0.1962 = 0.561;

[0074] Calculate the trend consistency score:

[0075]

[0076] Among them, the trend consistency score is a comprehensive index value that measures whether the change trends of three key physical quantities (gas temperature, cooling water temperature, moisture concentration) in the system are directionally synchronous and the fluctuation amplitudes are coordinated within a certain number of consecutive cycles. The core role of this score is to provide a quantitative basis for the system to judge whether it is in a state of continuous and stable evolution of working conditions or whether it has entered the mixed disturbance area. The higher the value, the more synchronized and balanced the fluctuations of the system parameters are, and it is suitable to adopt a fast response strategy (such as shortening the filtering window). A lower value reflects scattered signal fluctuations and loss of synchronization, and it is suitable to extend the sampling window to enhance data stability, so as to realize the dynamic stretching of the sampling window and improve the adaptive ability and anti-interference ability of data preprocessing under different working conditions. The above result C = 8.02, indicating that the trend consistency is significantly higher than the preset trend consistency critical reference value (set to 5.0), reflecting a high degree of consistency in the data change trend under the current working conditions. The formula comprehensively reflects the consistency and correlation of trend changes through the sum of the products of the normalized differences of the gas temperature, the return water temperature of the cooling water, and the moisture concentration, which is convenient for further accurately evaluating the data fluctuation situation.

[0077] The window adjustment sub-module calls the trend consistency score to adjust the sliding window length, including shortening the sliding window length when the score is higher than the trend consistency critical reference value and extending the sliding window length when it is lower than the trend consistency critical reference value, updating the average data into the sliding queue, and establishing the signal processing result;

[0078] In the window adjustment sub-module, after the trend consistency score is calculated, by comparing it with the trend consistency critical reference value, for example, the reference value setting method is to take the mean value of the trend consistency values in historical working conditions, 5.0 ± 1.0, as the interval. When the actual calculated C = 8.02 exceeds the upper limit of the interval, 6.0, the sliding window length shortening operation is executed. The specific method is to reduce the current sampling window from the original 3 cycles to 2 cycles. Through this adjustment, the mean value calculation of the sampling data is immediately updated and entered into the sliding queue. For example, the window data in the next cycle is updated to only take the data of the 2nd and 3rd cycles to ensure that the trend analysis responds more timely to the cycle changes; if the calculated trend consistency score is lower than the lower limit of the interval, 4.0, the window is extended to a data sequence of 4 cycles to improve stability, forming the final signal processing result.

[0079] Please refer to Figure 3 , the working condition identification module includes:

[0080] The proportional structure discrimination sub-module uses the signal processing result to calculate the proportional difference between the methane concentration and the moisture concentration in the current cycle, identify the proportional structure, and generate proportional structure characteristic information;

[0081] The proportional structure discrimination sub-module calls the signal processing result, which includes the real-time measurement values of the methane concentration and the moisture concentration in the gas in the current cycle. First, it reads and calls the real-time measurement value of the methane concentration (such as 25.4%) and the real-time measurement value of the moisture concentration (such as 3.5%). Subsequently, according to the proportional difference calculation process, the methane concentration is divided by the moisture concentration, that is, 25.4%÷3.5% = 7.257, to determine the ratio of the methane to the moisture concentration in the current cycle. Then, a difference operation is performed between this ratio and the ratio in the previous cycle (such as the ratio in the previous cycle was 7.125), specifically 7.257 - 7.125 = 0.132, to obtain the proportional difference value of 0.132. A judgment is made on the proportional difference value. The set proportional difference judgment threshold is 0.15. The value-taking process of this threshold is based on the historical data of experimental statistics, analyzing the distribution of the stable proportional difference values during the operation in the past three months. The historical stable difference is between 0.05 and 0.20. After experimental verification, when the proportional difference exceeds 0.15, it belongs to an obvious change state. Then, according to the judgment action, since the current proportional difference of 0.132 is less than the set threshold of 0.15, it is determined that the proportional structure in this cycle has not changed significantly. Then, the current cycle ratio of 7.257 is recorded as the proportional structure characteristic information.

[0082] The heat and humidity section identification sub-module, based on the proportional structure characteristic information, identifies the heat and humidity level of the current working condition by comparing with multiple preset heat and humidity distribution sections, and obtains the heat and humidity level identification result;

[0083] The heat and humidity section identification sub-module calls the proportional structure characteristic information. According to the specific value of 7.257 of this characteristic information, it compares its value with multiple predefined heat and humidity distribution sections. The specific sections and the corresponding heat and humidity level definitions are shown in Table 2:

[0084] Table 2 Definition Table of Heat and Humidity Level Distribution Sections

[0085] Thermal and humidity level Lower limit value of ratio Upper limit value of ratio Level I 6.0 6.8 Level II 6.8 7.5 Level III 7.5 8.2 Level IV 8.2 9.0

[0086] As shown in Table 2, according to the actual value of 7.257, a one-by-one numerical comparison is made with the upper and lower limits of the section. The specific comparison actions are as follows: First, it is judged whether 7.257 is greater than the lower limit of Grade I, which is 6.0, and less than the upper limit of Grade I, which is 6.8. After comparison, 7.257 is higher than 6.8, so Grade I is excluded. Then, it is compared whether 7.257 is greater than the lower limit of Grade II, which is 6.8, and less than the upper limit of Grade II, which is 7.5. After comparison, 7.257 falls between 6.8 and 7.5, so it is determined that 7.257 meets the requirements of the Grade II heat and humidity level section. Furthermore, the deviation direction of the heat and humidity trend is judged, and the proportional characteristic value of the previous cycle, which is 7.125, is called. Specifically, 7.257 - 7.125 = 0.132 is greater than zero, so it is determined that the trend of this cycle is to deviate towards the higher grade. According to the degree of section coincidence, the current proportional value of 7.257 is far from the lower limit of Grade II, which is 6.8 (the distance is 0.457), and greater than the distance from the upper limit of Grade II, which is 7.5 (the distance is 0.243). It is confirmed that the coincidence degree of the current proportional value is higher than the upper-middle part of the Grade II interval. Considering the deviation trend towards the higher grade, the final identified result of the current heat and humidity level is "Grade II on the high side".

[0087] The control parameter configuration sub-module calls the heat and humidity level identification result, matches the control mode according to the heat and humidity level, and adjusts the control parameter configuration to obtain the heat and humidity status label;

[0088] The control parameter configuration sub-module calls the heat and humidity level identification result of "Grade II on the high side". Subsequently, through the corresponding relationship between the preset grade and the control mode, the mode threshold is set according to the actual operation data. Specifically, the mode threshold range for Grade I is set as the low-speed working condition (the operating speed range is 1200 - 1300 r / min), the mode threshold range for Grade II is set as the medium-speed working condition (the operating speed range is 1301 - 1400 r / min), Grade III is the sub-high-speed working condition (the operating speed range is 1401 - 1500 r / min), and Grade IV is the high-speed working condition (the operating speed range is 1501 - 1600 r / min). The mode threshold setting process is based on the statistical analysis of the long-term actual operation parameters of the equipment. The current heat and humidity level is "Grade II on the high side", so the working condition mode is determined to be the medium-speed working condition. The operating parameters are adjusted according to the high side grade. Specifically, the basic operating speed is 1301 r / min. When it is at the high side grade, the set speed is adjusted to near the upper limit of the medium-speed working condition interval (such as 1380 r / min). The cold source frequency and the opening angle of the gas inlet valve are adjusted. Specifically, the basic value of the cold source frequency is set to 35 Hz, and when it is at the high side grade, it is set to be increased to 38 Hz. The basic opening angle of the gas inlet valve is set to 50°, and it is increased to 54°. Finally, the above parameter configuration is called to generate the heat and humidity status label of the current operating cycle, which is "Grade II - Medium Speed - High Side".

[0089] Please refer to Figure 4 , the temperature zone control module includes:

[0090] The temperature direction recognition sub-module calls the thermal and humidity status tag to obtain the inlet temperature value in the current period and the temperature value in the previous period, calculates the temperature difference direction, judges the temperature change trend direction according to the consistency ratio of the temperature difference directions in consecutive periods, and obtains the temperature change direction index.

[0091] The temperature direction recognition sub-module calls the thermal and humidity status tag "Level II - Medium speed - Slightly high", extracts the real-time measured value of the inlet temperature corresponding to this tag in the current period as 36.5 °C, and at the same time calls the measured value of the corresponding inlet temperature in the previous period as 36.1 °C, performs the temperature difference operation as the temperature value in the current period minus the temperature value in the previous period, that is, 36.5 °C - 36.1 °C = 0.4 °C. Then, it judges the positive and negative of this difference. Since the difference of 0.4 °C is positive, the temperature change trend direction is marked as an increasing trend. Further, it retrieves and compares the temperature difference direction records of the nearest consecutive 6 periods one by one. The specific direction records are as follows: increasing, increasing, decreasing, increasing, increasing, increasing. It performs the calculation of the direction consistency ratio, taking the increasing trend direction as the consistency reference. The specific calculation process is to count the number of periods with an increasing direction, which is 5 periods, and then divide by the total number of all periods, which is 6 periods. Specifically, 5÷6 = 0.8333, and it is determined that the current temperature change trend direction index is an increasing trend consistency ratio of 83.33%.

[0092] The boundary trend evaluation sub-module identifies the temperature data in the middle of the temperature control range and the buffer range according to the temperature change direction index, and analyzes the persistence according to the number of state maintenance periods, using the formula:

[0093]

[0094] Calculate the boundary approach status score;

[0095] Among them, S b is the boundary approach status score, which is used to represent the degree of the temperature change trend approaching the boundary of the temperature control range. ΔY d is the difference amplitude of the temperature change direction index, which represents the difference between the temperature change direction index in the current period and the previous period. It is calculated and extracted by calculating the temperature difference within the period and then calculating the difference. C p is the number of consecutive periods in the transition area, which represents the total number of periods when the temperature data is continuously in the transition state between the temperature control range and the buffer range, and is obtained by cumulative statistics of the period identification sequence. m t is the total number of identification periods, which represents the total number of time periods currently used for trend and transition evaluation, and is obtained by the sliding time window parameter set within the module. is the normalized value of the temperature control range offset amplitude, which represents the value after normalization of the deviation degree of the current temperature data from the center of the temperature control range, and is obtained by taking the absolute value of the temperature data minus the center value of the temperature control range and then normalizing. is the normalized value of the cold source frequency perturbation, which represents the normalized result of the perturbation amplitude of the current cycle's cold source frequency compared to the reference state, and is obtained by normalizing the difference between the current frequency value and the set frequency reference;

[0096] The boundary trend evaluation sub-module calls the temperature change trend direction index of 83.33%, and based on the index value and the temperature control range settings, identifies the real-time temperature value within the transition range between the temperature control range and the buffer range. The specific temperature control range is set from 5.5°C to 36.5°C, the buffer range is from 36.5°C to 37.0°C, and the current temperature of 36.5°C is at the junction of the two ranges, determined to be in the transition area. The continuous cycle statistics show that the number of consecutive cycles in the transition area is 3 cycles. Subsequently, the boundary approach state score is calculated using the formula:

[0097]

[0098] In the formula, ΔY d is the amplitude of the temperature difference between the current cycle and the previous cycle, obtained by calling the cycle data. For example, subtracting the difference of 0.2°C in the previous cycle from the difference of 0.4°C in the current cycle gives 0.2°C; C p is the number of consecutive cycles, which is 3 cycles; m t The total number of identification cycles is set to 5 cycles, determined by fixed parameter settings; The normalized value of the temperature control range offset amplitude, obtained by normalizing the difference of 0.5°C between the current temperature of 36.5°C and the center of the temperature control range of 36.0°C (temperature range is 1°C), resulting in 0.5; The normalized value of the cold source frequency perturbation, obtained by normalizing the difference of 3 Hz between the current cycle's cold source frequency of 38 Hz and the set reference frequency of 35 Hz with a reference frequency range of 10 Hz, resulting in 0.3. The specific values of each parameter are shown in Table 3:

[0099] Table 3 Calculation parameter table for the boundary approach state

[0100]

[0101] The specific data substitution and calculation are as follows:

[0102] |ΔY d ·C p | = |0.2 × 3| = 0.6;

[0103]

[0104] Among them, the boundary proximity status score is used to quantify whether the current temperature regulation status of the system is in a critical transition state, especially to determine whether it is stably approaching the edge of the temperature control range, reflecting the risk degree of the system operation facing the switching of the thermal control range. This indicator integrates three key factors: the consistency of the trend direction, the dwell time length, and the control offset amplitude, to comprehensively judge the significance of the critical state. The larger its value, the more continuous and stable the direction of the system temperature change, the longer the residence time in the transition interval, and the more significant the physical deviation. The system is more likely to be at the forefront of the regional switching boundary. This indicator can be used to trigger the early response of control strategies such as interval label update and cold source frequency regulation, and is an important state-driven signal and frequency regulation basis in the temperature zone control module. The result shows that the boundary proximity status score is 0.1976. After experimental verification, the threshold of the boundary proximity status score is set to 0.15. Through historical data testing, the threshold is in the range of 0.1 - 0.25. Exceeding 0.15 is the boundary proximity state. Here, the result exceeds this threshold, determining that the current cycle is in the boundary proximity state, and outputting the boundary proximity status score of 0.1976.

[0105] The frequency command setting sub-module detects the boundary proximity status according to the boundary proximity status score and performs the update of the interval identification mark, retrieves the preset interval cold source frequency command parameter library, outputs the command parameters as the current control target, and obtains the cold source frequency setting;

[0106] The frequency command setting sub-module calls the boundary proximity status score of 0.1976, and then performs the boundary status detection according to the preset interval cold source frequency command parameter library. The specific library defines the cold source frequency command preset as shown in Table 4:

[0107] Table 4 Boundary Status Cold Source Frequency Command Parameter Library Table

[0108] Boundary proximity status score range Set value of cold source frequency (Hz) 0～0.10 35 0.11～0.15 36 0.16～0.20 37 0.21～0.25 38

[0109] As shown in Table 4, the current boundary proximity status score of 0.1976 falls within the interval of 0.16 - 0.20. It is determined that the corresponding cold source frequency setting value is 37Hz. Subsequently, the update action of the interval identification mark is performed. The specific operation is to record that the temperature control interval in the current cycle is increased from the original set upper limit of the medium-speed working condition mode of 36.5°C to the starting boundary of the buffer interval of 36.8°C, complete the setting of the frequency command parameter of 37Hz in this cycle, and finally obtain the cold source frequency setting of 37Hz.

[0110] Please refer to Figure 5 , the abnormal monitoring module includes:

[0111] The fluctuation extraction sub-module calls the cold source frequency setting, extracts the gas temperature, moisture concentration, and cooling return water temperature values in consecutive cycles, analyzes the change direction of each data item between adjacent cycles, and obtains the multi-parameter change direction trend sequence;

[0112] The fluctuation extraction sub-module calls the cold source frequency setting value of 37 Hz, and then extracts the real-time monitoring data of gas temperature, moisture concentration, and cooling return water temperature within four consecutive cycles respectively. The specific data acquisition process for each cycle is as follows: in the first cycle, the gas temperature is 36.2 °C, the moisture concentration is 3.4%, and the cooling return water temperature is 32.4 °C; in the second cycle, the gas temperature is 36.5 °C, the moisture concentration is 3.6%, and the cooling return water temperature is 32.7 °C; in the third cycle, the gas temperature is 36.7 °C, the moisture concentration is 3.8%, and the cooling return water temperature is 32.9 °C; in the fourth cycle, the gas temperature is 36.9 °C, the moisture concentration is 4.0%, and the cooling return water temperature is 33.1 °C. Then, the difference between adjacent cycles of each item of data is calculated for each cycle. For example, the difference calculation between the gas temperature in the second cycle and the gas temperature in the first cycle is 36.5 °C - 36.2 °C = 0.3 °C, the difference in moisture concentration is 3.6% - 3.4% = 0.2%, and the difference in cooling return water temperature is 32.7 °C - 32.4 °C = 0.3 °C. The positive and negative of the difference are judged one by one. The differences in the second cycle are all positive, indicating that the change directions are all upward. The difference calculation and direction marking are carried out for the subsequent cycles in turn to form a multi-parameter change direction trend sequence. The specific trend sequence is that in the second cycle, it rises, rises, rises; in the third cycle, it rises, rises, rises; in the fourth cycle, it rises, rises, rises. Finally, the multi-parameter change direction trend sequence obtained is: in cycles 2, 3, and 4, they all show a synchronous upward trend.

[0113] Based on the multi-parameter change direction trend sequence, the trend screening sub-module compares the consistency of the change directions of gas temperature, moisture concentration, and cooling return water temperature in the same cycle, and combines the consistency of the change amplitudes to screen the parameter combinations with synchronous trends, using the formula:

[0114]

[0115] Calculate the synchronous score of the linkage trend and generate a list of trend linkage combinations;

[0116] Among them, is the normalized change value of gas temperature, which is obtained by dividing the difference between the gas temperature in the current cycle and the gas temperature in the previous cycle by the average value of the gas temperature in the corresponding cycle. is the normalized change value of moisture concentration, which is obtained by dividing the difference between the moisture concentration in the current cycle and the moisture concentration in the previous cycle by the average value of the moisture concentration in the corresponding cycle. is the normalized change value of cooling return water temperature, which is obtained by dividing the difference between the cooling return water temperature in the current cycle and the cooling return water temperature in the previous cycle by the average value of the cooling return water temperature in the corresponding cycle. ΔQ * is the normalized fluctuation value of moisture concentration in the current cycle, which is obtained by dividing the standard deviation of moisture concentration by the average value. ΔS *is the normalized fluctuation value of the cooling return water temperature in the current cycle, obtained by dividing the standard deviation of the cooling return water temperature by the average value. k is the total number of cycles participating in the trend detection, j is the index variable representing the cycle order in the trend detection, and L w is the linkage trend synchronization score, reflecting the coupling strength of the change trends of each parameter within a given cycle;

[0117] The trend screening sub-module calls the trend sequence, compares the consistency of the change directions of the three parameters cycle by cycle. If the direction identifiers are the same within each cycle, it then calls the change amplitude of the corresponding cycle and calculates the normalized change value. Taking the data of the second cycle as an example, the change amplitude of the gas temperature is 0.3 °C. After normalization, it is 0.3 °C ÷ [(36.5 °C

[0118] + 36.2 °C) ÷ 2] = 0.00825. The change amplitude of the moisture concentration is 0.2%, and after normalization, it is 0.2% ÷ [(3.6% + 3.4%) ÷ 2] = 0.0571. The change amplitude of the cooling return water temperature is 0.3 °C, and after normalization, it is 0.3 °C ÷ [(32.7 °C + 32.4 °C) ÷ 2] = 0.00923. The normalized change values of each cycle are shown in Table 5:

[0119] Table 5 Calculation Table of Cycle Normalized Change Values

[0120] Cycle number Change value of gas temperature Change value of moisture concentration Change value of cooling return water temperature 2 0.00825 0.0571 0.00923 3 0.00546 0.0530 0.00610 4 0.00542 0.0513 0.00606

[0121] Subsequently, it calls the standard deviation and average value of the moisture concentration and the cooling return water temperature in each cycle. Taking the fourth cycle as an example, the standard deviation of the moisture concentration is 0.16%, the average value is 3.9%, and the normalized fluctuation value is 0.16% ÷

[0122] 3.9% = 0.0410. The standard deviation of the cooling return water temperature is 0.25 °C, the average value is 33.0 °C, and the normalized fluctuation value is 0.25 °C ÷ 33.0 °C = 0.00758. Substitute the normalized change values of each cycle into the formula:

[0123]

[0124] |0.00825 × 0.0571 × 0.00923 + 0.00546 × 0.0530 × 0.00610 + 0.00542 × 0.0513 × 0.00606| = 2.23 × 10 -6 + 1.77 × 10 -6 + 1.69 × 10 -6 = 5.69 × 10 -6 ;

[0125]

[0126] Among them, the linkage trend synchronization score is a parameter used to measure the consistency of the changing behaviors of three key variables, namely, gas temperature, moisture concentration, and cooling return water temperature, within consecutive periods. It reflects the coupling in the changing directions among them and whether the fluctuation intensities are coordinated within the current period. The larger this value is, the more it indicates that the three variables show consistent trends in multiple time segments and have synchronous fluctuation amplitudes, that is, there is a significant linkage at both the trend and disturbance levels. Its effect is used as a pre-indicator for abnormal state detection. The linkage trend synchronization score can be used to mark the moisture fluctuation linkage state, and it is one of the key parameters for judging the probability of condensate water abnormal accumulation events. It is also used to dynamically adjust the response intensity of the control system to the operating conditions at different stages and the signal constraint range. Determine the trend linkage combination list according to the linkage trend synchronization score, and set the trend synchronization threshold to 1×10 -6 , and exceeding the threshold indicates a significant linkage trend. The current score exceeds the threshold, and the output trend linkage combination list is "gas temperature - moisture concentration - cooling return water temperature".

[0127] The linkage determination sub-module identifies and marks the moisture fluctuation linkage states in multiple periods according to the trend linkage combination list, identifies condensate water abnormal accumulation events, adjusts the constraint range of the control signal in the current operating stage, and obtains the linkage fluctuation determination result;

[0128] The linkage determination sub-module calls the trend linkage combination list. According to this list and the statistical results of historical data periods, it judges the moisture fluctuation linkage state. Taking the trend linkage states in the recent four periods as linkage, linkage, linkage, and linkage respectively, it is determined that all four consecutive periods are in the linkage state. Subsequently, the determination of condensate water abnormal accumulation events is executed. By calling the normalized fluctuation value of moisture concentration 0.0410 and comparing it with the experimentally set abnormal accumulation determination threshold 0.03, the current normalized fluctuation value exceeds the threshold 0.03, and it is determined that condensate water has abnormal accumulation. Subsequently, the constraint range of the control signal in the current operating stage is adjusted. The specific adjustment process is to set the upper limit of the opening degree constraint of the gas inlet valve to be reduced from 54° to 52°, and at the same time, lower the upper limit of the cold source frequency from 38Hz to 36Hz to control the abnormal accumulation situation. Finally, the generated linkage fluctuation determination result is "abnormal accumulation - constraint adjustment state".

[0129] Please refer to Figure 6 , the drift identification module includes:

[0130] The temperature difference trajectory extraction sub-module calls the linkage fluctuation determination result, extracts the gas temperature and cooling return water temperature data within each cold source startup period, calculates the temperature mean values in the startup stage and the stable operation stage, analyzes the temperature difference change trajectory between the stages, and extracts the temperature difference data sequence to generate the stage temperature difference trajectory sequence;

[0131] The temperature difference trajectory extraction sub-module calls the linkage fluctuation determination result as "abnormal accumulation - constraint adjustment state", and successively calls the gas temperature and cooling return water temperature data of the last 3 cold source startups. Each startup cycle is divided into a startup stage (0 - 10 minutes after startup) and a stable operation stage (30 - 40 minutes after startup). Taking the first startup cycle as an example for the specific temperature acquisition instance, the gas temperatures in the startup stage are successively recorded as [34.8℃, 35.0℃, 35.3℃], and the cooling return water temperature is recorded as [30.5℃, 30.7℃, 31.0℃]. Calculate the average gas temperature in the startup stage as (34.8 + 35.0 + 35.3) / 3 = 35.03℃, and the average cooling return water temperature as (30.5 + 30.7 + 31.0) / 3 = 30.73℃. The gas temperature in the stable operation stage is [36.8℃, 37.0℃, 37.2℃], and the cooling return water temperature is [32.5℃, 32.7℃, 33.0℃]. The average values are calculated as the gas temperature (36.8 + 37.0 + 37.2) / 3 = 37.00℃ and the cooling return water temperature (32.5 + 32.7 + 33.0) / 3 = 32.73℃ respectively. Then calculate the temperature difference between stages, that is, subtract the average temperature in the startup stage from the stable operation stage. The gas temperature difference is 37.00℃ - 35.03℃ = 1.97℃, and the cooling return water temperature difference is 32.73℃ - 30.73℃ = 2.00℃, forming a stage temperature difference data sequence [1.97, 2.00]. Calculate the second and third startup cycles in the same way, and obtain the temperature difference data sequences [2.05, 2.12] and [1.92, 1.98] respectively, generating a complete stage temperature difference trajectory sequence.

[0132] Based on the stage temperature difference trajectory sequence, the response trend comparison sub-module calculates the trend characteristics of the temperature difference change trajectory in multiple startup records, compares the consistency of the change direction and amplitude between adjacent trajectories, filters out the convergent trajectory combinations and identifies the consistent trend patterns, generating the cold source heat response performance trend;

[0133] The response trend comparison sub-module calls the temperature difference trajectory sequence at the sub-module call stage, and successively calculates the trend characteristics between the trajectories. Specifically, the trend comparison is carried out with the temperature difference amplitude of the trajectory as the characteristic value. First, the 1st and 2nd temperature difference trajectories are called. The temperature differences of the gas temperature are 1.97 °C and 2.05 °C respectively, and the temperature differences of the cooling return water are 2.00 °C and 2.12 °C respectively. The difference calculations are 2.05 °C - 1.97 °C = 0.08 °C for the gas temperature and 2.12 °C - 2.00 °C = 0.12 °C for the cooling return water temperature. The change directions of the temperature differences are judged respectively. Both the gas temperature and the cooling return water temperature show an increasing trend, and the amplitude changes are close (a change less than 0.15 °C is regarded as a consistent trend), so it is determined that the 1st and 2nd trajectories are convergent; then the 2nd and 3rd temperature difference trajectories are called. The difference calculation of the gas temperature is 1.92 °C - 2.05 °C = -0.13 °C, and the temperature difference calculation of the cooling return water is 1.98 °C - 2.12 °C = -0.14 °C. The negative value indicates a decreasing trend, and the amplitude is less than 0.15 °C. The trend direction and amplitude judgment are consistent, so it is determined that the 2nd and 3rd trajectories are convergent; furthermore, according to the convergence between the trajectories, it is identified and confirmed that the trend consistent mode is that the temperature difference change amplitude is within ±0.15 °C and the direction changes synchronously. Finally, it is determined that the cold source heat response performance trend mode is the "consistent mode".

[0134] The drift behavior detection sub-module calls the cold source heat response performance trend, calculates the deviation degree between the current trend and the reference trend, and identifies the cold source heat response performance trend, detects the drift behavior, and generates the operation monitoring feedback result according to the deviation direction of the temperature difference trajectory, the deviation degree of the amplitude, and the number of continuous periods;

[0135] The drift behavior detection sub-module calls the "consistent mode" of the cold source heat response performance trend. Subsequently, the standard value of the temperature difference amplitude of the reference trend mode (determined by the initial stable performance trend of the cold source during experimental analysis) is set to 2.00 °C, and the average temperature difference amplitude of the current trend (1.97 + 2.05 + 1.92) / 3 = 1.98 °C is called. The deviation amplitude between the current trend and the reference trend is calculated as 1.98 °C - 2.00 °C = -0.02 °C. The deviation direction is judged. The negative value indicates a tendency towards the decreasing trend direction; the number of continuous periods is called. The current "consistent mode" has continued for 3 start-up periods. The critical continuous period number for judging the drift behavior is set to 2 periods. After exceeding the critical period number, it is judged as the drift state; in the current situation, the number of periods has exceeded the critical period number. Further, the drift threshold is set to 0.05 °C for the deviation amplitude value. The current deviation amplitude of 0.02 °C is less than the threshold of 0.05 °C. It is determined that the current drift amplitude is not obvious but has occurred continuously, and the operation monitoring feedback result is generated as "slight drift - trend towards low".

[0136] The above are only the preferred embodiments of the present invention and do not limit the present invention in other forms. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications to equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A gas-water separation system for flexible low-concentration gas internal combustion engine power generation, characterized in that The system includes: The signal acquisition module calls the sampled signal sequence, analyzes the consistency of the change trend of data in consecutive periods according to the change amplitude between the mean values of adjacent periods of each sampling channel, adjusts the sliding window length, updates the average data into the sliding queue, and generates a signal processing result; The working condition identification module uses the signal processing result to calculate the proportional difference between the methane concentration and the moisture concentration in the current period, identifies the proportional structure, matches it with the preset thermal and humidity distribution section, identifies the thermal and humidity level of the current working condition and matches the control mode, and generates a thermal and humidity status label; The temperature zone control module calls the thermal and humidity status label, analyzes the change direction of the current temperature data in consecutive periods, detects the boundary approaching state according to the persistence of the transition area between the temperature control interval and the buffer interval, adjusts the cold source frequency setting instruction, and obtains the cold source frequency setting; The anomaly monitoring module calls the cold source frequency setting, compares the consistency of the change direction of each sampled data in adjacent consecutive periods, filters the combined results with consistent trends and fluctuation amplitudes, detects the moisture fluctuation linkage state, adjusts the constraint range of the control signal, and generates a linkage fluctuation determination result.

2. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 1, characterized in that The signal processing result is specifically the response adjustment amplitude, the window sliding structure, and the periodic change direction. The thermal and humidity status label includes the thermal and humidity distribution level, the methane moisture ratio label, and the working condition control mode. The cold source frequency setting is specifically the frequency adjustment gear, the boundary state mark, and the temperature control interval index. The linkage fluctuation determination result includes the anomaly identification label, the synchronous trend index, and the moisture disturbance state.

3. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 1, characterized in that, The signal acquisition module includes: The difference calculation sub-module calls the sampled signal sequence, obtains the current period sampling values of the gas temperature sensor, the cooling water return temperature sensor, and the moisture concentration sensor, compares them with the mean value of the previous period, calculates the period difference of each item of data, and establishes a multi-source sensing difference sequence; The trend evaluation sub-module extracts the change direction and amplitude of the gas temperature difference, the cooling water temperature difference, and the moisture concentration difference in consecutive periods according to the multi-source sensing difference sequence, analyzes the consistency of the change trend of the data in consecutive periods, and calculates the operation trend consistency score; The window adjustment sub-module calls the trend consistency score, adjusts the sliding window length, including shortening the sliding window length when the score is higher than the trend consistency critical reference value, and lengthening the sliding window length when it is lower than the trend consistency critical reference value, updates the average data into the sliding queue, and establishes a signal processing result.

4. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 3, wherein, The working condition identification module includes: The proportional structure discrimination sub-module uses the signal processing result to calculate the proportional difference between the methane concentration and the moisture concentration in the current period, identifies the proportional structure, and generates proportional structure characteristic information; The thermal and humidity section identification sub-module compares the proportional structure characteristic information with a plurality of preset thermal and humidity distribution sections, and identifies the thermal and humidity level of the current working condition according to the interval overlap degree and the trend offset direction, and obtains the thermal and humidity level identification result; The control parameter configuration sub-module calls the thermal and humidity level identification result, matches the control mode according to the thermal and humidity level, and adjusts the control parameter configuration to obtain the thermal and humidity status label.

5. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 4, characterized in that, The temperature zone control module includes: The temperature direction recognition sub-module calls the thermal and humidity state tag, obtains the inlet temperature value in the current period and the temperature value in the previous period, calculates the temperature difference direction, determines the temperature change trend direction according to the consistency ratio of the temperature difference directions in consecutive periods, and obtains the temperature change direction index; The boundary trend evaluation sub-module identifies the temperature data in the middle of the temperature control range and the buffer range according to the temperature change direction index, analyzes the persistence according to the number of state maintenance periods, and calculates the boundary proximity state score; The frequency command setting sub-module detects the boundary proximity state according to the boundary proximity state score and updates the interval identification mark, retrieves the preset interval cold source frequency command parameter library, outputs the command parameter as the current control target, and obtains the cold source frequency setting.

6. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 5, wherein, The abnormal monitoring module includes: The fluctuation extraction sub-module calls the cold source frequency setting, extracts the gas temperature, moisture concentration, and cooling return water temperature values in consecutive periods, analyzes the change directions of each data item between adjacent periods, and obtains a multi-parameter change direction trend sequence; The trend screening sub-module compares the change direction consistency of the gas temperature, moisture concentration, and cooling return water temperature in the same period based on the multi-parameter change direction trend sequence, combines the consistency of the change amplitudes, screens the parameter combinations with synchronized trends, calculates the linkage trend synchronization score, and generates a list of trend linkage combinations; The linkage determination sub-module identifies and marks the moisture fluctuation linkage states in multiple periods according to the list of trend linkage combinations, identifies the abnormal accumulation event of condensate water, and adjusts the constraint range of the control signal in the current operation stage to obtain the linkage fluctuation determination result.

7. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 1, wherein The system further includes: The drift identification module calls the linkage fluctuation determination result, extracts and analyzes the cold source start records, calculates the temperature difference trajectory between the start stage and the stable operation stage, compares the change directions and trends between the temperature difference trajectories in multiple start records, identifies the cold source thermal response performance trend, and detects the drift behavior to generate an operation monitoring feedback result; The operation monitoring feedback result specifically refers to the thermal response change trend, the drift behavior discrimination result, and the stage performance record.

8. The gas-water separation system for flexible low-concentration gas internal combustion engine power generation according to claim 7, characterized in that, The drift identification module includes: The temperature difference trajectory extraction sub-module calls the linkage fluctuation determination result, extracts the gas temperature and cooling return water temperature data in each cold source start period, calculates the temperature mean values in the start stage and the stable operation stage, analyzes the temperature difference change trajectory between the stages and extracts the temperature difference data sequence to generate a stage temperature difference trajectory sequence; The response trend comparison sub-module calculates the trend characteristics of the temperature difference change trajectories in multiple start records based on the stage temperature difference trajectory sequence, compares the consistency of the change directions and amplitudes between adjacent trajectories, screens the convergent trajectory combinations and identifies the consistent trend pattern to generate the cold source thermal response performance trend; The drift behavior detection sub-module calls the cold source thermal response performance trend, calculates the deviation degree between the current trend and the reference trend, and identifies the cold source thermal response performance trend and detects the drift behavior according to the deviation degree of the temperature difference trajectory offset direction, amplitude, and the number of continuous periods to generate an operation monitoring feedback result.

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