Abnormal state analysis method and apparatus, device and medium

Abstract

Disclosed is an abnormal state analysis method, comprising: acquiring a sampling data set, wherein the sampling data set is configured to represent a collection of sampling data obtained by a plurality of sampling instruments in a valve cooling system sampling multiple times within an instrument sampling cycle, and the sampling time points and sampling types of the plurality of sampling instruments are the same; performing differential value processing on the sampling data set to obtain a differential data set; performing tradeoff screening processing on the differential data set to obtain tradeoff data; and performing adjacent period-over-period comparison processing on the tradeoff data to obtain an anomaly analysis result. The phenomenon of abnormal state misjudgment in the valve cooling system can be effectively reduced, thereby effectively improving the accuracy of abnormal state judgment of the valve cooling system. Also disclosed an apparatus, a device and a medium. The abnormal state analysis method and apparatus, the device and the medium can be widely applied to the technical field of valve cooling system detection.

Description

Abnormal state analysis method, device, equipment and medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure claims priority to Chinese patent application number 2024103144253, filed with the Chinese Patent Office on March 19, 2024, entitled “A method, device, equipment and medium for analyzing abnormal conditions,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The present disclosure relates to the technical field of valve cooling system detection, and in particular to an abnormal state analysis method, device, equipment and medium. Background Art

[0004] One of the important reasons for the long-term stable operation of HVDC converter stations is that the valve cooling control system of the converter valve (referred to as the valve cooling system) can accurately detect the operating values ​​of physical quantities at key points in the system and the status of sampling instruments in real time.

[0005] At present, the conventional valve cooling system involves redundant design, that is, setting multiple sampling instruments at key points of the system and taking real-time values ​​for judgment. This method still leads to misjudgment of abnormal status of the valve cooling system, and the accuracy of abnormal status judgment still needs to be improved.

[0006] Therefore, the problems existing in related technologies still need to be solved and optimized urgently.

[0007] Summary of the Invention

[0008] The purpose of the present disclosure is to solve one of the technical problems existing in the related art to at least a certain extent.

[0009] To this end, one purpose of the embodiments of the present disclosure is to provide an abnormal state analysis method, which can effectively reduce the misjudgment of the abnormal state of the valve cooling system and effectively improve the accuracy of the judgment of the abnormal state of the valve cooling system.

[0010] Another object of the embodiments of the present disclosure is to provide an abnormal state analysis device.

[0011] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present disclosure include:

[0012] In a first aspect, an embodiment of the present disclosure provides a method for analyzing an abnormal state, which is applied to a converter valve cooling system. The method includes:

[0013] Acquire a sampling data set, wherein the sampling data set is configured to represent a plurality of sampling instruments in the valve cooling system, and is a collection of sampling data obtained by taking values ​​multiple times within a sampling period of the instruments, wherein the sampling time points and sampling types of the plurality of sampling instruments are the same;

[0014] Perform differential value processing on the sample data set to obtain a differential data set,

[0015] Performing weighing and screening processing on the differential data set to obtain weighed data;

[0016] Adjacent link comparison judgment processing is performed on the weighted data to obtain an abnormality analysis result.

[0017] In addition, the abnormal state analysis method according to the above embodiment of the present disclosure may also have the following additional technical features:

[0018] Optionally, in an embodiment of the present disclosure, performing differential value processing on the sample data set to obtain a differential data set includes:

[0019] Performing value screening processing on the sampled data set according to the sampling period to obtain a first data set;

[0020] Perform differential averaging processing on the first data set to obtain the differential data set.

[0021] Optionally, in one embodiment of the present disclosure, the differential data set includes a plurality of differential average data, and the weighing and screening processing is performed on the differential data set to obtain the weighed data, including:

[0022] Performing a first difference evaluation process on the differential data set to obtain a second data set, wherein the second data set is configured to represent an absolute value of a difference between differential average data corresponding to different sampling instruments at the same sampling time point;

[0023] According to the second data set, optimal weighing processing is performed on the differential data set to obtain the weighed data.

[0024] Optionally, in one embodiment of the present disclosure, performing optimal trade-off processing on the differential data set according to the second data set to obtain the trade-off data includes:

[0025] performing sorting and comparison processing on the second data set to obtain a comparison result;

[0026] According to the comparison result, performing a primary screening process on the differential data set to obtain a third data set;

[0027] An optimal screening process is performed on the third data set to obtain the weighted data.

[0028] Optionally, performing optimal screening on the third data set to obtain the weighted data includes:

[0029] The third data set is optimally screened according to a preset sampling condition corresponding to the sampling type to obtain the weighted data.

[0030] Optionally, in one embodiment of the present disclosure, performing adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result includes:

[0031] performing adjacent extraction processing on the differential data set according to the weighted data to obtain a fourth data set;

[0032] According to the fourth data set, difference analysis processing is performed on the weighted data to obtain the abnormality analysis result.

[0033] Optionally, in one embodiment of the present disclosure, the differential data set includes a plurality of differential average data, and performing differential analysis processing on the weighted data based on the fourth data set to obtain the abnormality analysis result includes:

[0034] performing a second difference evaluation process on the fourth data set to obtain a fifth data set, wherein the fifth data set is configured to represent absolute values ​​of differences between differential average data corresponding to the same sampling instrument at adjacent sampling time points;

[0035] According to the fifth data set, a comparison screening process is performed on the weighted data to obtain the abnormality analysis result.

[0036] Optionally, in an embodiment of the present disclosure, performing a comparison screening process on the weighted data based on the fifth data set to obtain the abnormality analysis result includes:

[0037] Get the preset abnormal threshold and warning times;

[0038] Performing a month-on-month threshold processing on the fifth data set to obtain a month-on-month value;

[0039] According to the month-on-month value, the abnormality threshold and the number of warnings, threshold judgment processing is performed on the weighed data to obtain the abnormality analysis result.

[0040] Optionally, performing threshold judgment processing on the weighted data according to the month-on-month value, the abnormality threshold, and the number of warnings to obtain the abnormality analysis result includes:

[0041] Obtaining a calculated intermediate value based on the difference between the month-on-month value and the weighted data;

[0042] Obtaining the number of abnormalities according to a comparison result between the calculated intermediate value and the abnormality threshold;

[0043] The abnormality analysis result is obtained according to the number of abnormalities and the number of warnings.

[0044] In a second aspect, an embodiment of the present disclosure provides an abnormal state analysis device, which is applied to a converter valve cooling system. The analysis device includes:

[0045] an acquisition module configured to acquire a sampling data set, wherein the sampling data set is configured to represent a plurality of sampling instruments in the valve cooling system, and is a collection of sampling data obtained by taking values ​​multiple times within a sampling period of the instruments, wherein the sampling time points and sampling types of the plurality of sampling instruments are the same;

[0046] A difference module is configured to perform difference value processing on the sample data set to obtain a difference data set,

[0047] a screening module configured to perform a weighing screening process on the differential data set to obtain weighed data;

[0048] The processing module is configured to perform adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result.

[0049] In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:

[0050] at least one processor;

[0051] at least one memory configured to store at least one program;

[0052] When the at least one program is executed by the at least one processor, the at least one processor implements the method of the first aspect described above.

[0053] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing a program executable by a processor, wherein the program executable by the processor is configured to implement the method of the first aspect when executed by the processor.

[0054] The advantages and benefits of the present disclosure will be partially given in the following description, and partially become apparent from the following description, or learned through practice of the present disclosure:

[0055] The embodiments of the present disclosure disclose an abnormal state analysis method, device, equipment and medium, wherein the analysis method obtains a sampling data set, wherein the sampling data set is configured to represent multiple sampling instruments in the valve cooling system, and is a collection of sampling data obtained by multiple sampling values ​​within the instrument sampling period, wherein the sampling time points and sampling types of the multiple sampling instruments are the same; the sampling data set is subjected to differential value processing to obtain a differential data set, and the differential data set is subjected to weighted screening processing to obtain weighted data; and the weighted data is subjected to adjacent ring comparison judgment processing to obtain abnormal analysis results. The analysis method can reduce the error influence of independent time factors on the real-time value of sampling by sequentially performing differential value taking and weighted screening on the collection of sampling data obtained by multiple sampling values ​​within the instrument sampling period (i.e., the sampling data set), effectively reducing the phenomenon of misjudging abnormal states in the valve cooling system, thereby effectively improving the accuracy of abnormal state judgment of the valve cooling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or related technologies, the following introduction is made to the drawings of the relevant technical solutions in the embodiments of the present disclosure or related technologies. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0057] FIG1 is a schematic flow chart of an abnormal state analysis method provided by an embodiment of the present disclosure;

[0058] FIG2 is a schematic structural diagram of an abnormal state analysis device provided by an embodiment of the present disclosure;

[0059] FIG3 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] The embodiments of the present disclosure are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure and are not to be construed as limiting the present disclosure. The step numbers in the following embodiments are provided only for the convenience of explanation and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art of the present disclosure. The terms used herein are only for the purpose of describing the embodiments of the present disclosure and are not intended to limit the present disclosure.

[0062] Currently, conventional valve cooling systems rely on redundant design, placing multiple sampling instruments at key points in the system to generate real-time readings for judgment. This approach's sampling accuracy depends on the instantaneous accuracy of the sampling time, and due to factors such as independent time, it can still lead to misjudgments of abnormal valve cooling system conditions. Furthermore, conventional valve cooling systems often experience abnormality reports due to factors such as disturbances in the sampling sensors. These abnormal data can affect the accuracy of conventional abnormality judgments, and this approach still needs to be improved.

[0063] In view of this, an embodiment of the present disclosure provides an abnormal state analysis method, which can effectively reduce the dependence on the accuracy of the sampling instrument and reduce the influence of independent time factors on the error by performing differential value taking on the set of sampling data obtained by multiple values ​​taking within the instrument sampling period; in addition, the analysis method can also screen out the differential average data with the smallest difference at the same sampling time point by weighing and screening the various differential average data in the differential data set, thereby effectively improving the accuracy of the abnormal state judgment of the valve cooling system; in addition, the analysis method can also effectively reduce the abnormal reporting phenomenon caused by malfunction of the sampling sensor in the traditional valve cooling system based on the adjacent ring ratio judgment processing of the weighed data, thereby effectively reducing the situation of misjudgment of the abnormal state of the valve cooling system.

[0064] The present disclosure can be applied to a wide range of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present disclosure can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present disclosure can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0065] 1 , in an embodiment of the present disclosure, a method for analyzing an abnormal state includes:

[0066] Step 110: Acquire a sampling data set, wherein the sampling data set is configured to represent multiple sampling instruments in the valve cooling system, and is a collection of sampling data obtained by multiple samplings within an instrument sampling period, wherein the sampling time points and sampling types of the multiple sampling instruments are the same;

[0067] In the embodiment of the present disclosure, the sampling data set can be obtained by sampling instruments set at key positions of the valve cooling system. Specifically, for a certain key position, 2, 3, 4 or even more sampling instruments can be set to obtain the required sampling data. The sampling time points and sampling types of the sampling data obtained by these sampling instruments are the same. The embodiment of the present disclosure takes 3 sampling instruments as an example, and the remaining examples can be simply inferred.

[0068] It is understood that the instrument sampling period in the embodiments of the present disclosure can be determined based on the sampling sensitivity of the sampling instrument. The sampling instrument can be a sensor, and the sampling type of the sampled data can be at least one of temperature, flow rate, humidity, pressure, conductivity, liquid level, and other valve parameters. The embodiments of the present disclosure use temperature and flow rate as examples, and the remaining examples can be simply deduced by analogy. Furthermore, the sampling time point can be the sampling point of the sampling instrument during the instrument sampling period.

[0069] For example, step 110 may be to perform high-frequency sampling based on the sampling sensitivity of the sampling instrument, taking multiple values ​​at sampling time points within the sampling period, thereby increasing the number of samples and obtaining a sampled data set. Furthermore, since the sampled data of the sampled data set comes from multiple sampling instruments, taking the temperature sampling type as an example, the sampled data set may include multiple first temperature sampling data, multiple second temperature sampling data, and multiple third temperature sampling data. The first temperature sampling data corresponds to the first temperature sampling instrument, the second temperature sampling data corresponds to the second temperature sampling instrument, and the third temperature sampling data corresponds to the third temperature sampling instrument. Each first temperature sampling data corresponds to one second temperature sampling data and one third temperature sampling data. The same applies to the flow sampling type, and the present disclosure will not elaborate on this in detail.

[0070] Step 120: Perform differential value processing on the sample data set to obtain a differential data set.

[0071] In some embodiments, step 120 of performing differential value processing on the sample data set to obtain a differential data set includes:

[0072] Step 121: Perform value screening on the sampled data set according to the sampling period to obtain a first data set;

[0073] Step 122: Perform differential averaging processing on the first data set to obtain the differential data set.

[0074] In the embodiment of the present disclosure, after obtaining the sampling data set, the sampling data set can be sorted according to the sampling time points in the sampling period, and then a first data set is generated according to the sampling values ​​at the sampling time points. Then, a differential algorithm is used to reduce the reading error of the sampling instrument, thereby solving the problem of large sampling errors at fixed time points in traditional valve cooling systems, thereby reducing the influence of the current sampling accuracy of the sampling instrument depending on the instantaneous accuracy of the fixed time point (that is, reducing the influence of independent time factors on the error), thereby obtaining a differential data set, and each differential average data in the differential data set corresponds to a sampling value at a fixed time point in the traditional valve cooling system.

[0075] For example, the first temperature sampling instrument a obtains the sampled storage values ​​(i.e., sampling data) of t8, t9, t10, t11, t12, and t13 at the sampling time points Tn+8δ, Tn+9δ, Tn+10δ, Tn+11δ, Tn+12δ, and Tn+13δ, respectively, where Tn- is the starting time point of the sampling period, 8δ is the eighth sampling time point of the sampling period, and the rest are similar. Then, the differential average data corresponding to the first temperature sampling instrument is determined by differential averaging processing. One of the equivalent formulas of the differential average data can be expressed as:

[0076] Wherein, ta1 is the differential average data corresponding to the first temperature sampling instrument.

[0077] It is understood that the differential averaging process in the embodiments of the present disclosure can also be implemented based on a sliding window differential, specifically based on a preset sliding window length and step size. Furthermore, for other temperature sampling instruments or flow sampling instruments, the same can be deduced based on the relevant content of the first temperature instrument, and the present disclosure will not elaborate on this further.

[0078] Step 130: performing weighted screening processing on the differential data set to obtain weighted data;

[0079] In some embodiments, the differential data set includes a plurality of differential average data, and step 130 of performing weighted screening processing on the differential data set to obtain weighted data includes:

[0080] Step 131: performing a first difference evaluation process on the differential data set to obtain a second data set, wherein the second data set is configured to represent the absolute value of the difference between the differential average data corresponding to different sampling instruments at the same sampling time point;

[0081] It can be understood that since the embodiment of the present disclosure takes three sampling instruments as an example, for the sampling type of temperature, the second data set may include three second data. The first second data may represent the absolute value of the difference between the differential average data of the first temperature sampling instrument and the differential average data of the second temperature sampling instrument, the second second data may represent the absolute value of the difference between the differential average data of the second temperature sampling instrument and the differential average data of the third temperature sampling instrument, and the third second data may represent the absolute value of the difference between the differential average data of the third temperature sampling instrument and the differential average data of the first temperature sampling instrument. The same applies to the sampling type of flow.

[0082] It should be noted that when the number of sampling instruments corresponding to a certain sampling type is 2, the second data set can include 1 second data; when the number of sampling instruments corresponding to a certain sampling type is 4, the second data set can include 6 second data, and the remaining numbers can be derived by analogy. In addition, the equivalent formula for the first second data can be expressed as:

[0083] tab1=|ta1-tb1|

[0084] Among them, tab1 is the first second data, and tb1 is the differential average data corresponding to the second temperature sampling instrument.

[0085] It is worth mentioning that the remaining second data in the temperature example, as well as the second data of other sampling types such as flow, can be simply inferred based on the second data content corresponding to the aforementioned first temperature sampling instrument.

[0086] Step 132: Perform optimal trade-off processing on the differential data set according to the second data set to obtain the trade-off data.

[0087] Optionally, step 132, performing optimal trade-off processing on the differential data set according to the second data set to obtain the trade-off data, includes:

[0088] Step 1321: perform sorting and comparison processing on the second data set to obtain a comparison result;

[0089] Step 1322: Perform a primary screening process on the differential data set according to the comparison result to obtain a third data set;

[0090] Step 1323: Perform optimal screening processing on the third data set to obtain the weighted data.

[0091] In an embodiment of the present disclosure, for the same sampling time point, the sorting and comparison processing can be to sort the second data in the second data set according to the size of the absolute value of the difference, so as to obtain a comparison result; then, based on the comparison result, select the two sampling data corresponding to the smallest absolute value of the difference, so as to complete the first-level screening; then, filter the two sampling data according to the sampling type of the sampling data, so as to complete the optimal screening, and use the final sampling data as the weighing data, and the final sampling data as the display value in the human-machine display interface of the valve cooling system.

[0092] Optionally, step 1323, performing optimal screening on the third data set to obtain the weighted data, includes:

[0093] The third data set is optimally screened according to a preset sampling condition corresponding to the sampling type to obtain the weighted data.

[0094] Exemplarily, if the second data tab1 corresponding to the first temperature sampling instrument is smaller than the second data tbc1 corresponding to the second temperature sampling instrument and the second data tac1 corresponding to the third temperature sampling instrument, the second data corresponding to the first temperature sampling instrument is used as the comparison result. Then, based on the second data, two differential average data corresponding to the second data (i.e., ta1 and tb1) are determined; then, based on the preset sampling conditions corresponding to the temperature sampling type, such as "when the temperature is too high, the valve cooling system will report an abnormality", the larger value between ta1 and tb1 is selected as the final sampling data. For example, when ta1 is greater than tb1, ta1 is used as the final sampling data; and if the second data tbc1 corresponding to the second temperature sampling instrument is the smallest, or if the second data tac1 corresponding to the third temperature sampling instrument is the smallest, etc., then the situation is similar to the above content and can be simply deduced.

[0095] It is understood that for the flow sampling type, the smaller value of the flow sampling data corresponding to the second data can be selected as the final sampling data based on the corresponding preset sampling conditions, such as "when the flow rate is extremely low, the valve cooling system will report an abnormality." Furthermore, in the special case where there are two sampling meters and the number of second data is one, the comparison result of step 1321 can be determined as the second data. The remaining steps are similarly implemented, and this disclosure will not be further elaborated here.

[0096] It's worth noting that, in another alternative approach, the disclosed embodiment can also use the final sampling data corresponding to the largest absolute difference among all differences as the display value on the valve cooling system's human-machine interface. Furthermore, by identifying the two sampling meters with the smallest difference, the disclosed embodiment can filter out more accurate sampling data and display the sampling data most likely to trigger an alert.

[0097] Step 140: Perform adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result.

[0098] In some embodiments, step 140 of performing adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result includes:

[0099] Step 141: Performing adjacent extraction processing on the differential data set according to the weighted data to obtain a fourth data set;

[0100] In the disclosed embodiment, the adjacent ring comparison determination process is configured to reduce abnormal reporting of valve cooling system errors caused by sampling instrument malfunction. Specifically, the adjacent extraction process is configured to filter out the differential average data corresponding to the sampling time point of the weighted data from the differential dataset based on the sampling time point of the weighted data. In other words, the fourth dataset is configured to represent the differential average data corresponding to each sampling instrument and the sampling time point of the weighted data.

[0101] Step 142: Perform difference analysis on the weighted data according to the fourth data set to obtain the abnormality analysis result.

[0102] Optionally, step 142, performing difference analysis on the weighted data according to the fourth data set to obtain the abnormality analysis result, includes:

[0103] Step 1421: Perform a second difference evaluation process on the fourth data set to obtain a fifth data set, wherein the fifth data set is configured to represent the absolute values ​​of differences between differential average data corresponding to the same sampling instrument at adjacent sampling time points;

[0104] In the embodiment of the present disclosure, the second difference evaluation process is similar to the aforementioned first difference evaluation process, and can be simply deduced that the difference lies in that the second data set is configured to represent the absolute value of the difference between the differential average data corresponding to different sampling instruments at the same sampling time point, while the fifth data set is configured to represent the absolute value of the difference between the differential average data corresponding to the same sampling instrument at adjacent sampling time points.

[0105] Specifically, in the fifth data set, the equivalent formula of the fifth data corresponding to a certain adjacent sampling time point of the first temperature sampling instrument can be expressed as:

[0106] ta56=|ta6-ta5|

[0107] Among them, ta56 is the fifth data corresponding to the first temperature sampling instrument and at the 5th sampling time point and the 6th sampling time point, ta6 is the differential average data corresponding to the first temperature sampling instrument and at the 6th sampling time point, and ta5 is the differential average data corresponding to the first temperature sampling instrument and at the 5th sampling time point.

[0108] It's worth noting that the sampling time points for the first difference evaluation process are configured to represent sampling points within the instrument's sampling cycle, while the sampling time points for the second difference evaluation process are configured to represent sampling points corresponding to the differential average data. Furthermore, the fifth data for the temperature sampling instrument at other different sampling times, as well as the flow rate example, can be derived by analogy with the fifth data corresponding to the first temperature sampling instrument described above, and will not be further elaborated upon in this disclosure.

[0109] Step 1422: Perform a quarter-on-quarter screening process on the weighted data based on the fifth data set to obtain the abnormality analysis result.

[0110] Optionally, step 1422, performing a quarter-on-quarter screening process on the weighted data based on the fifth data set to obtain the abnormality analysis result, includes:

[0111] Step 1423: Obtain the preset abnormality threshold and warning times;

[0112] Step 1424: Perform a month-on-month threshold processing on the fifth data set to obtain a month-on-month value;

[0113] Step 1425: Perform threshold judgment processing on the weighted data according to the month-on-month value, the abnormality threshold, and the number of warnings to obtain the abnormality analysis result.

[0114] In the embodiment of the present disclosure, after obtaining the fifth data set, the fifth data corresponding to different sampling instruments at the same adjacent sampling time points can be compared based on the attribute of the sampling time point, and the largest fifth data can be selected as the year-on-year value. The same applies to the remaining sampling time points; then, based on all the obtained year-on-year values, logical condition judgment is performed to finally obtain the abnormality analysis results.

[0115] Optionally, step 1425, performing threshold determination processing on the weighted data based on the month-on-month value, the abnormality threshold, and the number of warnings to obtain the abnormality analysis result, includes:

[0116] Step 151: Obtain a calculated intermediate value based on the difference between the month-on-month value and the weighted data;

[0117] Step 152: Obtain the number of abnormalities based on the comparison result between the calculated intermediate value and the abnormality threshold;

[0118] Step 153: Obtain the abnormality analysis result according to the abnormality number and the warning number.

[0119] In an embodiment of the present disclosure, a difference calculation is performed between each year-on-year value and the weighted data to obtain a calculated intermediate value, which is configured to record the difference between each year-on-year value and the weighted data, as well as the sampling instrument corresponding to the year-on-year value; then, each calculated intermediate value is compared with an abnormality threshold; when the calculated intermediate value is greater than or equal to the abnormality threshold, the number of abnormalities is increased by one, and the total number of abnormalities is obtained by comparing the calculated intermediate value obtained by calculating all year-on-year values ​​with the abnormality threshold; the total number of abnormalities is compared with the number of warnings to obtain an abnormality analysis result.

[0120] Exemplarily, the embodiment of the present disclosure takes the 5th sampling time point and the 6th sampling time point as an example. The fifth data corresponding to the first temperature sampling instrument is ta56, the fifth data corresponding to the second temperature sampling instrument is tb56, and the fifth data corresponding to the third temperature sampling instrument is tc56. The year-on-year threshold processing can be to compare ta56, tb56 and tc56. If tc56 is the largest, it means that the sampling data of the third temperature sampling instrument has changed significantly between the 5th sampling time point and the 6th sampling time point, and tc56 is selected as the year-on-year value; then, tc56 is calculated as the difference between the weighted data and the year-on-year value to obtain a calculated intermediate value. The calculated intermediate value is configured to record the difference between the year-on-year value and the weighted data, as well as the sampling instrument corresponding to the year-on-year value; then, the calculated intermediate value is compared with the abnormal threshold. When the calculated intermediate value is greater than or equal to the abnormal threshold, it is determined to be abnormal. The same is true for the other adjacent sampling time points, and the total number of abnormalities and the number of warnings are statistically determined to obtain the abnormality analysis results, which can be displayed on the valve cooling system human-computer interaction interface.

[0121] It is understood that the specific values ​​of the abnormal threshold and the number of warnings in the embodiment of the present disclosure can be set according to actual conditions, and step 1422 corresponding to the flow sampling type can be derived by referring to the above example of the temperature sampling type, and the present disclosure will not elaborate on it here. It is worth mentioning that the embodiment of the present disclosure analyzes the changing trend of the sampling data of the sampling instrument from multiple angles, including horizontal angles (same sampling instrument and same time axis) and vertical angles (different sampling instruments and same sampling time point), thereby effectively eliminating the influence of abnormal instrument malfunction and error, and effectively improving the accuracy of judging the abnormal state of the valve cooling system.

[0122] The abnormal state analysis device proposed according to the embodiment of the present disclosure is described in detail below with reference to the accompanying drawings.

[0123] 2 , the abnormal state analysis device proposed in an embodiment of the present disclosure includes:

[0124] An acquisition module 101 is configured to acquire a sampling data set, wherein the sampling data set is configured to represent a plurality of sampling instruments in the valve cooling system, and is a collection of sampling data obtained by multiple samplings during an instrument sampling period, wherein the sampling time points and sampling types of the plurality of sampling instruments are the same;

[0125] The difference module 102 is configured to perform difference value processing on the sample data set to obtain a difference data set.

[0126] a screening module 103 configured to perform a weighing screening process on the differential data set to obtain weighed data;

[0127] The processing module 104 is configured to perform adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result.

[0128] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0129] 3 , an embodiment of the present disclosure further provides an electronic device, including:

[0130] at least one processor 201;

[0131] at least one memory 202 configured to store at least one program;

[0132] When the at least one program is executed by the at least one processor 201 , the at least one processor 201 implements the above method embodiment.

[0133] Similarly, it can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0134] The embodiment of the present disclosure further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is configured to implement the above method embodiment when executed by the processor 201.

[0135] Similarly, the contents of the above method embodiments are applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0136] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present disclosure are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0137] In addition, although the present disclosure is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present disclosure. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art will be able to implement the present disclosure set forth in the claims using ordinary techniques without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present disclosure, which is determined by the full scope of the appended claims and their equivalents.

[0138] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the relevant technology or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0139] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions configured to implement the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions on an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0140] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0141] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit configured to implement a logic function for a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0143] Although the embodiments of the present disclosure have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents.

[0144] The above is a specific description of the preferred implementation of the present disclosure, but the present disclosure is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present disclosure. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present disclosure. Industrial Applicability

[0145] In summary, the present disclosure provides an abnormal state analysis method, device, equipment and medium. By performing differential value taking and weighted screening on a set of sampling data (i.e., a sampling data set) obtained by multiple times taking values ​​within the instrument sampling period, the error influence of independent time factors on the real-time value of sampling can be reduced, and the phenomenon of misjudging abnormal states in the valve cooling system can be effectively reduced, thereby effectively improving the accuracy of abnormal state judgment of the valve cooling system, and can be widely used in the field of valve cooling system detection technology.

Claims

1. A method for analyzing abnormal conditions, characterized in that: Applied to the converter valve cooling system, the analysis method includes: Acquire a sampling data set, wherein the sampling data set is configured to represent a plurality of sampling instruments in the valve cooling system, and is a collection of sampling data obtained by taking values ​​multiple times within a sampling period of the instruments, wherein the sampling time points and sampling types of the plurality of sampling instruments are the same; Perform differential value processing on the sample data set to obtain a differential data set, Performing weighing and screening processing on the differential data set to obtain weighed data; Adjacent link comparison judgment processing is performed on the weighted data to obtain an abnormality analysis result.

2. The abnormal state analysis method according to claim 1, characterized in that: The performing differential value processing on the sample data set to obtain a differential data set includes: Performing value screening processing on the sampled data set according to the sampling period to obtain a first data set; Perform differential averaging processing on the first data set to obtain the differential data set.

3. The abnormal state analysis method according to claim 1, characterized in that: The differential data set includes a plurality of differential average data, and the weighing and screening process is performed on the differential data set to obtain the weighed data, including: Performing a first difference evaluation process on the differential data set to obtain a second data set, wherein the second data set is configured to represent an absolute value of a difference between differential average data corresponding to different sampling instruments at the same sampling time point; According to the second data set, optimal weighing processing is performed on the differential data set to obtain the weighed data.

4. The abnormal state analysis method according to claim 3, characterized in that: The step of performing optimal trade-off processing on the differential data set according to the second data set to obtain the trade-off data includes: performing sorting and comparison processing on the second data set to obtain a comparison result; According to the comparison result, performing a primary screening process on the differential data set to obtain a third data set; An optimal screening process is performed on the third data set to obtain the weighted data.

5. The abnormal state analysis method according to claim 4, characterized in that: The performing optimal screening on the third data set to obtain the weighted data includes: The third data set is optimally screened according to a preset sampling condition corresponding to the sampling type to obtain the weighted data.

6. The abnormal state analysis method according to claim 1, characterized in that: The performing adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result includes: performing adjacent extraction processing on the differential data set according to the weighted data to obtain a fourth data set; According to the fourth data set, difference analysis processing is performed on the weighted data to obtain the abnormality analysis result.

7. The abnormal state analysis method according to claim 6, characterized in that: The differential data set includes a plurality of differential average data, and performing differential analysis on the weighted data based on the fourth data set to obtain the abnormality analysis result includes: performing a second difference evaluation process on the fourth data set to obtain a fifth data set, wherein the fifth data set is configured to represent absolute values ​​of differences between differential average data corresponding to the same sampling instrument at adjacent sampling time points; According to the fifth data set, a comparison screening process is performed on the weighted data to obtain the abnormality analysis result.

8. The abnormal state analysis method according to claim 7, characterized in that: The step of performing a comparative screening process on the weighted data based on the fifth data set to obtain the abnormality analysis result includes: Get the preset abnormal threshold and warning times; Performing a month-on-month threshold processing on the fifth data set to obtain a month-on-month value; According to the month-on-month value, the abnormality threshold and the number of warnings, threshold judgment processing is performed on the weighed data to obtain the abnormality analysis result.

9. The method according to claim 8, characterized in that The performing threshold judgment processing on the weighted data according to the month-on-month value, the abnormality threshold, and the number of warnings to obtain the abnormality analysis result includes: Obtaining a calculated intermediate value based on the difference between the month-on-month value and the weighted data; Obtaining the number of abnormalities according to a comparison result between the calculated intermediate value and the abnormality threshold; The abnormality analysis result is obtained according to the number of abnormalities and the number of warnings.

10. An abnormal state analysis device, characterized in that: Applied to the converter valve cooling system, the analysis device includes: an acquisition module configured to acquire a sampling data set, wherein the sampling data set is configured to represent a plurality of sampling instruments in the valve cooling system, and is a collection of sampling data obtained by taking values ​​multiple times within a sampling period of the instruments, wherein the sampling time points and sampling types of the plurality of sampling instruments are the same; A difference module is configured to perform difference value processing on the sample data set to obtain a difference data set, a screening module configured to perform a weighing screening process on the differential data set to obtain weighed data; The processing module is configured to perform adjacent ring comparison judgment processing on the weighted data to obtain an abnormality analysis result.

11. An electronic device, characterized in that: include: at least one processor; at least one memory configured to store at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 9.

12. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is configured to implement the method according to any one of claims 1 to 9 when executed by the processor.

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