A real-time dynamic control method for pressure controller

Abstract

The present application relates to the field of pressure detection technology, and specifically to a real-time dynamic control method for a pressure controller, the method comprising: collecting pressure electrical signals of a preset number of moments before the current moment of the electronic pressure controller to form a pressure electrical signal sequence at the current moment, and equally dividing the pressure electrical signal sequence into floating sequences of various pressure ranges; obtaining the pressure safety assessment factor of each element in the pressure range floating sequence according to the distribution characteristics of the elements in the pressure range floating sequence, obtaining the pressure instability characteristic value of the pressure range floating sequence, obtaining the pressure transformation characteristic sequence, obtaining the pressure electrical signal trend variable of the pressure transformation characteristic sequence, determining the adaptive weight parameter in the exponential moving average algorithm in combination with the pressure electrical signal trend variable, and completing the real-time detection of the pressure electrical signal of the electronic pressure controller using the exponential moving average algorithm. The present application improves the accuracy of real-time detection of pressure electrical signals.

Description

Technical Field

[0001] The present application relates to the technical field of pressure detection, and in particular to a real-time dynamic control method for a pressure controller. Background Art

[0002] Electronic pressure controllers are widely used in industrial automation industries such as high-precision assembly of manufacturing parts and components, generation of industrial electromagnetic valve control signals, etc. Existing electronic pressure controllers mostly use an integrated mode of monitoring, control, and servo drive to form online dynamic monitoring and management of pressure-related data information calculation, analysis, and statistics, meeting various application requirements in existing automation industrial scenarios.

[0003] However, in the actual production process, the equipment operating environment is dusty, oily, and vibrating, which results in the high failure rate, high computing power consumption, and high price of existing electronic pressure controllers, making them difficult to apply and promote on a large scale. Therefore, it is necessary to monitor the pressure numerical signal in the electronic pressure controller to ensure the control accuracy of the pressure controller. Summary of the invention

[0004] In view of the above, it is necessary to provide a real-time dynamic control method for a pressure controller, which improves the accuracy of pressure electrical signal detection compared to the traditional real-time detection method of pressure electrical signals of electronic pressure controllers.

[0005] A real-time dynamic control method for a pressure controller of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a real-time dynamic control method for a pressure controller, the method comprising the following steps:

[0007] Collect the pressure electrical signals of the electronic pressure controller at a preset number of times before the current time to form a pressure electrical signal sequence at the current time;

[0008] The pressure electric signal sequence is evenly divided into floating sequences of each pressure range; the pressure safety assessment factor of each element in the floating sequence of the pressure range is obtained according to the distribution characteristics of the elements in the floating sequence of the pressure range;

[0009] Analyze the distribution of pressure safety assessment factors of all elements in the pressure range floating sequence to obtain the pressure instability characteristic value of the pressure range floating sequence; combine the pressure instability characteristic values ​​of all pressure range floating sequences into a pressure transformation characteristic sequence;

[0010] The trend changes of multiple local elements in the pressure transformation characteristic sequence are analyzed to obtain the pressure electric signal trend variables of the pressure transformation characteristic sequence; the adaptive weight parameters in the exponential moving average algorithm are determined in combination with the pressure electric signal trend variables to perform real-time detection of the pressure electric signal of the electronic pressure controller.

[0011] In one embodiment, the method for determining the pressure safety assessment factor of each element in the pressure range floating sequence is:

[0012] Acquire floating data in the pressure electric signal sequence based on the element distribution characteristics in the pressure electric signal sequence;

[0013] Determine the pressure electric signal floating coefficient of the pressure electric signal sequence based on the discrete degree of the elements in the pressure electric signal sequence;

[0014] The mean of the floating data is used as the floating reference value of the pressure electric signal. For each pressure range floating sequence, the absolute value of the difference between each element and the pressure electric signal floating reference value is calculated. If the absolute value of the difference is greater than the pressure electric signal floating coefficient, the pressure safety assessment factor of the corresponding element in the pressure range floating sequence is positively correlated with the absolute value of the difference. Otherwise, the pressure safety assessment factor of the corresponding element in the pressure range floating sequence is a preset value.

[0015] In one embodiment, the floating data acquisition process is:

[0016] Arrange all elements in the pressure electric signal sequence in ascending order, and take all elements between the upper quartile and the lower quartile of the elements after ascending order as floating data.

[0017] In one embodiment, the pressure electric signal fluctuation coefficient is the standard deviation of all elements in the pressure electric signal sequence.

[0018] In one embodiment, the process of determining the pressure instability characteristic value is:

[0019] The mean of the absolute values ​​of the differences of all elements in the pressure range floating sequence is calculated and determined as the pressure floating mean; the mean of the pressure safety assessment factors of all elements in the pressure range floating sequence is calculated and determined as the pressure safety mean; the pressure instability characteristic value of the pressure range floating sequence is positively correlated with the pressure floating mean and the pressure safety mean.

[0020] In one embodiment, the method for determining the pressure electric signal trend variable is:

[0021] Decomposing the pressure transformation characteristic sequence to obtain a residual sequence, and obtaining each subsequence of the pressure transformation characteristic sequence and each residual subsequence of the residual sequence based on the distribution of abnormal elements in the pressure transformation characteristic sequence;

[0022] Determine the residual trend influence coefficient of each subsequence of the pressure transformation characteristic sequence based on the distribution of elements in the residual sequence and each residual subsequence;

[0023] For the pressure conversion characteristic sequence, the cumulative sum of the residual trend influence coefficients of all subsequences is calculated, and the ratio of the residual trend influence coefficient of each subsequence to the cumulative sum is used as the first pressure trend ratio. The difference between the number of subsequences contained in the pressure conversion characteristic sequence and the sequence number of each subsequence is calculated, and the ratio of the number of elements contained in each subsequence to the difference is used as the second pressure trend ratio. The product of the first pressure trend ratio and the second pressure trend ratio is used as the first trend product. The pressure electrical signal trend variable of the pressure conversion characteristic sequence is the sum of the first trend products of all subsequences.

[0024] In one embodiment, the method for determining each subsequence of the pressure change characteristic sequence and each residual subsequence of the residual sequence is:

[0025] An anomaly detection algorithm is used to obtain each mutation point in the pressure transformation feature sequence, and each mutation point is used as a segmentation point of the pressure transformation feature sequence to obtain each subsequence of the pressure transformation feature sequence, and the corresponding point of the segmentation point in the residual sequence is obtained. The residual sequence is segmented using the corresponding points to obtain each residual subsequence of the residual sequence.

[0026] In one embodiment, the process of determining the residual trend influence coefficient is:

[0027] The sum and mean of the residual sequence of the pressure transformation characteristic sequence are calculated to determine them as the first residual sum and the first residual mean. The sum and mean of the residual subsequences corresponding to each subsequence of the pressure transformation characteristic sequence are calculated to determine them as the second residual sum and the second residual mean. The absolute value of the difference between the first residual mean and the second residual mean is calculated, and the product of the absolute value of the difference and the second residual sum is determined as the first residual product. The residual trend influence coefficient of each subsequence of the pressure transformation characteristic sequence is positively correlated with the first residual sum, and negatively correlated with the first residual product.

[0028] In one embodiment, the step of determining the adaptive weight parameter in the exponential moving average algorithm is as follows:

[0029] The adaptive weight parameter in the exponential moving average algorithm is positively correlated with the pressure electric signal trend variable of the pressure transformation feature sequence, and negatively correlated with the number of divided subsequences in the pressure transformation feature sequence.

[0030] In one embodiment, the method for real-time detection of the pressure electrical signal of the electronic pressure controller is:

[0031] The pressure change characteristic sequence is used as the input of the exponential moving average algorithm, and the output is the predicted value of the pressure instability characteristic value at the current moment, which is recorded as the current moment predicted value. The same calculation method as the current moment predicted value is used to obtain the predicted value of the pressure instability characteristic value at the moment before the current moment, which is recorded as the previous moment predicted value. The ratio of the current moment predicted value to the previous moment predicted value is calculated, and the pressure sensor is used to detect the pressure electrical signal of the electronic pressure controller at the current moment. The pressure electrical signal correction value at the current moment is positively correlated with the pressure electrical signal at the current moment and the ratio.

[0032] This application has the following beneficial effects:

[0033] The present application obtains each pressure range floating sequence through the pressure electric signal sequence at the current moment, analyzes the distribution characteristics of the elements in the pressure range floating sequence to obtain the pressure safety assessment factor of each element in the pressure range floating sequence, takes into account the volatility characteristics of all elements in the pressure electric signal sequence, reflects the degree to which the pressure electric signal is disturbed by the external environment, and improves the reliability of measuring the measurement accuracy of the pressure electric signal; further, the pressure instability characteristic value of the pressure range floating sequence is obtained in combination with the pressure safety assessment factor, which reflects the difference between each element in the pressure range floating sequence and the pressure electric signal floating reference value, and reflects the degree to which each element in the pressure range floating sequence fluctuates above and below the pressure electric signal floating reference value; the pressure transformation is constructed using the pressure instability characteristic value Characteristic sequence, obtain the pressure electric signal trend variable of the pressure transformation characteristic sequence, which reflects the trend change of elements in the pressure transformation characteristic sequence and improves the accuracy of analyzing the distribution law of pressure instability characteristic value; combined with the pressure electric signal trend variable, determine the adaptive weight parameter in the exponential moving average algorithm, so that the adaptive weight parameter integrates the distribution characteristics and interference degree of the pressure electric signal, improves the accuracy of the adaptive weight parameter determination, and the close integration with the pressure electric signal environment; finally, use the improved exponential moving average algorithm and the pressure electric signal measurement value at the current moment to perform real-time correction on the pressure electric signal, complete more accurate real-time detection of the pressure electric signal, and improve the accuracy and reliability of the pressure electric signal detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flowchart of the steps of a real-time dynamic control method for a pressure controller provided in this application. DETAILED DESCRIPTION

[0036] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field in this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. It should be understood that, unless otherwise specified in this application, " / " means or. For example, A / B can represent A or B. "And / or" in this application is only a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. "At least one" means one or more. "Multiple" means two or more than two. For example, at least one of a, b or c can represent: a, b, c, a and b, a and c, b and c, a, b and c seven situations.

[0038] An embodiment of the present application provides a real-time dynamic control method for a pressure controller. Specifically, the following real-time dynamic control method for a pressure controller is provided. Figure 1 , the method comprises the following steps:

[0039] Step S001, collecting pressure electrical signal data of the electronic pressure controller and preprocessing the pressure electrical signal data.

[0040] By installing a pressure sensor on the nozzle of the electronic pressure controller, the pressure electrical signals of the electronic pressure controller at each historical moment at the current moment are obtained. As an embodiment of the present application, the time interval for collection is 0.1 seconds, and the total time for collection is 1 minute. The implementer can determine the time interval and the total time for collection according to the actual situation. This embodiment is not limited here. Due to the complexity of the pressure electrical signal acquisition environment, there may be data missing in the acquired pressure electrical signal. Therefore, in this application, the mean interpolation method is used to complete the missing data. Furthermore, the non-local mean denoising algorithm (Non-Local Means, NLM) is used to denoise the completed data. The mean interpolation method and the NLM algorithm are both existing well-known technologies, and the specific process will not be repeated in this application.

[0041] It should be understood that this embodiment only proposes a method for data completion and data denoising, that is, using the mean interpolation method for data completion and using the non-local mean denoising algorithm for data denoising. On the premise that the purpose of data completion and data denoising can be achieved, as other implementation methods, the implementer can also use other feasible data completion and data denoising algorithms in the prior art to pre-process the pressure electrical signal data, and this application does not impose any restrictions on this.

[0042] At this point, the pressure electrical signal sequence at the current moment is obtained by arranging the collected pressure electrical signal data in chronological order.

[0043] Step S002, divide the pressure electrical signal sequence into floating sequences of pressure ranges; obtain the pressure safety assessment factor of each element in the pressure range floating sequence according to the distribution characteristics of the elements in the pressure range floating sequence, and obtain the pressure instability characteristic value of the pressure range floating sequence according to the distribution of the pressure safety assessment factors of all elements in the pressure range floating sequence to form a pressure transformation characteristic sequence.

[0044] Since the composition of mortar is a mixture of cementing material, fine aggregate and water in a certain proportion, where fine aggregate refers to gravel smaller than 2.5 mm, due to the irregularity of gravel, when gravel flows in the pipe, different contact surfaces of gravel rub against the pipe wall, resulting in different losses of mortar kinetic energy, causing the pressure electrical signal detected by the nozzle to fluctuate.

[0045] Therefore, the elements in the pressure electric signal sequence are arranged from small to large, and then all elements between the upper quartile and the lower quartile of the arranged data are obtained and determined as floating data, wherein the upper quartile and the lower quartile are existing public technologies, and the specific calculation process will not be repeated in this application.

[0046] The pressure electric signal sequence is divided into M consecutive data, and each sequence obtained after the division is determined as each pressure range floating sequence. As an embodiment of the present application, M=10, and the implementer can set it according to the actual situation, and the present application does not limit it here. According to the above analysis, the pressure instability characteristic value of each pressure range floating sequence is calculated. First, the mean of the floating data is calculated and determined as the pressure electric signal floating reference value; the standard deviation of all elements in the pressure electric signal sequence is calculated and determined as the pressure electric signal floating coefficient; for each pressure range floating sequence, the absolute value of the difference between each element and the pressure electric signal floating reference value is calculated. When the absolute value of the difference is greater than the pressure electric signal floating coefficient, the absolute value of the difference is used as an exponential function with a natural constant as the base, and the calculation result of the exponential function is determined as the pressure safety assessment factor of the corresponding element in the pressure range floating sequence. When the absolute value of the difference is less than or equal to the pressure electric signal floating coefficient, the preset value 1 is determined as the pressure safety assessment factor of the corresponding element in the pressure range floating sequence. The implementer can determine the value of the preset value according to the actual situation, and the present application does not limit it here.

[0047] Furthermore, after obtaining the pressure safety assessment factor of each element in the pressure range floating sequence, the average of the absolute values ​​of the differences of all elements in the pressure range floating sequence is calculated and determined as the pressure floating mean; the average of the pressure safety assessment factors of all elements in the pressure range floating sequence is calculated and determined as the pressure safety mean; then the pressure instability characteristic value of the pressure range floating sequence is positively correlated with the pressure floating mean and the pressure safety mean.

[0048] It should be understood that the positive correlation relationship may specifically be a multiplication relationship, an addition relationship, or the idempotence of an exponential function, which is determined by actual applications and is not limited in this application.

[0049] In one embodiment of the present application, the pressure instability characteristic value of the pressure range floating sequence is the product of the pressure floating mean value and the pressure safety mean value.

[0050] In another embodiment of the present application, the pressure instability characteristic value of the pressure range floating sequence is the sum of the pressure floating mean value and the pressure safety mean value.

[0051] The larger the gap between the elements in the pressure range floating sequence and the pressure electrical signal floating reference value, the larger the pressure fluctuation or deviation is. The injection system of the electronic pressure controller may have certain instability at the current moment. In this case, the pressure instability characteristic value of the pressure range floating sequence becomes larger.

[0052] Furthermore, the pressure instability characteristic values ​​of all pressure range floating sequences are arranged according to the order of the pressure range floating sequences to obtain a pressure change characteristic sequence.

[0053] Step S003, obtaining the pressure electric signal trend variable of the pressure transformation characteristic sequence according to the trend change of multiple local elements in the pressure transformation characteristic sequence; and determining the adaptive weight parameter in the exponential moving average algorithm in combination with the pressure electric signal trend variable.

[0054] When the electronic pressure controller is blocked, due to the characteristics of mortar non-Newtonian fluid, the flow state in the pipeline may change from partial blockage to complete blockage, which will cause the pressure signal to change accordingly. The pressure signal data of the electronic pressure controller will have a certain trend. When it changes from partial blockage to complete blockage, the measured pressure signal value will tend to decrease. When some obstacles are displaced or fall off, the pressure signal value will tend to increase.

[0055] Therefore, for the pressure change feature sequence, the STL (Seasonal-Trend decomposition procedure based on Loess) sequence decomposition algorithm is used to obtain the residual sequence, trend item sequence and seasonal item sequence of the pressure change feature sequence. Secondly, as an embodiment of the present application, the LOF (Local Outliers Factor) anomaly detection algorithm is used to obtain the mutation points in the pressure change feature sequence, and the pressure change feature sequence is segmented using the mutation points to obtain each subsequence of the pressure change feature sequence. The subsequences of the pressure change feature sequence segmentation are matched to the residual sequence of the pressure change feature sequence to obtain each residual subsequence. Among them, the STL sequence decomposition algorithm is an existing well-known technology, and this application will not be described in detail here.

[0056] It should be noted that this embodiment only proposes a method for mutation point detection, that is, using the LOF anomaly detection algorithm to perform mutation point detection. On the premise that the purpose of mutation point detection can be achieved, as other implementation methods, the implementer can also use other feasible mutation point detection algorithms in the prior art to perform mutation point detection on the pressure change feature sequence, and this application does not impose any restrictions on this.

[0057] Furthermore, the trend changes of each subsequence of the pressure transformation characteristic sequence are analyzed to obtain the residual trend influence coefficient of each subsequence of the pressure transformation characteristic sequence, specifically: the sum and mean of the residual sequence of the pressure transformation characteristic sequence are calculated to determine them as the first residual sum and the first residual mean, the sum and mean of the residual subsequences corresponding to each subsequence of the pressure transformation characteristic sequence are calculated to determine them as the second residual sum and the second residual mean, the absolute value of the difference between the first residual mean and the second residual mean is calculated, recorded as the first difference absolute value, the product of the first difference absolute value and the second residual sum is determined as the first residual product, and the residual trend influence coefficient of each subsequence of the pressure transformation characteristic sequence is positively correlated with the first residual sum, and negatively correlated with the first residual product.

[0058] It can be understood that a positive correlation means that the dependent variable will increase as the independent variable increases, and the dependent variable will decrease as the independent variable decreases. The specific relationship can be a multiplication relationship, an addition relationship, or the power of an exponential function, which is determined by actual application and this application does not impose any special restrictions. A negative correlation means that the dependent variable will decrease as the independent variable increases, and the dependent variable will increase as the independent variable decreases. It can be a subtraction relationship, a division relationship, etc., which is determined by actual application.

[0059] In one embodiment of the present application, the residual trend influence coefficient of each subsequence of the pressure transformation feature sequence is a ratio of the first residual sum to the product of the first residual.

[0060] In other embodiments of the present application, the residual trend influence coefficient of each subsequence of the pressure transformation feature sequence is the difference between the first residual sum and the product of the first residual.

[0061] Preferably, the pressure electrical signal trend variable of the pressure conversion feature sequence is determined in combination with the residual trend influence coefficient of each subsequence of the pressure conversion feature sequence, specifically: for the pressure conversion feature sequence, the cumulative sum of the residual trend influence coefficients of all subsequences is calculated, and the ratio of the residual trend influence coefficient of each subsequence to the cumulative sum is used as the first pressure trend ratio, the difference between the number of subsequences contained in the pressure conversion feature sequence and the sequence number of each subsequence is calculated, and the ratio of the number of elements contained in each subsequence to the difference is used as the second pressure trend ratio, and the product of the first pressure trend ratio and the second pressure trend ratio is used as the first trend product. The pressure electrical signal trend variable of the pressure conversion feature sequence is the sum of the first trend products of all subsequences.

[0062] The weight parameters in the EMS exponential moving average algorithm are improved by using the pressure electric signal trend variables of the pressure transformation feature sequence, and the adaptive weight parameters of the exponential moving average algorithm are obtained. Specifically, the adaptive weight parameters are positively correlated with the pressure electric signal trend variables of the pressure transformation feature sequence, and negatively correlated with the number of subsequences divided in the pressure transformation feature sequence.

[0063] As an embodiment of the present application, the process of determining the adaptive weight parameter may be: calculating the ratio of the pressure electrical signal trend variable of the pressure transformation feature sequence to the number of divided subsequences in the pressure transformation feature sequence, determining it as a local trend difference, taking the local trend difference as the exponent of an exponential function with a natural constant as the base, and taking the normalized value of the calculation result of the exponential function as the adaptive weight parameter of the pressure transformation feature sequence.

[0064] If the pressure electrical signal trend variable of the pressure conversion feature sequence is larger, it means that the pressure instability characteristic value has a larger changing trend, and the predicted value of the pressure instability characteristic value is greatly affected by the average value of the pressure conversion feature sequence, and a larger adaptive weight parameter is required; the smaller the number of subsequences divided in the pressure conversion feature sequence, the fewer the number of mutation points in the pressure conversion feature sequence, the more normal the pressure instability characteristic value in the pressure conversion feature sequence is, and the pressure instability characteristic value in the pressure conversion feature sequence has a higher credibility for the prediction of the pressure instability characteristic value, so the larger the adaptive weight parameter is.

[0065] The adaptive weight parameter is used as the weight parameter in the EMS exponential moving average algorithm to improve the EMS exponential moving average algorithm.

[0066] Step S004, using an exponential moving average algorithm to complete real-time detection of the pressure electrical signal of the electronic pressure controller.

[0067] The pressure change characteristic sequence is used as the input of the improved EMS exponential moving average algorithm, and the output is the predicted value of the pressure instability characteristic value at the current moment, which is recorded as the predicted value at the current moment; wherein, the EMS exponential moving average algorithm is an existing well-known technology and will not be described in detail in this application.

[0068] Furthermore, the same calculation method as the predicted value of the pressure instability characteristic value at the current moment is adopted to obtain the predicted value of the pressure instability characteristic value at the previous moment, recorded as the predicted value at the previous moment, calculate the ratio of the predicted value at the current moment to the predicted value at the previous moment, determine it as the pressure electric signal adjustment coefficient, obtain the pressure electric signal of the electronic pressure controller detected by the pressure sensor at the current moment, recorded as the measured pressure at the current moment, and the pressure electric signal correction value at the current moment is positively correlated with the measured pressure at the current moment and the pressure electric signal adjustment coefficient.

[0069] As an embodiment of the present application, the calculation method of the pressure electrical signal correction value of the electronic pressure controller at the current moment can be: the product of the pressure electrical signal adjustment coefficient and the pressure measured at the current moment is used as the pressure electrical signal correction value of the electronic pressure controller at the current moment.

[0070] At this point, the present application corrects the pressure electrical signal measurement value of the electronic pressure controller at the current moment, and completes more accurate pressure electrical signal detection of the electronic pressure controller.

[0071] In summary, an embodiment of the present application provides a real-time dynamic control method for a pressure controller, including: obtaining each pressure range floating sequence through the pressure electric signal sequence at the current moment, analyzing the element distribution characteristics in the pressure range floating sequence to obtain the pressure safety assessment factor of each element in the pressure range floating sequence, taking into account the volatility characteristics of all elements in the pressure electric signal sequence, reflecting the degree to which the pressure electric signal is disturbed by the external environment, and improving the reliability of measuring the measurement accuracy of the pressure electric signal; further, combining the pressure safety assessment factor to obtain the pressure instability characteristic value of the pressure range floating sequence, reflecting the difference between each element in the pressure range floating sequence and the pressure electric signal floating reference value, and reflecting the degree to which each element in the pressure range floating sequence floats above and below the pressure electric signal floating reference value. ; The pressure transformation feature sequence is constructed using the pressure instability eigenvalue, and the pressure electrical signal trend variable of the pressure transformation feature sequence is obtained, which reflects the trend change of the elements in the pressure transformation feature sequence and improves the accuracy of analyzing the distribution law of the pressure instability eigenvalue; the adaptive weight parameters in the exponential moving average algorithm are determined in combination with the pressure electrical signal trend variable, so that the adaptive weight parameters integrate the distribution characteristics and interference degree of the pressure electrical signal, improve the accuracy of the adaptive weight parameter determination, and the close integration with the pressure electrical signal environment; finally, the improved exponential moving average algorithm and the pressure electrical signal measurement value at the current moment are used to perform real-time correction on the pressure electrical signal, complete more accurate real-time detection of the pressure electrical signal, and improve the accuracy and reliability of the pressure electrical signal detection.

[0072] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0073] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or essential features of the present application.

Claims

1. A real-time dynamic control method for a pressure controller, characterized in that: The method comprises the following steps: Collect the pressure electrical signals of the electronic pressure controller at a preset number of times before the current time to form a pressure electrical signal sequence at the current time; The pressure electric signal sequence is evenly divided into floating sequences of each pressure range; the pressure safety assessment factor of each element in the floating sequence of the pressure range is obtained according to the distribution characteristics of the elements in the floating sequence of the pressure range; Analyze the distribution of pressure safety assessment factors of all elements in the pressure range floating sequence to obtain the pressure instability characteristic value of the pressure range floating sequence; combine the pressure instability characteristic values ​​of all pressure range floating sequences into a pressure transformation characteristic sequence; Analyze the trend changes of multiple local elements in the pressure conversion feature sequence to obtain the pressure electric signal trend variable of the pressure conversion feature sequence; determine the adaptive weight parameter in the exponential moving average algorithm based on the pressure electric signal trend variable to perform real-time detection of the pressure electric signal of the electronic pressure controller; Acquire floating data in the pressure electric signal sequence based on the element distribution characteristics in the pressure electric signal sequence; Determine the pressure electric signal floating coefficient of the pressure electric signal sequence based on the discrete degree of the elements in the pressure electric signal sequence; The mean of the floating data is used as the floating reference value of the pressure electric signal. For each pressure range floating sequence, the absolute value of the difference between each element and the pressure electric signal floating reference value is calculated. If the absolute value of the difference is greater than the pressure electric signal floating coefficient, the pressure safety assessment factor of the corresponding element in the pressure range floating sequence is positively correlated with the absolute value of the difference. Otherwise, the pressure safety assessment factor of the corresponding element in the pressure range floating sequence is a preset value.

2. A real-time dynamic control method for a pressure controller according to claim 1, characterized in that: The acquisition process of the floating data is as follows: Arrange all elements in the pressure electric signal sequence in ascending order, and take all elements between the upper quartile and the lower quartile of the elements after ascending order as floating data.

3. A real-time dynamic control method for a pressure controller according to claim 1, characterized in that: The pressure electric signal floating coefficient is the standard deviation of all elements in the pressure electric signal sequence.

4. A real-time dynamic control method for a pressure controller according to claim 1, characterized in that: The process of determining the pressure instability characteristic value is as follows: The mean of the absolute values ​​of the differences of all elements in the pressure range floating sequence is calculated and determined as the pressure floating mean; the mean of the pressure safety assessment factors of all elements in the pressure range floating sequence is calculated and determined as the pressure safety mean; the pressure instability characteristic value of the pressure range floating sequence is positively correlated with the pressure floating mean and the pressure safety mean.

5. A real-time dynamic control method for a pressure controller according to claim 1, characterized in that: The method for determining the pressure electric signal trend variable is: Decomposing the pressure transformation characteristic sequence to obtain a residual sequence, and obtaining each subsequence of the pressure transformation characteristic sequence and each residual subsequence of the residual sequence based on the distribution of abnormal elements in the pressure transformation characteristic sequence; Determine the residual trend influence coefficient of each subsequence of the pressure transformation characteristic sequence based on the distribution of elements in the residual sequence and each residual subsequence; For the pressure conversion characteristic sequence, the cumulative sum of the residual trend influence coefficients of all subsequences is calculated, and the ratio of the residual trend influence coefficient of each subsequence to the cumulative sum is used as the first pressure trend ratio. The difference between the number of subsequences contained in the pressure conversion characteristic sequence and the sequence number of each subsequence is calculated, and the ratio of the number of elements contained in each subsequence to the difference is used as the second pressure trend ratio. The product of the first pressure trend ratio and the second pressure trend ratio is used as the first trend product. The pressure electrical signal trend variable of the pressure conversion characteristic sequence is the sum of the first trend products of all subsequences.

6. A real-time dynamic control method for a pressure controller as claimed in claim 5, characterized in that: The method for determining each subsequence of the pressure transformation characteristic sequence and each residual subsequence of the residual sequence is as follows: An anomaly detection algorithm is used to obtain each mutation point in the pressure transformation feature sequence, and each mutation point is used as a segmentation point of the pressure transformation feature sequence to obtain each subsequence of the pressure transformation feature sequence, and the corresponding point of the segmentation point in the residual sequence is obtained. The residual sequence is segmented using the corresponding points to obtain each residual subsequence of the residual sequence.

7. A real-time dynamic control method for a pressure controller as claimed in claim 5, characterized in that: The determination process of the residual trend influence coefficient is: The sum and mean of the residual sequence of the pressure transformation characteristic sequence are calculated to determine them as the first residual sum and the first residual mean. The sum and mean of the residual subsequences corresponding to each subsequence of the pressure transformation characteristic sequence are calculated to determine them as the second residual sum and the second residual mean. The absolute value of the difference between the first residual mean and the second residual mean is calculated, and the product of the absolute value of the difference and the second residual sum is determined as the first residual product. The residual trend influence coefficient of each subsequence of the pressure transformation characteristic sequence is positively correlated with the first residual sum, and negatively correlated with the first residual product.

8. A real-time dynamic control method for a pressure controller according to claim 1, characterized in that: The adaptive weight parameter in the exponential moving average algorithm is specifically determined as follows: The adaptive weight parameter in the exponential moving average algorithm is positively correlated with the pressure electric signal trend variable of the pressure transformation feature sequence, and negatively correlated with the number of divided subsequences in the pressure transformation feature sequence.

9. A real-time dynamic control method for a pressure controller as claimed in claim 8, characterized in that: The method for real-time detection of the pressure electrical signal of the electronic pressure controller is: The pressure change characteristic sequence is used as the input of the exponential moving average algorithm, and the output is the predicted value of the pressure instability characteristic value at the current moment, which is recorded as the current moment predicted value. The same calculation method as the current moment predicted value is used to obtain the predicted value of the pressure instability characteristic value at the moment before the current moment, which is recorded as the previous moment predicted value. The ratio of the current moment predicted value to the previous moment predicted value is calculated, and the pressure sensor is used to detect the pressure electrical signal of the electronic pressure controller at the current moment. The pressure electrical signal correction value at the current moment is positively correlated with the pressure electrical signal at the current moment and the ratio.

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