A method, system and device for pressure detection of a storage tank breathing valve

Abstract

The present application relates to a method, system and device for detecting the pressure of a storage tank breathing valve. The method includes: obtaining key parameters for characterizing the current state inside the storage tank, storing 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, and calculating the degree of abnormality for characterizing the performance of the breathing valve; determining whether the degree of abnormality exceeds a preset normal range, and if so, outputting a warning signal. By using this method, 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 can be obtained. This parameter describes the current working state of the breathing valve in numerical form, and then by setting a warning threshold for the degree of abnormality to monitor the degree of abnormality of the breathing valve, the function of real-time monitoring whether the breathing valve is working properly can be realized.

Description

Technical Field

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

[0002] With the booming development of the petrochemical industry in China, the number of storage tanks is increasing day by day. As a commonly used device in storage tanks and the process industry, the normal operation of the breather valve is crucial for the safety of the storage tank and the petrochemical raw materials or products stored in the tank. Therefore, how to ensure the normal operation of the breather valve has become a highly concerned issue in the industry.

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

[0004] However, this method can still only perform quality inspection on the breather valve during ex-factory, but cannot monitor the status of the breather valve in actual working conditions. Summary of the Invention

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

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

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

[0008] Input at least part of the historical key data record into a preset breather valve performance evaluation relationship, and calculate the degree of abnormality for characterizing the performance of the breather valve;

[0009] Judge whether the degree of abnormality exceeds a preset normal range, and if so, output a warning signal.

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

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

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

[0013] According to the opening and closing state and its corresponding opening and closing time, divide at least part of the timestamp data into a pressure rapid change time period and a pressure slow change time period;

[0014] Obtain the pressure slow change time period closest to the current time among several pressure slow change data segments as the closest pressure slow change time period;

[0015] Substitute the pressure change data and liquid level height change data corresponding to at least part of the timestamp data included in the closest pressure slow change time period into the preset breather valve performance evaluation relationship to calculate the local characteristic value and the credibility of the local characteristic value corresponding to each timestamp data;

[0016] Calculate the normal degree of the breather valve according to the local characteristic and its credibility;

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

[0018] Substitute the normal degree and the critical point abnormal degree into the breather valve performance evaluation relationship to calculate the abnormal degree used to characterize the breather valve performance.

[0019] In one embodiment, the specific steps for dividing at least part of the pressure change data into a pressure rapid change data segment and a pressure slow change data segment according to the opening and closing state and its corresponding opening and closing time include:

[0020] Classify the corresponding opening and closing times into an opening time and a closing time according to the opening and closing states;

[0021] Regard a number of consecutive timestamp information between one opening time and the adjacent next closing time as a rapid pressure change time period;

[0022] Regard a number of consecutive timestamp information between one closing time and the adjacent next opening time as a slow pressure change time period.

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

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

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

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

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

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

[0029] Δp(t) = k×Δh(t) + b

[0030] where k is the slope of the straight line obtained by least - squares fitting and is also the local eigenvalue of the timestamp data;

[0031] Obtain at least part of the temperature change data, and substitute the 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.

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

[0033] Extract several historical reference times from the historical key data record, and calculate the local eigenvalue and credibility corresponding to the historical reference times;

[0034] Substitute the historical feature values, historical credibility, local feature values, and credibility corresponding to the historical reference moment into the preset calculation relationship of the degree of conformity to obtain the degree of conformity corresponding to the local feature values and credibility;

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

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

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

[0038] Classify the pressure critical points into upper pressure critical points and lower pressure critical points according to the values of the pressure critical points;

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

[0040] Calculate the proportion of the number of upper pressure critical points greater than the upper pressure threshold among all the upper pressure critical points as the first abnormal degree, and calculate the proportion of the number of lower pressure critical points less than the lower pressure threshold among all the lower pressure critical points as the second abnormal degree.

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

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

[0043]

[0044] Where 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.

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

[0046] A key parameter acquisition module, which is arranged 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, configured to store a preset data record table and store key parameters to update historical key data records;

[0048] A data processing module, configured to store and run a preset breathing valve performance evaluation relationship to output the degree of abnormality characterizing the breathing valve performance;

[0049] A control module, connected to the data processing module to obtain the degree of abnormality, configured 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] Thirdly, the present application further provides a pressure detection device for a storage tank breathing valve. The device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

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

[0052] Input at least part of the historical key data records into the preset breathing valve performance evaluation relationship, and calculate the degree of abnormality characterizing the breathing valve performance;

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

[0054] The above-mentioned pressure detection method, device, computer equipment, storage medium and computer program product for the storage tank breathing valve, 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, which 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 a warning threshold for the degree of abnormality, so as to realize the function of real-time monitoring of whether the breathing valve is working properly. Description of the Drawings

[0055] Figure 1 It is an application environment diagram of the pressure detection method for the storage tank breathing valve in an embodiment;

[0056] Figure 2 It is a flow schematic diagram of the pressure detection method for the storage tank breathing valve in an embodiment;

[0057] Figure 3 It is a system structure topology diagram of the pressure detection system for the storage tank breathing valve in an embodiment;

[0058] Figure 4 It is an internal structure diagram of the pressure detection device for the storage tank breathing valve in an embodiment. Detailed Embodiments

[0059] In order to make the objectives, technical solutions, and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] The method for detecting the pressure of the storage tank breathing valve provided by the embodiment of the present application can be applied to an application environment as shown in 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 can be placed in 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 liquid state 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, as shown in Figure 2 In the figure, a method for detecting the pressure of the storage tank breathing valve is provided. Taking the method applied to the server in Figure 1 as an example, the method includes the following steps:

[0062] Step S100: 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 a new historical key data record.

[0063] Among them, the key parameters include the real-time temperature, real-time pressure, and real-time liquid level height. 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] Store the real-time temperature, real-time pressure, real-time liquid level height, and their corresponding sampling timestamp information together in a preset data record table to obtain a historical key data record including data such as real-time temperature, real-time pressure, real-time liquid level height, and sampling timestamp information. The historical key data record is data used to represent the change trends of parameters such as temperature, pressure, and liquid level height in the storage tank. The historical key data record at least includes pressure change data, temperature change data, liquid level height change data, and timestamp data; when the real-time temperature, real-time pressure, and / or real-time liquid level height in the storage tank exceeds the preset range, the breathing valve or air supply valve connected to the storage tank will switch its opening and closing state to make the temperature, pressure, and / or liquid level height in the storage tank return to the normal range. When the breathing valve or air supply valve switches its opening and closing state, its switched state and the switching time will also be stored as key data in the preset data record table.

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

[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. This parameter describes the current working state of the breathing valve in numerical form. Then, by setting a warning threshold for the degree of abnormality, the degree of abnormality of the breathing valve is monitored, so as to realize the function of real-time monitoring of whether the breathing valve is working properly. Moreover, this method does not require improvement of the hardware structure of the breathing valve. By directly calculating the key parameters collected by various sensors installed on the tank body, the indirect evaluation of the working state of the breathing valve can be completed, effectively reducing the use cost.

[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, divide at least part of the timestamp data into a pressure rapid change time period and a pressure slow change time period. Specifically, step S210 further includes:

[0069] Step S211: Divide the corresponding opening and closing times into an opening time and a closing time according to the opening and closing states.

[0070] Step S212: Take a continuous number of timestamp information between one opening time and the adjacent next closing time as the pressure rapid change time period.

[0071] Step S213: Take a continuous number of timestamp information between one closing time and the adjacent next opening time as the pressure slow change time period.

[0072] Through steps S211 to S213, taking the opening time and closing time of the pressure relief valve and the air replenishing valve in the historical key data record as nodes, the timestamp data in the historical key data record is divided into several time periods. The pressure change data, temperature change data, and liquid level height change data corresponding to the timestamp data included in the time period are also divided into several data segments. The time period starting from the opening time of the pressure relief valve or the air replenishing valve in the divided time period is used as the pressure rapid change time period, and the corresponding pressure change data, temperature change data, and liquid level height change data are recorded as the pressure rapid change data segment; the time period starting from the closing time of the pressure relief valve or the air replenishing valve in the divided time period is used as the pressure slow change time period, and the corresponding pressure change data, temperature change data, and liquid level height change data are recorded as the pressure slow change data segment.

[0073] Step S220: Obtain the pressure slow change time period closest to the current time among several pressure slow change data segments as the closest pressure slow change time period.

[0074] Among them, the current time is the timestamp data of the latest obtained key data; if the current time is within a rapid change time period, that is, the pressure relief valve or the air replenishing valve is in the open state at this time, then the closest pressure slow change time period at this time is the pressure slow change time period after the previous valve closing; if the current time is within a pressure slow change time period, then the closest pressure slow change time at this time is the pressure slow change time period where the current time is located.

[0075] Step S230: Substitute the pressure change data and the liquid level height change data corresponding to at least part of the timestamp data included in the closest pressure slow 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.

[0076] Among them, 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 included in the closest pressure slow change time period and their corresponding pressure change data and liquid level height change data.

[0078] Step S232: Calculate the pressure change amount AP between each obtained pressure change data and the pressure change data at the previous moment corresponding to it.

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

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

[0081] In this embodiment, the specific method for obtaining the local characteristic time is as follows: sequentially extract the timestamp data in the slow change time period as the current moment, the time interval between each timestamp data is the unit time, obtain the number of consistent directions of the liquid level height change amount (that is, the positive or negative of Δh) within 5 unit times before the current moment. If the directions of the liquid level height change amounts within 5 unit times are consistent (that is, 5 consecutive Δh are all positive or all negative), then use the current moment as the local characteristic moment. If they are inconsistent, then use the moments corresponding to several change amounts with consistent height changes adjacent to this moment as the local characteristic moments of this moment, and count the number of local characteristic moments.

[0082] Step S235: Calculate the pressure-liquid level height relationship by the least squares method for the pressure change Δp and the height change Δh corresponding to the local feature moments of each timestamp data:

[0083] Δp(t) = k×Δh(t) + b

[0084] where k is the slope of the straight line obtained by least squares fitting and is also the local feature value of the timestamp data;

[0085] Obtain at least part of the temperature change data, and substitute the at least part of the temperature change data, the local feature value k, and the pressure change data into a preset credibility calculation relationship to calculate the credibility l corresponding to the local feature value.

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

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

[0088]

[0089] where l is the credibility of the feature value, n is the serial number of the timestamp data included in the pressure slow change time period, that is, the serial number of the current moment in the pressure slow change time period, Δp 实，n is the actual pressure change at the nth moment, Δp 预测，n is the pressure value corresponding to the fitting straight line at the nth moment. N i is the number of the i-th local feature moments at the current moment, and i is the serial 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 i is the temperature value of the i-th local feature moment at the current moment, I0 is the temperature value at the current moment, and norm() is a normalization calculation function.

[0090] Step S240: Calculate the normality of the breather valve according to the local features and their credibility. Among them, the preset breather valve performance evaluation relationship also includes a preset compliance calculation relationship. The specific steps of Step S240 also include:

[0091] Step S241: Extract several historical reference moments from the historical key data records, and calculate the corresponding local feature values and credibility at the historical reference moments.

[0092] In this step, the slow pressure change data segments can be classified into two categories according to the valves opened by the previous fast pressure change data segment, including the upper pressure critical point category after the air release valve is opened and the lower pressure critical point category after the air replenishment valve is opened; the specific steps of step S241 include obtaining the type of the valve opened at the current moment once, and obtaining several moments with the same liquid level height as the current moment among several slow pressure change data segments with the same valve opening type as that at the moment before the current moment in the historical data, which are recorded as several historical reference moments of the current moment. The local feature values and credibility corresponding to the historical reference moments in this step can either be recalculated or saved during the previous calculation of local feature values and credibility and directly read in this step.

[0093] Step S242: Substitute the historical feature values, historical credibility, local feature values, and credibility corresponding to the historical reference moments into the preset correspondence degree calculation relationship to obtain the correspondence degree corresponding to the local feature values and credibility.

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

[0095]

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

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

[0098]

[0099] where D is the normal degree, 30 is the number of moments in the reference data segment of the current moment, q is the serial number of the timestamp data corresponding to each moment in the reference data segment, represents the historical correspondence degree S of the local feature value of each moment q adjusted by time. The closer to the current moment, the more credible the historical correspondence degree S q of this moment.

[0100] Step S250: Extract the pressure critical moments when pressure critical points appear in the pressure rapid change time period according to the pressure change data, and substitute the pressure change data corresponding to the pressure critical moments into the preset breathing valve performance evaluation relationship to obtain the abnormal degree of the critical points of the breathing valve.

[0101] Specifically, the specific steps of step S250 are as follows: Obtain the pressure critical points that appear in the pressure rapid change data segment, and use the timestamp data when the pressure critical points appear as the pressure critical moments; Divide the pressure critical points into upper pressure critical points and lower pressure critical points according to the values of the pressure critical points; Calculate the mean and standard deviation of the upper pressure critical points and the lower pressure critical points respectively, and calculate the upper pressure threshold and the lower pressure threshold according to the mean and the standard deviation; Calculate the proportion of the number of upper pressure critical points that are greater than the upper pressure threshold among all the upper pressure critical points as the first abnormal degree, and calculate the proportion of the number of lower pressure critical points that are less than the lower pressure threshold among all the lower pressure critical points as the second abnormal degree.

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

[0103] Among them, the preset breathing valve performance evaluation relationship also includes an abnormal degree 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 abnormal degree calculation relationship to calculate the abnormal degree used to characterize the performance of the breathing valve:

[0104]

[0105] Among them, 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.

[0106] Step S300: Judge whether the abnormal degree exceeds the preset normal range. If so, output a warning signal.

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

[0108] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limitation, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0109] Based on the same inventive concept, an embodiment of the present application further provides a tank breather valve pressure detection device for implementing the above-mentioned tank breather valve pressure detection method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the following tank breather valve pressure detection device can refer to the limitations on the tank breather valve pressure detection method in the above text, and will not be repeated here.

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

[0111] The key parameter acquisition module is arranged inside the tank and is used to acquire key parameters related to the tank and the breather valve;

[0112] The data storage module is used to store a preset data record table and store key parameters to update the historical key data record;

[0113] The data processing module is used to store and run a preset breather valve performance evaluation relationship to output the degree of abnormality characterizing the breather valve performance;

[0114] The control module is connected to the data processing module to obtain the degree of abnormality, and is used to judge whether the degree of abnormality exceeds the 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 breather valve pressure detection device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0116] In one embodiment, a pressure detection device for a storage tank breather valve is provided, and its internal structure diagram can be as shown in Figure 4 . 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, it implements a method for detecting the pressure of a storage tank breather valve. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0117] Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures 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 some components, or have a different component layout.

[0118] In one embodiment, a pressure detection device for a storage tank breather valve is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

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

[0120] Input at least part of the historical key data records into a preset breather valve performance evaluation relationship, and calculate the degree of abnormality for characterizing the breather valve performance;

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

[0122] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. 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. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0123] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope described in this specification.

[0124] The above-described embodiments only represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, 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 abnormality exceeds the preset normal range, and if so, output a warning signal; 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 a historical key data record including data such as the real-time temperature, real-time pressure, real-time liquid level and sampling timestamp information, wherein the historical key data record is data used to indicate the change trend of parameters such as the temperature, pressure and liquid level in the storage tank, and the historical key data record at least includes pressure change data, temperature change data, liquid level change data and timestamp data; 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.

2. The method for detecting the pressure of a storage tank breathing valve according to claim 1, 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.

3. The method for detecting the pressure of a storage tank breathing valve according to claim 1 or 2, 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 between each acquired pressure change data and its corresponding pressure change data at the previous moment ; Calculate the liquid level change amount between each acquired liquid level change data and its corresponding liquid level change data at the previous moment ; According to the change of liquid level Get the local feature moment corresponding to each timestamp data; The pressure change corresponding to the local characteristic moment of each timestamp data and height variation The pressure-liquid level relationship is calculated by the least squares method: ; 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; Obtain at least part of the temperature change data, and substitute the 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 corresponding to the local eigenvalue .

4. The method for detecting the pressure of a storage tank breathing valve according to claim 3, 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.

5. The method for detecting the pressure of a storage tank breathing valve according to claim 1 or 2, 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.

6. The method for detecting the pressure of a storage tank breathing valve according to claim 4, 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.

7. 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; A control module, connected to the data processing module to obtain the degree of abnormality, 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; 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 a historical key data record including data such as the real-time temperature, real-time pressure, real-time liquid level and sampling timestamp information, wherein the historical key data record is data used to indicate the change trend of parameters such as the temperature, pressure and liquid level in the storage tank, and the historical key data record at least includes pressure change data, temperature change data, liquid level change data and timestamp data; 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.

8. 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 6 are implemented.

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