Storage tank breather valve pressure detection method, system and device

Abstract

The invention relates to a pressure detection method, system and device for a breather valve of a storage tank. The method comprises the steps that key parameters used for representing the current state in the storage tank are obtained, and the key parameters are stored in a preset data record table to obtain new historical key data records; at least part of the historical key data records are input into a preset breather valve performance evaluation relation, and the abnormal degree used for representing the breather valve performance is obtained through calculation; and judging whether the abnormal degree exceeds a preset normal range, and if yes, outputting an early warning signal. By the adoption of the method, the historical key data records stored in the preset data record sheet are substituted into the preset breather valve performance evaluation relation to calculate the parameter result, and the parameter describes the current working state of the breather valve in a numerical value mode; and the abnormal degree of the breather valve is monitored by setting a warning threshold value about the abnormal degree, so that the function of monitoring whether the breather valve works normally or not in real time is realized.

Description

Technical Field

[0001] The present application relates to the field of process control technology, and in particular to a method, system and device for detecting the pressure of a storage tank breathing valve. Background Art

[0002] With the vigorous development of my country's petrochemical industry, the number of storage tanks is also increasing day by day. As a commonly used equipment in storage tanks and process industries, whether the breathing valve can work normally is related to the safety of the storage tanks and the petrochemical raw materials or products stored in the tanks. Therefore, how to ensure the normal operation of the breathing valve has become a highly concerned issue in the industry.

[0003] The existing Chinese invention patent with publication number CN116429409A provides a full parameter detection system and method for the factory test of a breathing valve, including a control and interaction module, a gas medium generation module, an opening pressure and leakage detection module, a ventilation detection module, and a valve body water pressure test module; the control and interaction module is connected to the gas medium generation module, the opening pressure and leakage detection module, the ventilation detection module, and the valve body water pressure test module through a control line; the control and interaction module is used to control the working state of the gas medium generation module, the opening pressure and leakage detection module, the ventilation detection module, and the valve body water pressure test module, and collect information from each module. It has a high degree of integration and comprehensive testing functions. It can realize the opening pressure, leakage, ventilation detection of the breathing valve and the water pressure test of the breathing valve body at one time, and it is easy to operate, which greatly reduces the labor intensity of the operator.

[0004] However, this method can still only perform quality inspection on the breathing valves leaving the factory, but cannot realize status monitoring of the breathing valves in actual working conditions. Summary of the invention

[0005] Based on this, it is necessary to provide a tank breathing valve pressure detection method, device, computer equipment, computer readable storage medium and computer program product that can monitor the working status of the breathing valve in real time to address the above technical problems.

[0006] In a first aspect, the present application provides a method for detecting the pressure of a storage tank breathing valve, the method comprising:

[0007] Obtain key parameters for characterizing the current state in the storage tank, and store the key parameters in a preset data record table to obtain new historical key data records;

[0008] Inputting at least a portion of the historical key data records into a preset breathing valve performance evaluation relationship to calculate an abnormality degree for characterizing the breathing valve performance;

[0009] Determine whether the degree of abnormality exceeds the preset normal range. If so, output a warning signal.

[0010] In one embodiment, the key parameters include real-time temperature, real-time pressure, and real-time liquid level, and the specific steps of storing the key parameters in a preset data record table to obtain new historical key data records also include:

[0011] The real-time temperature, real-time pressure, real-time liquid level and their corresponding sampling timestamp information are stored together in a preset data record table to obtain historical key data records including real-time temperature, real-time pressure, real-time liquid level and sampling timestamp information. The historical key data records are data used to represent the changing trends of parameters such as temperature, pressure and liquid level in the storage tank. The historical key data records contain at least pressure change data, temperature change data, liquid level change data and timestamp data.

[0012] In one embodiment, the key parameters also include the opening and closing state of the breathing valve and the opening and closing time when the opening and closing state changes. The specific steps of inputting at least part of the historical key data records into the preset breathing valve performance evaluation relationship and calculating the local characteristic value used to characterize the performance of the breathing valve include:

[0013] According to the opening and closing states and the corresponding opening and closing times, at least part of the timestamp data is divided into a time period of rapid pressure change and a time period of slow pressure change;

[0014] Obtaining the pressure slow change time period closest to the current time among the plurality of pressure slow change data segments as the most recent pressure slow change time period;

[0015] Substituting the pressure change data and the liquid level change data corresponding to at least part of the timestamp data contained in the most recent slow pressure change period into the preset breathing valve performance evaluation relationship to calculate the local eigenvalue corresponding to each timestamp data and the credibility of the local eigenvalue;

[0016] Calculate the normality of the breathing valve according to the local features and their credibility;

[0017] Extract the critical pressure moment when the critical pressure point appears in the rapid pressure change period according to the pressure change data, and substitute the pressure change data corresponding to the critical pressure moment into the preset breathing valve performance evaluation relationship to obtain the critical point abnormality degree of the breathing valve;

[0018] The normal degree and the critical point abnormal degree are substituted into the breathing valve performance evaluation relationship to calculate the abnormal degree used to characterize the breathing valve performance.

[0019] In one embodiment, the specific steps of dividing at least part of the pressure change data into a pressure fast change data segment and a pressure slow change data segment according to the opening and closing states and their corresponding opening and closing times include:

[0020] According to the opening and closing status, the corresponding opening and closing time is divided into opening time and closing time;

[0021] A plurality of consecutive timestamp information between an opening time and an adjacent next closing time is regarded as a time period of rapid pressure change;

[0022] A plurality of continuous time stamp information between a closing time and the next adjacent opening time is regarded as a time period of slow pressure change.

[0023] In one embodiment, the specific steps of substituting the pressure change data and the liquid level change data corresponding to at least part of the timestamp data contained in the most recent slow pressure change time period into the preset breathing valve performance evaluation relationship to calculate the local characteristic value corresponding to each timestamp data and the credibility of the local characteristic value include:

[0024] Obtain at least part of the timestamp data contained in the most recent slow pressure change time period and its corresponding pressure change data and liquid level height change data;

[0025] Calculate the pressure change Δp between each acquired pressure change data and the corresponding pressure change data at the previous moment;

[0026] Calculate the liquid level change amount Δh between each acquired liquid level change data and the corresponding liquid level change data at the previous moment;

[0027] Obtain the local characteristic moment corresponding to each timestamp data according to the liquid level height change Δh;

[0028] The pressure change Δp and height change Δh corresponding to the local characteristic moment of each timestamp data are calculated by the least squares method to obtain the pressure-liquid level relationship:

[0029] Δp(t)=k×Δh(t)+b

[0030] Among them, k is the slope of the straight line obtained by least squares fitting, which is also the local eigenvalue of the timestamp data;

[0031] At least part of the temperature change data is acquired, and the at least part of the temperature change data, the local eigenvalue k and the pressure change data are substituted into a preset credibility calculation relationship to calculate the credibility l corresponding to the local eigenvalue.

[0032] In one embodiment, the specific steps of calculating the normality of the breathing valve according to the local features and their credibility include:

[0033] Extract several historical reference moments from historical key data records, and calculate the local feature values ​​and credibility corresponding to the historical reference moments;

[0034] Substituting the historical characteristic value and historical credibility corresponding to the historical reference moment and the local characteristic value and credibility into the preset matching degree calculation relationship to obtain the matching degree corresponding to the local characteristic value and credibility;

[0035] Substitute the calculated degree of conformity into the preset normal degree calculation relationship to obtain the normal degree.

[0036] In one embodiment, the specific steps of extracting the critical pressure moment at which the critical pressure point appears in the rapid pressure change period according to the pressure change data, and substituting the pressure change data corresponding to the critical pressure moment into the preset breathing valve performance evaluation relationship to obtain the critical point abnormality degree of the breathing valve include:

[0037] Obtain the critical pressure point in the pressure rapid change data segment, and use the timestamp data of the critical pressure point as the critical pressure moment;

[0038] According to the value of the pressure critical point, the pressure critical point is divided into an upper pressure critical point and a lower pressure critical point;

[0039] Calculate the mean and standard deviation of the upper pressure critical point and the lower pressure critical point respectively, and calculate the upper pressure threshold and the lower pressure threshold according to the mean and the standard deviation;

[0040] The proportion of all upper pressure critical points that are greater than the upper pressure threshold is calculated as the first abnormality degree, and the proportion of all lower pressure critical points that are less than the lower pressure threshold is calculated as the second abnormality degree.

[0041] In one embodiment, the specific steps of substituting the normal degree and the critical point abnormal degree into the breathing valve performance evaluation relationship to calculate the abnormal degree for characterizing the breathing valve performance include:

[0042] Substitute the normal degree, the first abnormal degree and the second abnormal degree into the following relationship to calculate the abnormal degree representing the performance of the breathing valve:

[0043]

[0044] Among them, D is the normal level, C 1 is the first abnormality degree, C 2 is the second abnormality degree, norm is the normalization calculation function, and G is the abnormality degree used to characterize the performance of the breathing valve.

[0045] In a second aspect, the present application also provides a tank breathing valve pressure detection system, the system comprising:

[0046] A key parameter acquisition module is provided in the storage tank and is used to acquire key parameters related to the storage tank and the breathing valve;

[0047] A data storage module, used to store preset data record tables and store key parameters to update historical key data records;

[0048] A data processing module, used for storing and running a preset breathing valve performance evaluation relationship to output an abnormality degree for characterizing the breathing valve performance;

[0049] The control module is connected to the data processing module to obtain the degree of abnormality, and is used to determine whether the degree of abnormality exceeds a preset normal range, and output a warning signal after the degree of abnormality exceeds the preset normal range.

[0050] In a third aspect, the present application also provides a tank breathing valve pressure detection device, the device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the following steps when executing the computer program:

[0051] Obtain key parameters for characterizing the current state in the storage tank, and store the key parameters in a preset data record table to obtain new historical key data records;

[0052] Inputting at least a portion of the historical key data records into a preset breathing valve performance evaluation relationship to calculate an abnormality degree for characterizing the breathing valve performance;

[0053] Determine whether the degree of abnormality exceeds the preset normal range. If so, output a warning signal.

[0054] The above-mentioned tank breathing valve pressure detection method, device, computer equipment, storage medium and computer program product, by substituting the historical key data records stored in the preset data record table into the preset breathing valve performance evaluation relationship to calculate the parameter result, the parameter describes the current working state of the breathing valve in a numerical way, and then monitors the abnormality degree of the breathing valve by setting the warning threshold of the abnormality degree, thereby realizing the function of real-time monitoring of whether the breathing valve is working normally. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is an application environment diagram of a method for detecting the pressure of a storage tank breathing valve in one embodiment;

[0056] Figure 2 A schematic diagram of a flow chart of a method for detecting the pressure of a storage tank breathing valve in one embodiment;

[0057] Figure 3 A system structure topology diagram of a storage tank breathing valve pressure detection system in one embodiment;

[0058] Figure 4 The figure is a diagram of the internal structure of a storage tank breathing valve pressure detection device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The tank breathing valve pressure detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various sensors or transmitters such as pressure sensors, temperature sensors, and liquid level sensors for detecting the state of the liquid in the storage tank, and the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0061] In one embodiment, Figure 2 As shown, a method for detecting the pressure of a storage tank breathing valve is provided, and the method is applied to Figure 1 The server in the example is used to illustrate the following steps:

[0062] Step S100: Acquire key parameters for characterizing the current state in the storage tank, and store the key parameters in a preset data record table to obtain new historical key data records.

[0063] Among them, the key parameters include real-time temperature, real-time pressure and real-time liquid level. In addition, the key parameters also include the opening and closing state of the breathing valve and the opening and closing time when the opening and closing state changes. The specific steps of step S100 are as follows:

[0064] The real-time temperature, real-time pressure, real-time liquid level and their corresponding sampling timestamp information are stored together in a preset data recording table to obtain historical key data records including real-time temperature, real-time pressure, real-time liquid level and sampling timestamp information. The historical key data records are data used to represent the changing trends of parameters such as temperature, pressure and liquid level in the storage tank. The historical key data records at least include pressure change data, temperature change data, liquid level change data and timestamp data. When the real-time temperature, real-time pressure and / or real-time liquid level in the storage tank exceed the preset range, the breathing valve or air supply valve connected to the storage tank will switch the open and closed state to make the temperature, pressure and / or liquid level in the storage tank return to the normal range. When the breathing valve or air supply valve changes the open and closed state, the switched state and the switching time will also be stored as key data in the preset data recording table.

[0065] Step S200: inputting at least part of the historical key data records into a preset breathing valve performance evaluation relationship to calculate the degree of abnormality used to characterize the breathing valve performance.

[0066] Among them, the degree of abnormality is the parameter result calculated by substituting the historical key data records stored in the preset data record table into the preset breathing valve performance evaluation relationship. The parameter describes the current working state of the breathing valve in a numerical way, and then monitors the degree of abnormality of the breathing valve by setting the warning threshold of the degree of abnormality, thereby realizing the function of real-time monitoring of whether the breathing valve is working normally. Moreover, this method does not need to improve the hardware structure of the breathing valve, and can directly complete the indirect evaluation of the working state of the breathing valve by calculating the key parameters collected by various sensors installed in the tank body, thereby effectively reducing the cost of use.

[0067] The specific steps of step S200 include:

[0068] Step S210: According to the opening and closing states and their corresponding opening and closing times, at least part of the time stamp data is divided into a time period of rapid pressure change and a time period of slow pressure change. Specifically, step S210 also includes:

[0069] Step S211: dividing the corresponding opening and closing time into opening time and closing time according to the opening and closing state.

[0070] Step S212: taking a plurality of consecutive timestamp information between an opening time and an adjacent next closing time as a time period of rapid pressure change.

[0071] Step S213: taking a plurality of consecutive timestamp information between a closing time and an adjacent next opening time as a time period of slow pressure change.

[0072] Through steps S211 to S213, the timestamp data in the historical key data records are divided into several time periods with the opening time and closing time of the pressure relief valve and the air supply valve in the historical key data records as nodes, and the pressure change data, temperature change data and liquid level change data corresponding to the timestamp data contained in the time period are also divided into several data segments. The time period starting with the opening time of the pressure relief valve or the air supply valve in the divided time period is taken as the rapid pressure change time period, and its corresponding pressure change data, temperature change data and liquid level change data are recorded as the rapid pressure change data segment; the time period starting with the closing time of the pressure relief valve or the air supply valve in the divided time period is taken as the slow pressure change time period, and its corresponding pressure change data, temperature change data and liquid level change data are recorded as the slow pressure change data segment.

[0073] Step S220: obtaining a pressure slow change time period closest to the current time among a plurality of pressure slow change data segments as the most recent pressure slow change time period.

[0074] Among them, the current time is the timestamp data of the most recent acquisition of key data; if the current time is in a fast-changing time period, that is, the pressure relief valve or the air supply valve is in an open state at this time, then the most recent slow pressure change time period at this time is the slow pressure change time period after the previous valve was closed; if the current time is in a slow pressure change time period, then the most recent slow pressure change time at this time is the slow pressure change time period of the current time.

[0075] Step S230: Substitute the pressure change data and liquid level change data corresponding to at least part of the timestamp data contained in the most recent slow pressure change time period into the preset breathing valve performance evaluation relationship to calculate the local eigenvalue corresponding to each timestamp data and the credibility of the local eigenvalue.

[0076] The preset breathing valve performance evaluation relationship includes a preset credibility calculation relationship. Specifically, step S230 further includes the following steps:

[0077] Step S231: Obtain at least part of the timestamp data contained in the most recent slow pressure change time period and its corresponding pressure change data and liquid level height change data.

[0078] Step S232: Calculate the pressure change AP between each acquired pressure change data and its corresponding pressure change data at the previous moment.

[0079] Step S233: Calculate the liquid level change amount Δh between each acquired liquid level change data and the corresponding liquid level change data at the previous moment.

[0080] Step S234: Obtain the local characteristic moment corresponding to each timestamp data according to the liquid level change Δh.

[0081] In this embodiment, the specific method for obtaining the local characteristic time is as follows: extract the timestamp data in the slowly changing time period in sequence as the current moment, the time interval between each timestamp data is the unit time, and obtain the number of liquid level height changes in the 5 unit times before the current moment that are consistent in direction (that is, Δh is positive or negative). If the direction of the liquid level height change in the 5 unit times is consistent (that is, 5 consecutive Δh are all positive or negative), the current moment is taken as the local characteristic moment. If they are inconsistent, the moments corresponding to several changes in height that are consistent with the moment are taken as the local characteristic moments of the moment, and the number of local characteristic moments is counted.

[0082] Step S235: Calculate the pressure change Δp and height change Δh corresponding to the local characteristic moment of each timestamp data by the least square method to obtain the pressure-liquid level relationship:

[0083] Δp(t)=k×Δh(t)+b

[0084] Among them, k is the slope of the straight line obtained by least squares fitting, which is also the local eigenvalue of the timestamp data;

[0085] At least part of the temperature change data is acquired, and the at least part of the temperature change data, the local eigenvalue k and the pressure change data are substituted into a preset credibility calculation relationship to calculate the credibility l corresponding to the local eigenvalue.

[0086] Step S236: Acquire at least part of the temperature change data, and substitute at least part of the temperature change data, the local eigenvalue k, and the pressure change data into a preset credibility calculation relationship to calculate the credibility l corresponding to the local eigenvalue.

[0087] Specifically, the preset credibility calculation relationship is as follows:

[0088]

[0089] Among them, l is the credibility of the characteristic value, n is the serial number of the timestamp data contained in the time period of slow pressure change, that is, the serial number of the current time period in the time period of slow pressure change, Δp 实，n is the actual pressure change at the nth moment, Δp 预测，n is the pressure value corresponding to the fitting line at the nth moment. i is the number of the i-th local feature moment at the current moment, and i is the sequence number of the local feature moment at the current moment. The more the number of local feature moments, the greater the credibility of the feature value. i is the temperature value of the i-th local characteristic moment at the current moment, I 0 To calculate the temperature value at the current moment, norm() is the normalization calculation function.

[0090] Step S240: Calculate the normality of the breathing valve according to the local features and their credibility. The preset breathing valve performance evaluation relationship also includes a preset matching degree calculation relationship. The specific steps of step S240 also include:

[0091] Step S241: extracting a number of historical reference moments from the historical key data records, and calculating the local feature values ​​and credibility corresponding to the historical reference moments.

[0092] In this step, the slowly changing pressure data segment can be divided into two categories according to the valve opened in the previous quickly changing pressure data segment, including the upper critical point of pressure after the air relief valve is opened and the lower critical point of pressure after the air supply valve is opened; the specific steps of step S241 include obtaining the type of valve opened once at the current moment, obtaining several moments with the same liquid level as the current moment in several slowly changing pressure data segments in the historical data that are consistent with the type of valve opened before the current moment, and recording them as several historical reference moments of the current moment. The local eigenvalues ​​and credibility corresponding to the historical reference moments in this step can be recalculated or saved when the local eigenvalues ​​and credibility are calculated before, and directly read in this step.

[0093] Step S242: Substitute the historical feature value and historical credibility corresponding to the historical reference moment and the local feature value and credibility into the preset matching degree calculation relationship to obtain the matching degree corresponding to the local feature value and credibility.

[0094] In this step, the preset matching degree calculation relationship is as follows:

[0095]

[0096] Where M is the number of historical reference moments at the current moment, k j is the local eigenvalue at the jth moment in the reference data segment at the current moment, k j，m is the local eigenvalue of the i-th moment in the reference data segment of the m-th historical reference moment at the current moment, l j，m is the credibility of the local feature value at the i-th moment in the reference data segment of the m-th historical reference moment at the current moment.

[0097] Step S243: Substitute the calculated degree of conformity into the preset normal degree calculation relationship to obtain the normal degree. In the embodiment of the present application, only the 30 timestamp data closest to the current time in the recent slow pressure change period and their corresponding pressure, temperature, and liquid level height data are extracted for calculation, and the same is true when processing the historical reference data segment.

[0098]

[0099] Where D is the normality, 30 is the number of moments in the reference data segment at the current moment, and q is the sequence number of the timestamp data corresponding to each moment in the reference data segment. Indicates the historical consistency of the local eigenvalue at each moment S q Adjust by time. The closer it is to the current moment, the higher the historical consistency of that moment. q The more credible.

[0100] Step S250: extracting the critical pressure moment at which the critical pressure point appears in the rapid pressure change period according to the pressure change data, and substituting the pressure change data corresponding to the critical pressure moment into the preset breathing valve performance evaluation relationship to obtain the critical point abnormality degree of the breathing valve.

[0101] Specifically, the specific steps of step S250 are as follows: obtain the pressure critical point appearing in the pressure rapid change data segment, and use the timestamp data of the pressure critical point as the pressure critical moment; divide the pressure critical point into an upper pressure critical point and a lower pressure critical point according to the value of the pressure critical point; calculate the mean and standard deviation of the upper pressure critical point and the lower pressure critical point respectively, and calculate the upper pressure threshold and the lower pressure threshold according to the mean and the standard deviation; calculate the proportion of all upper pressure critical points that are greater than the upper pressure threshold as the first abnormality degree, and calculate the proportion of all lower pressure critical points that are less than the lower pressure threshold as the second abnormality degree.

[0102] Step S260: Substitute the normal degree and the critical point abnormal degree into the breathing valve performance evaluation relationship to calculate the abnormal degree used to characterize the breathing valve performance.

[0103] The preset breathing valve performance evaluation relationship also includes an abnormality calculation relationship. The specific method of step S260 is as follows: Substitute the normal degree, the first abnormal degree, and the second abnormal degree into the following abnormality calculation relationship to calculate the abnormality that characterizes the breathing valve performance:

[0104]

[0105] Among them, D is the normal level, C 1 is the first abnormality degree, C 2 is the second abnormality degree, norm is the normalization calculation function, and G is the abnormality degree used to characterize the performance of the breathing valve.

[0106] Step S300: Determine whether the abnormality level exceeds a preset normal range, and if so, output a warning signal.

[0107] In the embodiment of the present application, the preset normal range is G≤0.9. If the abnormality degree of the breathing valve exceeds 0.9, it is determined that the breathing valve may be abnormal, and leakage or inability to exhaust may have occurred. The system outputs a warning signal to prompt the staff to perform further inspection and replacement to keep the air pressure in the tank within a safe range.

[0108] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0109] Based on the same inventive concept, the embodiment of the present application also provides a storage tank breathing valve pressure detection device for implementing the above-mentioned storage tank breathing valve pressure detection method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more storage tank breathing valve pressure detection device embodiments provided below can refer to the limitations of the storage tank breathing valve pressure detection method above, and will not be repeated here.

[0110] In one embodiment, Figure 3 As shown, a tank breathing valve pressure detection system is provided, including: a key parameter acquisition module, a data storage module, a data processing module and a control module, wherein:

[0111] A key parameter acquisition module is provided in the storage tank and is used to acquire key parameters related to the storage tank and the breathing valve;

[0112] A data storage module, used to store preset data record tables and store key parameters to update historical key data records;

[0113] A data processing module, used for storing and running a preset breathing valve performance evaluation relationship to output an abnormality degree for characterizing the breathing valve performance;

[0114] The control module is connected to the data processing module to obtain the degree of abnormality, and is used to determine whether the degree of abnormality exceeds a preset normal range, and output a warning signal after the degree of abnormality exceeds the preset normal range.

[0115] Each module in the above-mentioned tank breathing valve pressure detection device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of each of the above modules.

[0116] In one embodiment, a storage tank breathing valve pressure detection device is provided, and its internal structure diagram can be shown as follows: Figure 4 As shown. The device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for detecting the pressure of a tank breathing valve is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0117] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0118] In one embodiment, a storage tank breathing valve pressure detection device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program:

[0119] Obtain key parameters for characterizing the current state in the storage tank, and store the key parameters in a preset data record table to obtain new historical key data records;

[0120] Inputting at least a portion of the historical key data records into a preset breathing valve performance evaluation relationship to calculate an abnormality degree for characterizing the breathing valve performance;

[0121] Determine whether the degree of abnormality exceeds the preset normal range. If so, output a warning signal.

[0122] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0123] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for detecting the pressure of a storage tank breathing valve, characterized in that: The method comprises: Obtain key parameters for characterizing the current state in the storage tank, and store the key parameters in a preset data record table to obtain new historical key data records; Inputting at least part of the historical key data records into a preset breathing valve performance evaluation relationship to calculate an abnormality degree for characterizing the breathing valve performance; Determine whether the degree of abnormality exceeds the preset normal range. If so, output a warning signal.

2. The method for detecting the pressure of a storage tank breathing valve according to claim 1, characterized in that: The key parameters include real-time temperature, real-time pressure and real-time liquid level. The specific steps of storing the key parameters in a preset data record table to obtain new historical key data records also include: The real-time temperature, real-time pressure, real-time liquid level and their corresponding sampling timestamp information are stored together in the preset data recording table to obtain historical key data records including data such as real-time temperature, real-time pressure, real-time liquid level and sampling timestamp information. The historical key data records are data used to represent the changing trends of parameters such as temperature, pressure, liquid level in the storage tank, and the historical key data records at least include pressure change data, temperature change data, liquid level change data and timestamp data.

3. The method for detecting the pressure of a storage tank breathing valve according to claim 2, characterized in that: The key parameters also include the opening and closing state of the breathing valve and the opening and closing time when the opening and closing state changes. The specific steps of inputting at least part of the historical key data records into the preset breathing valve performance evaluation relationship and calculating the local characteristic value used to characterize the performance of the breathing valve include: According to the opening and closing states and the corresponding opening and closing times, at least part of the timestamp data is divided into a time period of rapid pressure change and a time period of slow pressure change; Obtaining the pressure slow change time period closest to the current time from among the plurality of pressure slow change data segments as the most recent pressure slow change time period; Substituting the pressure change data and the liquid level change data corresponding to at least part of the timestamp data contained in the most recent slow pressure change time period into a preset breathing valve performance evaluation relationship to calculate the local characteristic value corresponding to each timestamp data and the credibility of the local characteristic value; Calculating the normality of the breathing valve according to the local features and their credibility; Extracting the critical pressure moment at which the critical pressure point appears in the rapid pressure change period according to the pressure change data, and substituting the pressure change data corresponding to the critical pressure moment into the preset breathing valve performance evaluation relationship to obtain the critical point abnormality degree of the breathing valve; The normal degree and the critical point abnormal degree are substituted into the breathing valve performance evaluation relationship to calculate the abnormal degree used to characterize the breathing valve performance.

4. The method for detecting the pressure of a storage tank breathing valve according to claim 3, characterized in that: The specific steps of dividing at least part of the pressure change data into a pressure fast change data segment and a pressure slow change data segment according to the opening and closing states and their corresponding opening and closing times include: According to the opening and closing status, the corresponding opening and closing time is divided into opening time and closing time; A plurality of consecutive timestamp information between an opening time and an adjacent next closing time is used as a time period of rapid pressure change; A plurality of consecutive timestamp information between a closing time and an adjacent next opening time is used as a time period of slow pressure change.

5. The method for detecting the pressure of a storage tank breathing valve according to claim 3 or 4, characterized in that: The specific steps of substituting the pressure change data and the liquid level change data corresponding to at least part of the timestamp data contained in the most recent slow pressure change time period into the preset breathing valve performance evaluation relationship to calculate the local eigenvalue corresponding to each timestamp data and the credibility of the local eigenvalue include: Acquire at least part of the timestamp data contained in the most recent slow pressure change time period and its corresponding pressure change data and liquid level height change data; Calculate the pressure change Δp between each acquired pressure change data and the corresponding pressure change data at the previous moment; Calculate the liquid level change amount Δh between each acquired liquid level change data and the corresponding liquid level change data at the previous moment; Obtain the local characteristic moment corresponding to each timestamp data according to the liquid level height change Δh; The pressure change Δp and height change Δh corresponding to the local characteristic moment of each timestamp data are calculated by the least squares method to obtain the pressure-liquid level relationship: Δp(t)=k×Δh(t)+b Among them, k is the slope of the straight line obtained by least squares fitting, which is also the local eigenvalue of the timestamp data; At least part of the temperature change data is acquired, and the at least part of the temperature change data, the local eigenvalue k and the pressure change data are substituted into a preset credibility calculation relationship to calculate the credibility l corresponding to the local eigenvalue.

6. The method for detecting the pressure of a storage tank breathing valve according to claim 5, characterized in that: The specific steps of calculating the normality of the breathing valve according to the local features and their credibility include: Extracting a number of historical reference moments from the historical key data records, and calculating local feature values ​​and credibility corresponding to the historical reference moments; Substituting the historical characteristic value and the historical credibility corresponding to the historical reference moment and the local characteristic value and the credibility into the preset matching degree calculation relationship to obtain the matching degree corresponding to the local characteristic value and the credibility; The calculated degree of conformity is substituted into a preset normal degree calculation relationship to obtain the normal degree.

7. The method for detecting the pressure of a storage tank breathing valve according to claim 3 or 4, characterized in that: The specific steps of extracting the critical pressure moment when the critical pressure point appears in the rapid pressure change period according to the pressure change data, and substituting the pressure change data corresponding to the critical pressure moment into the preset breathing valve performance evaluation relationship to obtain the critical point abnormality degree of the breathing valve include: Obtain the critical pressure point in the pressure rapid change data segment, and use the timestamp data of the critical pressure point as the critical pressure moment; The pressure critical point is divided into an upper pressure critical point and a lower pressure critical point according to the value of the pressure critical point; Calculate the mean and standard deviation of the upper pressure critical point and the lower pressure critical point respectively, and calculate the upper pressure threshold and the lower pressure threshold according to the mean and the standard deviation; The proportion of all upper pressure critical points that are greater than the upper pressure threshold is calculated as the first abnormality degree, and the proportion of all lower pressure critical points that are less than the lower pressure threshold is calculated as the second abnormality degree.

8. The method for detecting the pressure of a storage tank breathing valve according to claim 6, characterized in that: The specific steps of substituting the normal degree and the critical point abnormal degree into the breathing valve performance evaluation relationship to calculate the abnormal degree for characterizing the breathing valve performance include: Substitute the normal degree, the first abnormal degree and the second abnormal degree into the following relationship to calculate the abnormal degree representing the performance of the breathing valve: Wherein, D is the normal degree, C1 is the first abnormal degree, C2 is the second abnormal degree, norm is the normalization calculation function, and G is the abnormal degree used to characterize the performance of the breathing valve.

9. A tank breathing valve pressure detection system, characterized in that: The storage tank breathing valve pressure detection system comprises: A key parameter acquisition module is provided in the storage tank and is used to acquire key parameters related to the storage tank and the breathing valve; A data storage module, used to store preset data record tables and store key parameters to update historical key data records; A data processing module, used for storing and running a preset breathing valve performance evaluation relationship to output an abnormality degree for characterizing the performance of the breathing valve; The control module is connected to the data processing module to obtain the degree of abnormality, and is used to determine whether the degree of abnormality exceeds a preset normal range, and output a warning signal after the degree of abnormality exceeds the preset normal range.

10. A tank breathing valve pressure detection device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the tank breathing valve pressure detection method according to any one of claims 1 to 8 are implemented.

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