A method and system for precise piston guidance in a labyrinth compressor

Abstract

The present application relates to the technical field of compressor, in particular to a labyrinth compressor piston accurate guiding method and system, by obtaining the piston offset data and the compressor operating condition data in the piston running process, analyzing the offset data, determining the piston guiding offset characteristic index; and based on the change of the compressor operating condition data, analyzing the significant degree of the influence of the compressor operating condition on the piston offset, and combining the piston guiding offset characteristic index, determining the piston guiding control characteristic value; based on the offset data and the compressor operating condition data, and combining the piston guiding control characteristic value, the compressor piston guiding control is carried out. The present application effectively improves the compressor piston guiding control precision by identifying the piston guiding offset characteristic.

Description

Technical Field

[0001] This invention relates to the field of compressor technology, and specifically to a method and system for precise piston guidance in a labyrinth compressor. Background Technology

[0002] A labyrinth compressor is a reciprocating piston oil-free compressor that utilizes non-contact labyrinth seal technology. It is primarily used for the compression and transportation of flammable, explosive, toxic gases, and gases under high pressure conditions. The labyrinth compressor's sealing structure is highly adaptable to media containing microparticles, significantly reducing wear on vulnerable parts and improving continuous operation efficiency. As a key component of the labyrinth compressor, the precise guidance of the piston directly determines the compressor's sealing effect, operational reliability, and service life.

[0003] Reciprocating piston compressors typically refer to compressors that use piston ring seals within the cylinder. Following the principle of "two points determine a straight line," the straight line of its reciprocating motion is determined by two points: the crosshead slide and the guide ring (support ring) on ​​the piston. Labyrinth compressors, as a branch of reciprocating piston compressors, use labyrinth seals and do not have guide rings (support rings) on the piston. Instead, they require an additional guide point, namely a "guide bearing," which, together with the crosshead slide, forms the "two points" determining the straight line. Due to the limitations of the overall structure of the labyrinth compressor, the distance between the guide bearing and the crosshead slide is relatively short, while the distance between the guide bearing and the piston is relatively long. The piston acts as an externally suspended guided element. When the compressor vibrates during operation, the short guide distance limits the constraint on the piston, making it easy for the piston to deviate from its intended straight-line motion trajectory, resulting in reduced piston straightness. For example, under high speed or high load conditions, this piston deviation caused by vibration becomes more pronounced, severely affecting the compressor's performance and stability. Summary of the Invention

[0004] To address the technical problem of reduced piston straightness due to limited constraint on the piston when the compressor vibrates during operation, the present invention aims to provide a method and system for precise piston guidance in a labyrinth compressor. The specific technical solution adopted is as follows:

[0005] In a first aspect, the present invention provides a method for precise guidance of a labyrinth compressor piston, comprising the following steps:

[0006] Acquire piston offset data and at least one compressor operating condition data during compressor piston operation, wherein the compressor operating condition data is used to reflect changes in compressor operating conditions;

[0007] The fluctuations in the offset data are analyzed to determine the piston guide offset characteristic index, which reflects the degree of fluctuation in piston guide change.

[0008] Based on the changes in compressor operating condition data, the significance of the compressor operating condition on piston offset is analyzed, and the piston guide offset characteristic index is combined to determine the piston guide control characteristic value, which is used to reflect the offset dynamic characteristics of piston guide.

[0009] Based on the offset data and the compressor operating condition data, and in conjunction with the piston guidance control characteristic value, compressor piston guidance control is performed.

[0010] In conjunction with the first aspect above, in some possible implementations, the offset data includes offset amount data and offset direction data. Analyzing the fluctuations in the offset data to determine the piston guide offset characteristic index includes:

[0011] Based on the fluctuation of the offset data in the offset data, an offset fluctuation index is determined, which is used to reflect the severity of the offset data fluctuation.

[0012] Based on the changes in the offset direction data in the offset data, an offset direction change index is determined, which is used to reflect the intensity of the change in the offset direction;

[0013] Based on the offset fluctuation index and the offset direction change index, the piston guide offset characteristic index is determined.

[0014] In conjunction with the first aspect above, in some possible implementations, the offset fluctuation index is determined based on the fluctuation of the offset data in the offset data, including:

[0015] Obtain the offset data within a specified time period prior to the current moment as the target offset data;

[0016] The difference between any offset in the target offset data and its previous offset is determined to obtain offset difference data;

[0017] Determine the range of the target offset data;

[0018] Based on the range value and the distribution of the difference values ​​in the offset difference data, the offset fluctuation index is determined.

[0019] In conjunction with the first aspect above, in some possible implementations, the offset fluctuation index is determined based on the range value and the distribution of the difference values ​​in the offset difference data, including:

[0020] Take the absolute value of the difference value in the offset difference data to obtain the absolute difference value data;

[0021] Determine the maximum interval between any adjacent positive and negative values ​​in the offset difference data;

[0022] Based on the difference in the number of positive and negative values ​​in the offset difference data, and the maximum interval, the degree of repetition of the offset change is determined, which reflects the degree of repetition of the offset change trend.

[0023] The offset fluctuation index is determined based on the range value, the degree of repetition of the offset change, and the slope of the absolute value of the difference data.

[0024] In conjunction with the first aspect above, in some possible implementations, the offset direction change index is determined based on the changes in the offset direction data in the offset data, including:

[0025] Divide the space into several offset direction intervals;

[0026] The time period before the current moment will be divided into several sub-time periods;

[0027] Based on the frequency of occurrence of the offset direction data in several sub-time periods, the dominant offset direction of the offset direction data in several sub-time periods is determined;

[0028] Based on the changes in the dominant offset direction of the offset direction data in several sub-time periods, the offset direction change index is determined.

[0029] In conjunction with the first aspect above, in some possible implementations, based on the changes in the dominant offset direction of the offset direction data over several sub-time periods, an offset direction change index is determined, including:

[0030] Based on whether the dominant offset direction of the offset direction data changes in adjacent sub-time periods, the continuous change period consisting of the sub-time periods is determined.

[0031] Determine the maximum change value of the offset direction data when the dominant offset direction changes in adjacent sub-time periods;

[0032] The offset direction change index is determined based on the proportion of the length of the continuous change period in all sub-time periods and the maximum change value.

[0033] In conjunction with the first aspect mentioned above, among some possible implementation methods, the influence of compressor operating conditions on piston offset is analyzed based on changes in compressor operating condition data, including:

[0034] Based on the fluctuations in the compressor operating condition data, the activity level of the operating condition data is determined, and the activity level is used to reflect the stability of the compressor operating condition.

[0035] Based on the degree of correlation between the compressor operating condition data and the offset data, as well as the activity level of the changes in the operating condition data, a dynamic correlation characteristic index between the operating condition data and the piston offset is determined. The dynamic correlation characteristic index is used to reflect the sensitivity of the piston offset to changes in the operating condition data.

[0036] Based on the dynamic correlation characteristic index between the operating condition data and piston offset corresponding to all compressor operating condition data, the significance of the compressor operating condition on piston offset is determined.

[0037] In conjunction with the first aspect above, in some possible implementations, determining the activity level of the operating condition data based on its fluctuations includes:

[0038] Obtain the compressor operating condition data within a set time period prior to the current moment as the target operating condition data;

[0039] Determine the range of the target operating condition data, and determine the total number of peaks and troughs in the target operating condition data;

[0040] The activity level of the operating condition data is determined based on the range of the target operating condition data and the total number of peaks and troughs.

[0041] In conjunction with the first aspect described above, in some possible implementations, compressor piston guidance control is performed based on the offset data and the compressor operating condition data, combined with the piston guidance control characteristic value, including:

[0042] The offset data of the compressor piston operation in the latest time period, the compressor operating condition data, and the corresponding piston guidance control feature value are input into the trained piston guidance prediction model, and the piston guidance prediction model is used to predict the piston guidance offset information.

[0043] Based on the predicted piston guide offset information, the piston guide trajectory adjustment device is controlled to adjust the piston's motion trajectory.

[0044] Secondly, the present invention also provides a labyrinth compressor piston precision guidance system, including a memory and a processor. The memory is used to store executable computer program code, and the processor is used to call and run the executable computer program code from the memory, causing the system to perform a labyrinth compressor piston precision guidance method according to the first aspect or any possible implementation thereof.

[0045] Thirdly, the present invention also provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to execute a labyrinth compressor piston precise guidance method as described in the first aspect or any possible implementation thereof.

[0046] Fourthly, the present invention also provides a computer-readable storage medium storing computer program code that, when executed on a computer, causes the computer to perform a labyrinth compressor piston precise guidance method according to the first aspect or any possible implementation thereof.

[0047] This invention offers the following advantages: By analyzing the fluctuations in piston offset data during compressor piston operation, a piston guiding offset characteristic index is determined. This index reflects the degree of fluctuation in piston guiding changes. Simultaneously, based on changes in compressor operating condition data during piston operation, the significant impact of compressor operating conditions on piston offset is analyzed. Combined with the piston guiding offset characteristic index, a piston guiding control characteristic value is determined. This value reflects the dynamic characteristics of piston guiding offset. Finally, based on the offset data and compressor operating condition data, and in conjunction with the piston guiding control characteristic value, compressor piston guiding control is implemented. This invention analyzes piston offset data and compressor operating condition data during compressor piston operation to extract piston guiding control characteristic values. These characteristic values ​​effectively reflect the dynamic characteristics of piston guiding offset caused by vibration, significantly improving the accuracy of compressor piston guiding control. Attached Figure Description

[0048] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating the steps of a precise piston guidance method for a labyrinth compressor according to an embodiment of the present invention.

[0050] Figure 2 This is a flowchart illustrating the steps for determining the piston guide offset characteristic index according to an embodiment of the present invention.

[0051] Figure 3 This is a flowchart illustrating the steps for determining the offset fluctuation index according to an embodiment of the present invention.

[0052] Figure 4 This is a flowchart illustrating the steps for determining the offset direction change index according to an embodiment of the present invention.

[0053] Figure 5 This is a flowchart illustrating the steps of analyzing the significant impact of compressor operating conditions on piston offset in an embodiment of the present invention.

[0054] Figure 6 This is a flowchart illustrating the steps of compressor piston guidance control according to an embodiment of the present invention;

[0055] Figure 7 This is a schematic diagram of the structure of a labyrinth compressor piston precision guidance system according to an embodiment of the present invention. Detailed Implementation

[0056] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0057] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0058] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0059] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0060] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0061] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of the present invention, this should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of the present invention, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.

[0062] Furthermore, it is understood that the data involved in the technical solutions of this invention (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and all parameters or indicators in the formulas involved in this invention are normalized values ​​that have eliminated the influence of dimensions.

[0063] The following will provide a detailed description of a precise piston guidance method and system for a labyrinth compressor provided by an embodiment of the present invention, with reference to the accompanying drawings.

[0064] Figure 1 This diagram illustrates the basic flow chart of a precise piston guidance method for a labyrinth compressor provided by an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps:

[0065] Step S100: Obtain piston offset data and at least one compressor operating condition data during compressor piston operation.

[0066] Among them, the compressor operating condition data is used to reflect the vibration changes during the compressor operation.

[0067] Ideally, during the operation of a labyrinth compressor, the piston should make precise reciprocating linear motion along the central axis of the cylinder, with the direction of motion always constant and without any swaying or deviation. Furthermore, the piston guide can perfectly adapt to various operating conditions of the labyrinth compressor. Whether operating at high or low speeds or with changes in load, the piston can maintain linear motion, ensuring that the compressor can operate efficiently and stably under different conditions.

[0068] However, in actual operation, many factors can interfere with the ideal guidance of the piston, such as the vibration generated by the compressor operation and the vibration interference of the external environment. These factors can cause the piston to deviate from the predetermined straight motion trajectory, reduce the straightness of operation, and thus lead to a series of problems such as accelerated wear of the piston and cylinder wall and shortened service life of components.

[0069] To address the aforementioned issues, this invention collects piston offset data and compressor operating condition data during piston operation. The offset data and operating condition data are then analyzed to determine piston guidance control characteristic values. These characteristic values ​​accurately reflect the dynamic characteristics of piston guidance offset. Therefore, based on the piston offset data and compressor operating condition data during piston operation, and combined with these piston guidance control characteristic values, precise guidance control is achieved for the labyrinth compressor piston.

[0070] Offset data refers to quantitative information that directly reflects the deviation of the compressor piston from its predetermined linear trajectory during operation. Its core function is to describe the degree of deviation between the actual piston position and the ideal trajectory. Accurate acquisition and analysis of offset data are crucial for assessing compressor operating status, predicting potential faults, optimizing maintenance strategies, and achieving precise directional control. In one possible implementation, offset data can include offset magnitude data and offset direction data. The offset direction reflects the azimuth deviation between the actual piston trajectory and the preset linear trajectory.

[0071] Compressor operating condition data refers to operating parameter data that reflects the vibration changes during compressor operation. Considering that mechanical inertia is the primary source of vibration generated during compressor operation, and that this inertia is generated by reciprocating moving parts, increasing speed significantly increases the inertial force, thereby exacerbating the radial runout of the piston rod and overall vibration. Simultaneously, the compressor load (pressure difference between the compressor inlet and outlet pipes) directly determines the magnitude of the axial force on the piston rod. When the pressure difference increases, the periodic alternating stress on the piston rod intensifies, leading to increased mechanical vibration amplitude and causing pressure fluctuations within the pipes. In one possible implementation, compressor operating condition data may include compressor speed data and compressor load data.

[0072] In a specific embodiment of the present invention, step S100, which involves acquiring the offset data of the compressor piston, includes: using displacement sensors installed at both ends and the middle of the piston stroke to collect the offset amount and offset direction during the piston's operation; averaging the offset amounts collected by different displacement sensors at the same moment during the piston's operation to obtain an average offset amount; determining a comprehensive offset direction based on the offset amounts and offset directions collected by different displacement sensors at the same moment during the piston's operation; and obtaining the offset data of the compressor piston's operation based on all average offset amounts and all comprehensive offset directions obtained during the piston's operation.

[0073] Specifically, based on the working environment and accuracy requirements of the labyrinth compressor, suitable high-precision displacement sensors, such as laser displacement sensors, are selected. Multiple sensors are installed at both ends and the middle of the piston stroke. For example, three sets of displacement sensors are evenly distributed around the top, bottom, and middle of the cylinder. All displacement sensors synchronously collect data to obtain the piston's offset data at different positions. The piston offset data collected by each set of displacement sensors includes the offset amount and offset direction (i.e., offset angle). The offset data collected by each set of sensors can be expressed as {( , ) , ( , ) … ( , ) …}.in, 、 、 They represent the first The, the +1, the first An offset, , , They represent the first The, the +1, the first The offset direction is calculated. The mean offset and the combined offset direction of the three sets of sensor offset data at each time step are obtained. Based on the mean offset and the combined offset direction, a comprehensive offset data sequence {( , ) , ( , ) … ( , )…}.in, , , They represent the first The, the +1, the first Average of each offset , , They represent the first The, the +1, the first The composite offset direction is calculated. The composite offset data sequence is split into two sequences: the average offset sequence and the composite offset direction sequence. These two sequences are then used as the final offset data and offset direction data.

[0074] The method for determining the overall offset direction is as follows: Each group of displacement sensors is assigned to the [missing information - likely a specific location or component]. The offset detected at each time point is denoted as The offset direction is denoted as offset Decomposed into horizontal components and vertical components , for the At each moment, the horizontal and vertical components of all displacement sensors are summed to obtain X and Y, and the overall offset direction is then determined. It is possible Then, the overall offset direction is determined based on the signs of X and Y. The quadrant is defined with 0° to the right in the horizontal direction. The angle is increased counterclockwise (i.e., counterclockwise is positive and clockwise is negative) to determine the overall offset direction, which ranges from 0 to ±180°.

[0075] In a specific embodiment of the present invention, step S100, acquiring compressor operating condition data during compressor piston operation, includes: selecting a speed sensor suitable for the compressor's working environment, such as a magnetoelectric speed sensor, and installing it near the compressor's crankshaft or other rotating parts; connecting the electrical signal output by the speed sensor to a tachometer, which can display the compressor's speed in real time; constructing speed data based on all acquired speeds, and using the speed data as a type of compressor operating condition data. Simultaneously, pressure sensors are installed on the compressor's inlet and outlet pipes, respectively. The electrical signals output by the pressure sensors are connected to a data acquisition system via cables. The data acquisition system acquires and converts the pressure signals, transforming them into actual pressure values; calculating the inlet and outlet pressure difference to obtain the compressor's load; constructing load data based on all acquired loads, and using the load data as another type of compressor operating condition data. Both types of compressor operating condition data and offset data are acquired synchronously at the same acquisition frequency (e.g., 10 times per second).

[0076] Step S200: Analyze the fluctuations in the offset data to determine the piston guide offset characteristic index.

[0077] Among them, the piston guide offset characteristic index is used to reflect the degree of fluctuation in piston guide change.

[0078] Analyzing piston offset data during compressor piston operation quantifies the fluctuation of piston guidance changes, thereby determining the piston guidance offset characteristic index. In one possible implementation, the offset amount and offset direction data can be analyzed separately to determine the piston guidance offset characteristic index. For example, a higher degree of fluctuation in both piston offset amount and offset direction indicates a more significant fluctuation in piston guidance changes, and the corresponding piston guidance offset characteristic index value is larger.

[0079] In one specific embodiment of the present invention, the offset data includes offset amount data and offset direction data, such as... Figure 2 As shown, step S200 analyzes the fluctuations in the offset data to determine the piston guide offset characteristic index, including:

[0080] Step S201: Determine the offset fluctuation index based on the fluctuation of the offset data in the offset data.

[0081] The offset fluctuation index is used to reflect the severity of the fluctuation in offset data.

[0082] The fluctuations in the offset data are analyzed to quantify the severity of these fluctuations, resulting in an offset fluctuation index, which is denoted as [index name missing]. For example, the greater the fluctuation in piston offset, the faster the rate of change, and the more repetitive the trend, the more frequent the fluctuation in piston offset. In this case, the offset fluctuation index... The larger the value, the better.

[0083] Step S202: Determine the offset direction change index based on the changes in the offset direction data in the offset data.

[0084] The offset direction change index is used to reflect the intensity of the change in offset direction.

[0085] Since the offset fluctuation index is obtained only from the analysis of piston offset, but the piston-guided offset may change in both magnitude and direction simultaneously, focusing solely on the change in offset cannot fully and completely reflect the true characteristics of piston offset. Therefore, an offset direction change index is determined based on the changes in the offset direction data, and denoted as [index missing]. For example, the stronger the continuity of the change in offset direction in the offset direction data, and the greater the degree of change, the more severe the change in the offset direction of the piston guide, and the corresponding offset direction change index. The larger the value, the better.

[0086] Step S203: Determine the piston guide offset characteristic index based on the offset fluctuation index and the offset direction change index.

[0087] Specifically, a preset normalization algorithm is used to normalize the offset fluctuation index. and the index of change in offset direction All values ​​are standardized to eliminate interference from differences in units and value ranges, resulting in the processed offset fluctuation index. and the index of change in offset direction Offset fluctuation index after fusion processing and the index of change in offset direction Determine the piston guide offset characteristic index; at this point, the piston guide offset characteristic index is obtained. .

[0088] Based on the above technical solution, the fluctuation of the offset amount data in the offset data is analyzed to quantify the severity of the offset amount data fluctuation, thereby determining the offset amount fluctuation index; at the same time, the fluctuation of the offset direction data in the offset data is analyzed to quantify the magnitude and duration of the offset direction change, thereby determining the offset direction change index; then, the offset amount fluctuation index and the offset direction change index are integrated to finally achieve accurate identification of the fluctuation degree of piston guide change, thereby determining the piston guide offset characteristic index.

[0089] In one specific embodiment of the present invention, such as Figure 3 As shown, step S201 determines the offset fluctuation index based on the fluctuation of the offset data in the offset data, including:

[0090] Step S2011: Obtain the offset data within the set time period before the current time as the target offset data.

[0091] Specifically, the target offset data is obtained from a period of time (e.g., 1 minute) prior to the current moment. This target offset data includes the offset at the current moment.

[0092] Step S2012: Determine the difference between any offset in the target offset data and its previous offset to obtain the offset difference data.

[0093] Specifically, the difference between the offsets of two adjacent moments in the target offset data (the difference between the subsequent offset and the preceding offset) is calculated and denoted as b. Since the time interval between adjacent moments is fixed, the value of b can represent the rate of change of the piston offset between adjacent moments. A positive value of b indicates that the piston offset has increased at adjacent moments, and a negative value of b indicates that the offset has decreased. Based on all the differences b in the target offset data, the offset difference data of the target offset data is constructed.

[0094] Step S2013: Determine the range of the target offset data.

[0095] Specifically, obtain the maximum and minimum values ​​in the target offset data, and calculate the range (i.e., the maximum value minus the minimum value), denoted as . This value, 'a', represents the range of piston offset over a historical period. A larger value indicates a greater fluctuation in piston offset over that period, and a stronger impact on the piston; conversely, a smaller value indicates a smaller range of displacement. The smaller the value, the smaller the fluctuation of its offset, and the more stably the piston can run along the predetermined trajectory.

[0096] Step S2014: Determine the offset fluctuation index based on the range value and the distribution of the difference values ​​in the offset difference data.

[0097] Specifically, the offset fluctuation index is determined by comprehensively considering the range value in the target offset data and the distribution of the difference values ​​in the offset difference data. For example, the larger the range value, the greater the offset fluctuation of the inner piston. The smaller the interval between the positive and negative difference values ​​in the offset difference data, the more cross-sectional the distribution of the positive and negative difference values, and the more repetitive the change direction of the piston offset. In this case, the value of the offset fluctuation index is larger.

[0098] In a specific embodiment of the present invention, the offset fluctuation index is determined based on the range value and the distribution of the difference values ​​in the offset difference data, including:

[0099] First, take the absolute value of the difference value in the offset difference data to obtain the absolute difference value data.

[0100] Specifically, determine the absolute value of all differences b in the offset difference data, and the absolute values ​​of all differences b constitute a difference absolute value data.

[0101] Secondly, determine the maximum interval between any adjacent positive and negative values ​​in the offset difference data.

[0102] Specifically, determine the interval between any two adjacent positive and negative values ​​in the offset difference data, and determine the maximum interval among all intervals, denoted as . In a data sequence, the interval between adjacent positive and negative values ​​refers to the number of data points traversed between one positive value and the next negative value (or vice versa), excluding the start and end points. For example, in the sequence [+1, +2, -3, +4, -5], +1 → -3: interval = 1 (interval with +2); +2 → -3: interval = 0 (directly adjacent); -3 → +4: interval = 0 (directly adjacent); +4 → -5: interval = 0 (directly adjacent); in this case, the maximum interval = 1.

[0103] Next, based on the difference in the number of positive and negative values ​​in the offset difference data, as well as the maximum interval, the degree of repetition of offset changes is determined.

[0104] Among them, the degree of repetition of the offset change reflects the degree of repetition of the offset change trend.

[0105] Specifically, in the offset difference data, the number of all positive and negative values ​​is obtained and recorded as follows: and .when and The smaller the difference between the two, the closer the number of positive and negative values ​​in the offset difference data; at the same time, the smaller the value of the maximum interval, the more the distribution of positive and negative values ​​in the offset difference data is intersecting, the more repetitive the direction of the piston offset change is, and the greater the value of the degree of repetition of the offset change.

[0106] In one specific embodiment of the present invention, based on the difference in the number of positive and negative values ​​in the offset differential data, and the maximum interval The degree of repetition in offset changes is determined by the following formula:

[0107]

[0108] In the formula: Indicates the degree of repetition in the offset change; This represents the denominator correction factor, used to prevent the denominator from being zero. and The value can be 0 or a positive number greater than 0. Set The value is 1.

[0109] Finally, the offset fluctuation index is determined based on the range and the degree of repetition of the offset changes, as well as the slope of the absolute difference data.

[0110] Specifically, a straight line is fitted to the absolute difference data, and the slope of the fitted line is obtained and denoted as c. The larger the value of c, the faster the absolute value of the difference increases in that time period, which means that the rate of change of piston offset is accelerating. Conversely, the smaller the value of c, the smaller the rate of change of offset is.

[0111] Furthermore, the slope c of the combined range value 'a', the absolute difference value data, and the degree of repetition of the offset changes are considered. Determine the offset fluctuation index; at this point, there is the piston offset fluctuation index. ;in, This represents an exponential function with base e as the natural constant, used to express... Positive correlation maps to the range of positive numbers. This is relevant when the range (a), the slope (c) of the absolute difference data, and the degree of repetition in the shift change are considered. The larger the value of B, the greater the fluctuation of the piston offset, the faster the offset changes, and the more repetitive the trend. This indicates that the piston offset fluctuates more frequently, and the corresponding value of B is larger.

[0112] In one specific embodiment of the present invention, such as Figure 4 As shown, in step S202, the offset direction change index is determined based on the changes in the offset direction data in the offset data, including:

[0113] Step S2021: Divide the area into several offset direction intervals.

[0114] Specifically, the offset direction is divided into 12 directional intervals at 30° intervals, and these intervals are numbered from 1 to 12.

[0115] Step S2022: Divide the time period before the current time into several sub-time periods.

[0116] Specifically, based on the data collection frequency (e.g., 10 times per second), three seconds in the period before the current moment (e.g., 1 minute) are taken as a sub-period (at which time, one sub-period corresponds to 30 data points), and the period before the current moment is divided into multiple smaller sub-periods.

[0117] Step S2023: Based on the frequency of occurrence of the offset direction data in several sub-time periods, determine the dominant offset direction of the offset direction data in several sub-time periods.

[0118] Specifically, within the local offset direction data corresponding to each sub-time period, the frequency of the offset direction appearing in each direction interval is counted. The direction interval with the highest frequency is taken as the dominant offset direction in that sub-time period. Based on this, the dominant offset direction of each sub-time period is determined. The numbers corresponding to the dominant offset directions are arranged in chronological order to obtain a dominant offset direction number sequence for a period of time before the current moment.

[0119] Step S2024: Determine the offset direction change index based on the changes in the dominant offset direction of the offset direction data in several sub-time periods.

[0120] Specifically, the changes in the dominant offset direction data over several sub-time periods are analyzed, such as analyzing the changes in the numbers within the dominant offset direction number sequence, determining the offset direction change index, and denoting it as... For example, the stronger the continuity of changes in the numbers within the dominant offset direction numbering sequence, and the greater the amount of change, the higher the corresponding offset direction change index. The larger the value, the better.

[0121] In a specific embodiment of the present invention, step S2024, which determines the offset direction change index based on the changes in the dominant offset direction of the offset direction data over several sub-time periods, includes:

[0122] First, based on whether the dominant offset direction of the offset direction data changes in adjacent sub-time periods, the continuously changing time periods constituted by the sub-time periods are determined.

[0123] Specifically, in the dominant offset direction numbering sequence, when the difference between two adjacent numbers is greater than 1, the dominant direction of the adjacent sub-time period is considered to have changed, and this is recorded as a changed period; otherwise, it is recorded as a stable period. If there is no stable period between two adjacent changed periods, they are considered continuous. The length of all continuous changed periods is determined and recorded as... .

[0124] Secondly, determine the maximum change value of the offset direction data when the dominant offset direction changes in adjacent sub-time periods.

[0125] Specifically, in the dominant offset direction numbering sequence, the absolute value of the difference between two adjacent numbers is determined as the numbering difference between the adjacent sub-time periods corresponding to those two numbers. The maximum value among all numbering differences is then determined as the maximum change value when the dominant offset direction of the offset direction data changes in adjacent sub-time periods, and denoted as... Maximum change value Used to indicate the magnitude of change in the dominant direction.

[0126] Finally, the offset direction change index is determined based on the proportion of the continuous change period in the total sub-time periods and the maximum change value.

[0127] Specifically, it integrates the proportion of the length of continuously changing periods among all sub-time periods and the maximum value of change. The characteristics of piston offset direction change are quantified to determine the offset direction change index. ;in, Indicates the length of all continuously changing time periods. This indicates the length of all sub-time periods.

[0128] Step S300: Based on the changes in compressor operating condition data, analyze the significance of the impact of compressor operating conditions on piston offset, and determine the piston guide control characteristic value by combining the piston guide offset characteristic index.

[0129] Among them, the piston guidance control characteristic value is used to reflect the offset dynamic characteristics of piston guidance.

[0130] During the operation of a labyrinth compressor, piston guide deviation is affected by multiple factors. Controlling solely based on the piston guide deviation characteristic index is insufficient to accurately maintain the predetermined trajectory. This is because variables such as speed and load not only act independently on the deviation but also exhibit nonlinear coupling relationships. For instance, speed fluctuations can amplify load disturbances, leading to overcompensation or undercompensation when adjusting a single parameter. To improve control accuracy, it is necessary to deeply analyze the intrinsic relationship between various factors and piston deviation, constructing a piston guide deviation characteristic index that comprehensively reflects the dynamic characteristics of piston guide deviation. This allows for precise adjustment and control of the piston guide, avoiding over- or under-adjustment.

[0131] Therefore, the changes in compressor operating condition data acquired during compressor piston operation were analyzed to determine the significance of the compressor operating condition's influence on piston offset, and this was denoted as... The significance level reflects the degree to which changes in compressor operating condition data significantly affect piston offset. For example, when compressor operating condition data changes rapidly and frequently, it increases the likelihood of the piston deviating from its predetermined trajectory, making the piston more prone to offset. In this case, the significance level of the changes in compressor operating condition data on piston offset is higher, and the corresponding significance level value is larger.

[0132] Furthermore, the degree of significance of the compressor's operating conditions on piston offset was determined. Subsequently, the significance of this influence was correlated with the piston guide offset characteristic index. By combining these, the piston guiding control characteristic value is obtained, at which point the piston guiding control characteristic value is obtained. In the formula: This represents the normalization function, used to normalize values ​​to the range [0,1]. The more significant the impact of operating condition changes on piston offset, and the more pronounced the piston guide offset index... The larger the value of K, the more precise the adjustment of the piston guide is required, and the more accurate the control of the magnitude and direction of the electromagnetic force is needed. If it is not adjusted in time, the piston may deviate from the predetermined trajectory, affecting the performance and stability of the compressor. The larger the K value, the more likely it is to be.

[0133] In one specific embodiment of the present invention, such as Figure 5 As shown, step S300 analyzes the significance of the compressor's operating conditions on piston offset based on changes in compressor operating condition data, including:

[0134] Step S301: Determine the activity level of the operating condition data based on the fluctuation of the compressor operating condition data.

[0135] Among them, the activity level is used to reflect the stability of the compressor's operating conditions.

[0136] The fluctuations in the operating data of each compressor were analyzed to determine the activity level of the operating data changes, and this activity was recorded as follows: The activity of this change It can reflect the stability of the compressor's operating conditions. For example, during the operation of a labyrinth compressor, changes in load and speed have a significant impact on piston offset. Regarding load and speed data, the larger the range of change and the more frequent the changes, the higher the activity and instability of these changes over a historical period. This increases the likelihood of the piston deviating from its intended trajectory, making it more prone to offset.

[0137] Step S302: Based on the degree of correlation between the compressor operating condition data and the offset data, and the activity level of the operating condition data, determine the dynamic correlation characteristic index between the operating condition data and the piston offset.

[0138] Among them, the dynamic correlation characteristic index is used to reflect the sensitivity of piston offset to changes in operating condition data.

[0139] Specifically, taking the target operating condition data corresponding to the speed data as an example, the Pearson correlation coefficient between the target operating condition data corresponding to the speed data and the offset data in the offset data within a set time period before the current moment is calculated as the degree of correlation, and denoted as . degree of correlation of changes This is used to represent the correlation between changes in rotational speed and piston offset. The greater the correlation, the greater the offset. The smaller the correlation, the less the increase in rotational speed will cause a change in offset.

[0140] Furthermore, the degree of integration and change is related. and activity level Determine the dynamic correlation characteristic index between the rotational speed data and piston offset; at this point, there is a dynamic correlation characteristic index. Among them, the higher the activity of the rotational speed data and the greater the correlation with the offset, the corresponding... The larger the value, the more sensitive the piston offset is to changes in rotational speed.

[0141] Following the same method described above, the Pearson correlation coefficient between the target load data corresponding to the load data and the offset data in the offset data within a set time period prior to the current moment can be obtained as the degree of correlation, and denoted as . This leads to the dynamic correlation characteristic index between load data and piston offset, at which point the dynamic correlation characteristic index is obtained. Among them, the dynamic correlation feature index The larger the value, the closer the load and piston offset are, and the more sensitive the piston offset is to changes in the coefficient.

[0142] Step S303: Based on the dynamic correlation characteristic index between the operating condition data and piston offset corresponding to all compressor operating condition data, determine the degree of significance of the compressor operating condition on piston offset.

[0143] Specifically, a preset normalization algorithm is used to analyze the dynamic correlation characteristic index between rotational speed data and piston offset. and the dynamic correlation characteristic index between load data and piston offset All values ​​are standardized to eliminate interference from differences in units and value ranges, resulting in the processed dynamic correlation characteristic index. and dynamic correlation feature index And calculate the processed dynamic correlation feature index. and dynamic correlation feature index average and the average value The significance of the compressor's operating conditions on piston offset is denoted as H. The larger the significance H, the more significant the effect of the compressor's operating conditions on piston offset.

[0144] Based on the above technical solution, by analyzing the fluctuation of compressor operating condition data, the activity level of the operating condition data is determined to quantify the stability of the compressor operating condition. At the same time, by combining the degree of correlation between the compressor operating condition data and the offset data, the dynamic correlation characteristic index between each type of operating condition data and piston offset is determined. Then, by fusing the dynamic correlation characteristic indices corresponding to all compressor operating condition data, the significance of the impact of compressor operating condition on piston offset is finally obtained.

[0145] In a specific embodiment of the present invention, step S301, which determines the activity level of the operating condition data based on the fluctuation of the compressor operating condition data, includes:

[0146] First, obtain the compressor operating condition data for a set period of time prior to the current moment as the target operating condition data.

[0147] Specifically, for each type of compressor operating condition data, namely load data and speed data, the compressor operating condition data within a certain period of time (e.g., 1 minute) prior to the current moment is obtained as the target operating condition data. The target operating condition data includes the operating condition data value at the current moment.

[0148] Secondly, determine the range of the target operating condition data and the total number of peaks and troughs in the target operating condition data.

[0149] Specifically, taking the target operating condition data corresponding to the speed data as an example, the maximum and minimum values ​​in the target operating condition data are obtained, and the range value (i.e., maximum value minus minimum value) is calculated based on the maximum and minimum values, and denoted as... The range Used to represent the range of speed variation over a historical period. The larger the value, the more likely the compressor has undergone a wider range of speed adjustments due to increased workload requirements. The smaller the value, the more stable the compressor's operating conditions are, and the smaller the impact on piston guidance.

[0150] Simultaneously, based on the time-varying curve of the target operating condition data, the total number of peaks and troughs in the curve is determined and denoted as... Each peak or trough indicates a change in the rotational speed at that point, specifically a shift from increasing to decreasing or vice versa. The larger the value, the more frequently the rotational speed changes direction during this period; The smaller the value, the more stable the rotation speed.

[0151] Using the same method described above, the range of the target operating condition data corresponding to the load data can be determined, as well as the total number of peaks and troughs in the target operating condition data.

[0152] Finally, the activity level of the operating condition data is determined based on the range of the target operating condition data and the total number of peaks and troughs.

[0153] Specifically, taking the target operating condition data corresponding to the speed data as an example, the range values ​​are fused. And the total number of peaks and troughs. Determine the activity level of the operating condition data; at this point, there is activity level. Among them, the larger the range of speed variation and the more frequent the changes, the higher the activity and instability of the changes over a historical period. The corresponding value of D is larger. Rapid and frequent changes increase the possibility of the piston deviating from the predetermined motion trajectory, making the piston more prone to deviation. Conversely, the smaller the value of D, the more accurately the piston can reciprocate along the predetermined straight motion trajectory in the cylinder under a more stable force state, reducing the risk of deviation caused by sudden changes in inertial force.

[0154] Following the same method described above, the activity level of the target operating condition data corresponding to the load data can be determined, denoted as E. A larger value for E may indicate that the compressor load has undergone frequent and significant changes over a historical period. High load change activity will make the force conditions on both sides of the piston complex and variable, increasing the possibility and degree of piston displacement. A smaller value for E indicates that the load changes over a historical period are relatively gradual, reducing the possibility and degree of piston displacement.

[0155] Step S400: Based on the offset data and compressor operating condition data, and combined with the piston guidance control characteristic value, perform compressor piston guidance control.

[0156] Based on the piston offset data and compressor operating condition data during compressor piston operation, and combined with piston guidance control characteristic values, the current guidance information of the piston is predicted. Based on the predicted guidance information, the piston movement trajectory is adjusted using a piston guidance trajectory adjustment device to make it as close as possible to the predetermined straight-line movement trajectory, thereby achieving precise control of compressor piston guidance.

[0157] In one specific embodiment of the present invention, such as Figure 6 As shown, step S400 involves performing compressor piston guidance control based on offset data and compressor operating condition data, combined with piston guidance control characteristic values, including:

[0158] Step S401: Input the offset data of the compressor piston operation in the latest time period, the compressor operating condition data, and the corresponding piston guidance control feature values ​​into the trained piston guidance prediction model, and use the piston guidance prediction model to predict the piston guidance offset information.

[0159] Specifically, the compressor's operating condition data (including speed data and load data) and piston offset data (including offset amount data and offset direction data) over a historical period are collected, along with the piston guidance control characteristic value K obtained based on this historical data. These data are then used to construct a training sample set, which includes training set data and validation set data.

[0160] A suitable neural network architecture, such as a Long Short-Term Memory (LSTM) network, is selected to construct a piston-guided prediction model, which is essentially a time-series prediction model. This piston-guided prediction model is trained using training set data. During training, an appropriate loss function (such as the mean squared error loss function) is chosen to measure the difference between the predicted and actual values. The network weights and biases are adjusted using the backpropagation algorithm to minimize the loss function. During training, validation set data is used to monitor the model's performance and prevent overfitting.

[0161] During the training of the piston guidance prediction model, the piston guidance control feature value K is used as one of the input features. A larger K value carries richer information about piston guidance instability, which can enhance feature expression and provide more important reference for determining the control strategy of the piston guidance prediction model. A smaller K value indicates that the piston guidance change is relatively simple and regular. During the learning process, the neural network can relatively simplify the modeling of the relationship between this part of the data features and piston offset.

[0162] During the operation of the piston in the labyrinth compressor, the piston's offset data (including speed data and load data) within a set time period (such as the most recent 1 minute) before the latest moment, the offset data of the piston (including offset amount data and offset direction data), and the calculated piston guidance control characteristic value K are input into the trained piston guidance prediction model. The piston guidance prediction model is used to obtain the predicted piston guidance offset information. The piston guidance offset information mainly consists of the offset amount and offset direction of the piston relative to the predetermined linear motion trajectory. The offset amount and offset direction can directly represent the deviation of the piston's current position from the predetermined trajectory.

[0163] Step S402: Based on the predicted piston guide offset information, control the piston guide trajectory adjustment device to adjust the piston's motion trajectory.

[0164] A piston guide trajectory adjustment device is pre-set. The function of this piston guide trajectory adjustment device is to generate control commands based on the predicted piston guide offset information, and adjust and control the piston's motion trajectory based on the control commands to make it as close as possible to the predetermined linear motion trajectory.

[0165] Specifically, select coils and permanent magnets of appropriate specifications. Based on the piston's structure and space constraints, design the layout of the coils and permanent magnets to ensure that their placement does not affect the normal movement of the piston and generates sufficient and uniform electromagnetic force to control the piston's guidance. Then, install the coils and permanent magnets on the piston or piston rod according to the designed layout, ensuring a secure connection, good contact, and proper insulation. After preliminary electromagnetic performance testing, install them into the labyrinth compressor, ensuring accurate installation and good compatibility with other components, thus obtaining the configured piston guide trajectory adjustment device.

[0166] During the real-time operation of the piston in the labyrinth compressor, based on predicted piston guide offset information, optimization algorithms (such as gradient descent) are used to calculate the magnitude and direction of the electromagnetic force required to return the piston to a predetermined linear motion trajectory. The calculated magnitude and direction of the electromagnetic force are converted into actual control signals, and based on these signals, coils arranged inside the piston or on the piston rod are controlled. By controlling the magnitude and direction of the current in the coils, a corresponding electromagnetic force is generated, which acts on the piston, thereby adjusting the piston's motion trajectory to make it as close as possible to the predetermined linear motion trajectory.

[0167] It should be understood that the above only provides a specific and feasible structure for a piston guide trajectory adjustment device. Based on this structure, control commands corresponding to the piston guide trajectory adjustment device can be generated according to the predicted piston guide offset information. Thus, the piston guide can be precisely controlled by the piston guide trajectory adjustment device based on the control commands, ensuring that the piston moves along the predetermined straight motion trajectory as much as possible under various operating conditions, making the piston movement more stable and improving the stability and reliability of the compressor operation. In order to achieve this goal, other structures of piston guide trajectory adjustment devices can also be set, which are not limited here.

[0168] Based on the above technical solution, by using the offset data of the compressor piston operation in the latest time period, the compressor operating condition data, and the corresponding piston guidance control characteristic values, the piston guidance prediction model is used to accurately predict the latest piston guidance, detect potential piston offset problems in advance, and use the piston guidance trajectory adjustment device installed in the compressor to accurately control the piston guidance, thereby ensuring that the piston runs along the predetermined straight motion trajectory as much as possible under various operating conditions, making the piston movement more stable, extending the service life of the internal components of the compressor, and improving the stability and reliability of the compressor operation.

[0169] Based on the same inventive concept, embodiments of the present invention also provide a precise piston guiding system for a labyrinth compressor, such as... Figure 7 As shown, the system includes: a memory, a processor, and computer program code stored in the memory and running on the processor, wherein when the processor executes the computer program code, the system is able to perform any of the aforementioned labyrinth compressor piston precise guidance methods.

[0170] In this embodiment of the invention, the system can be divided into functional modules according to the above method example. For example, each module can correspond to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0171] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute any of the aforementioned labyrinth compressor piston precise guidance methods.

[0172] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing computer program code that, when executed on a computer, causes the computer to perform any of the aforementioned labyrinth compressor piston precise guidance methods.

[0173] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method of precise guiding of a labyrinth compressor piston, characterized in that, The method comprises the following steps: obtaining offset data of a compressor piston during operation of the piston and at least one compressor operating condition data reflecting changes in the compressor operating condition; analyzing fluctuations in the offset data to determine a piston guide offset characteristic index reflecting the fluctuation degree of the piston guide change; based on changes in the compressor operating condition data, analyzing the significant degree of the influence of the compressor operating condition on the piston offset, and combining the piston guide offset characteristic index, determining a piston guide control characteristic value reflecting the dynamic characteristics of the piston guide offset; based on the offset data and the compressor operating condition data, and combining the piston guide control characteristic value, performing compressor piston guide control.

2. A method of precision guiding of a labyrinth compressor piston according to claim 1, characterized in that, The offset data includes offset amount data and offset direction data, and the analysis of the fluctuations in the offset data to determine the piston guide offset characteristic index comprises: based on the fluctuation of the offset amount data in the offset data, determining an offset amount fluctuation index reflecting the intensity of the offset amount data fluctuation; based on the change of the offset direction data in the offset data, determining an offset direction change index reflecting the change intensity of the offset direction; based on the offset amount fluctuation index and the offset direction change index, determining the piston guide offset characteristic index.

3. A method of precision guiding a labyrinth compressor piston according to claim 2, characterized in that, Based on the fluctuation of the offset amount data in the offset data, determining an offset amount fluctuation index comprises: obtaining offset amount data in a set period before the current time as target offset amount data; determining the difference between any one of the target offset amount data and its previous offset amount to obtain offset amount difference data; determining the range value of the target offset amount data; based on the range value and the distribution of the difference value in the offset amount difference data, determining the offset amount fluctuation index.

4. A method of precision guiding a labyrinth compressor piston according to claim 3, characterized in that, Based on the range value and the distribution of the difference value in the offset amount difference data, determining the offset amount fluctuation index comprises: taking the absolute value of the difference value in the offset amount difference data to obtain difference absolute value data; determining the maximum interval between any adjacent positive and negative values in the offset amount difference data; based on the number difference between the positive and negative values in the offset amount difference data and the maximum interval, determining the offset amount change repetition degree reflecting the repetition degree of the offset amount change trend; based on the range value and the offset amount change repetition degree, and the slope of the difference absolute value data, determining the offset amount fluctuation index.

5. A method of precision guiding a labyrinth compressor piston according to claim 2, characterized in that, Based on the change of the offset direction data in the offset data, determining an offset direction change index comprises: dividing a plurality of offset direction intervals; dividing a set period before the current time into a plurality of sub-time periods; based on the occurrence frequency of the offset direction data in the plurality of sub-time periods, determining the dominant offset direction of the offset direction data in the plurality of sub-time periods; Determine a deflection direction change index based on the change of the dominant deflection direction of the deflection direction data in a plurality of sub-time periods.

6. A method of precision guiding a labyrinth compressor piston according to claim 5, characterized in that, Determine a deflection direction change index based on the change of the dominant deflection direction of the deflection direction data in a plurality of sub-time periods, including: Determine a continuous change period formed by sub-time periods based on whether the dominant deflection direction of the deflection direction data in adjacent sub-time periods changes; Determine a maximum change value when the dominant deflection direction of the deflection direction data in adjacent sub-time periods changes; Determine a deflection direction change index based on the length proportion of the continuous change period in all sub-time periods and the maximum change value.

7. A method of precision guiding of a labyrinth compressor piston according to claim 1, characterized in that, Analyze the significant degree of the influence of the compressor operating condition on the piston deflection based on the change of the compressor operating condition data, including: Determine a change activity of the operating condition data based on the fluctuation of the compressor operating condition data, the change activity being used to reflect the stability degree of the compressor operating condition; Determine a dynamic correlation characteristic index between the operating condition data and the piston deflection based on the change correlation degree between the compressor operating condition data and the deflection data and the change activity of the operating condition data, the dynamic correlation characteristic index being used to reflect the sensitivity degree of the piston deflection to the change of the operating condition data; Determine the significant degree of the influence of the compressor operating condition on the piston deflection based on the dynamic correlation characteristic index between the operating condition data and the piston deflection corresponding to all compressor operating condition data.

8. A method of precision guiding a labyrinth compressor piston according to claim 7, characterized in that, Determine a change activity of the operating condition data based on the fluctuation of the compressor operating condition data, including: Obtain the compressor operating condition data in a set period before the current time as target operating condition data; Determine the range value of the target operating condition data and determine the total number of wave crests and troughs in the target operating condition data; Determine a change activity of the operating condition data based on the range value of the target operating condition data and the total number of wave crests and troughs.

9. A method of precision guiding of a labyrinth compressor piston according to claim 1, characterized in that, Perform compressor piston guiding control based on the deflection data and the compressor operating condition data and in combination with the piston guiding control characteristic value, including: Input the deflection data of the compressor piston operating in the latest period, the compressor operating condition data and the corresponding piston guiding control characteristic value to the trained piston guiding prediction model, and predict the piston guiding deflection information by using the piston guiding prediction model; Control the piston guiding track adjustment device based on the predicted piston guiding deflection information to adjust the movement track of the piston.

10. A labyrinth compressor piston precision guiding system, characterized in that, The computer program product comprises a memory, a processor, and executable computer program code stored in the memory and executable on the processor, and the processor executes the computer program code to perform the labyrinth compressor piston accurate guiding method according to any one of claims 1-9.

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