A data processing method for intelligently controlling a marine processing system

Abstract

The present application relates to the technical field of control adjustment, and particularly relates to a data processing method for intelligently controlling a marine product processing system, which comprises the following steps: obtaining dimension data of different preset dimensions of different preset links of the marine product processing system in a current time period, and equally dividing the current time period; determining an abnormal state feature value of each preset dimension under each target sub-period; screening out a target abnormal dimension of each preset link under each target sub-period; determining an abnormal feature value of each preset link under each target sub-period; screening out a target abnormal link corresponding to each target sub-period; determining a comprehensive abnormal representation quantity corresponding to each preset link; and determining a current operation condition evaluation value corresponding to the marine product processing system. Through analysis of the dimension data in the current time period, the present application realizes evaluation of the operation condition of the marine product processing system, and improves the rationality of the operation condition evaluation of the marine product processing system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control adjustment, and in particular to a data processing method for intelligently controlling a sea cucumber processing system. BACKGROUND

[0002] As a kind of echinoderm living in the sea, sea cucumber is not only an important part of marine ecosystem, but also a kind of nutritious food that people like very much. However, fresh sea cucumber is not easy to preserve, and is usually processed into dried sea cucumber, instant sea cucumber, sea cucumber peptide and other products by a sea cucumber processing system. In order to ensure the quality of sea cucumber processing, the running condition of the sea cucumber processing system needs to be monitored during the processing, so as to monitor abnormal running condition in time. At present, when the running condition is evaluated by a data processing method, the method usually adopted is as follows: the monitoring data at the current time is collected by a sensor, and the running condition is evaluated based on the abnormal condition of the monitoring data collected at the current time.

[0003] However, when the running condition of the sea cucumber processing system is evaluated based on the abnormal condition of the monitoring data collected at the current time, the following technical problems often exist:

[0004] In actual situation, the sensor may be affected by instantaneous electromagnetic interference, vibration or occasional error in reading transmission, resulting in serious distortion of a certain data point. Therefore, the monitoring data collected at the current time may not represent the real condition of the sea cucumber processing system, thereby leading to poor rationality of evaluating the running condition of the sea cucumber processing system. SUMMARY

[0005] In order to solve the technical problem of poor rationality of evaluating the running condition of the sea cucumber processing system, the present application provides a data processing method for intelligently controlling a sea cucumber processing system.

[0006] In the first aspect, the present application provides a data processing method for intelligently controlling a sea cucumber processing system, which comprises:

[0007] obtaining dimension data of different preset dimensions of different preset links of the sea cucumber processing system in a current time period, and equally dividing the current time period to obtain a target sub-period;

[0008] determining an abnormal situation characteristic value of each preset dimension in each target sub-period according to the distribution of the dimension data of each preset dimension in each target sub-period;

[0009] selecting a target abnormal dimension of each preset link in each target sub-period from all preset dimensions according to the abnormal situation characteristic value;

[0010] determine an abnormal feature value of each preset link under each target sub-segment according to dimension data correlation between the target abnormal dimension under each target sub-segment and other target abnormal dimensions within the target sub-segment;

[0011] screen a target abnormal link corresponding to each target sub-segment from all preset links according to the abnormal feature value;

[0012] determine a comprehensive abnormal representation quantity corresponding to each preset link according to abnormal distribution of the preset dimension under different target sub-segments of each preset link;

[0013] determine a current operation condition evaluation value corresponding to the marine working system according to the comprehensive abnormal representation quantity corresponding to all preset links.

[0014] In a possible implementation manner of the above first aspect, the determining of the abnormal trend feature value of each preset dimension under each target sub-segment according to the distribution of the dimension data of each preset dimension within each target sub-segment comprises:

[0015] determine any one preset link as a marker link, and determine any one preset dimension of the marker link as a marker dimension, and determine any one target sub-segment as a marker sub-segment;

[0016] obtain a theoretical standard value corresponding to the marker dimension as a marker theoretical standard value;

[0017] obtain dimension data of the marker dimension within a historical operation time period of the marine working system;

[0018] determine a mean value of all dimension data of the marker dimension obtained within the historical operation time period as a marker actual standard value;

[0019] construct a data reference range of the marker dimension according to the marker theoretical standard value and the marker actual standard value;

[0020] if the dimension data of the marker dimension does not belong to the data reference range, determine the dimension data of the marker dimension as error data;

[0021] determine an initial deviation factor corresponding to each error data of the marker dimension according to a condition that each error data of the marker dimension exceeds the data reference range;

[0022] determine the abnormal trend feature value of the marker dimension under the marker sub-segment according to a number of error data of the marker dimension within the marker sub-segment, an accumulated value of the initial deviation factors corresponding to all error data of the marker dimension within the marker sub-segment, and a number of extreme values in a fitting curve formed by all error data of the marker dimension within the marker sub-segment.

[0023] With reference to the first aspect, in a possible implementation manner, the constructing the data reference range in the marking dimension according to the marking theoretical standard value and the marking actual standard value comprises:

[0024] determining an absolute value of a difference between the marking theoretical standard value and the marking actual standard value as an allowable fluctuation error in the marking dimension;

[0025] taking the marking theoretical standard value as a middle value and taking the allowable fluctuation error as a step size to construct the data reference range in the marking dimension.

[0026] With reference to the first aspect, in a possible implementation manner, the determining the initial deviation factor corresponding to each error data in the marking dimension according to a case that each error data in the marking dimension exceeds the data reference range comprises:

[0027] determining any one error data in the marking dimension as a marking error data;

[0028] if the marking error data is greater than a maximum value of the data reference range, determining a difference between the marking error data and the maximum value of the data reference range as the initial deviation factor corresponding to the marking error data;

[0029] if the marking error data is less than a minimum value of the data reference range, determining a difference between the minimum value of the data reference range and the marking error data as the initial deviation factor corresponding to the marking error data.

[0030] With reference to the first aspect, in a possible implementation manner, the screening the target abnormal dimension of each target subsegment of each preset link from all preset dimensions according to the abnormal state feature value comprises:

[0031] determining any one preset link as a marking link, determining any one preset dimension of the marking link as a marking dimension, and determining any one target subsegment as a marking subsegment;

[0032] if an abnormal state feature value of the marking dimension in the marking subsegment is greater than a preset dimension abnormal threshold value, determining the marking dimension as the target abnormal dimension of the marking link in the marking subsegment.

[0033] With reference to the first aspect, in a possible implementation manner, the determining the abnormal feature value of each preset link in each target subsegment according to a dimension data correlation case between the target abnormal dimension of each target subsegment in the target subsegment and other target abnormal dimensions in the target subsegment comprises:

[0034] Based on the correlation of dimensional data between each target anomaly dimension and other target anomaly dimensions within each target sub-segment, determine the initial anomaly feature factor corresponding to each target anomaly dimension in each target sub-segment for each preset step.

[0035] Based on the initial abnormal feature factor corresponding to each target abnormal dimension under each target sub-segment of each preset step, and the abnormal situation feature value of the target abnormal dimension under the target sub-segment, the target abnormal feature factor corresponding to each target abnormal dimension under each target sub-segment of each preset step is determined.

[0036] Based on the mean of the target anomaly feature factors corresponding to all target anomaly dimensions under each target sub-segment for each preset step, and the number of target anomaly dimensions under each target sub-segment for each preset step, the anomaly feature value of each preset step under each target sub-segment is determined.

[0037] In conjunction with the first aspect above, in one possible implementation, determining the initial anomaly feature factor corresponding to each target anomaly dimension in each target sub-segment based on the dimensional data correlation between each target anomaly dimension and other target anomaly dimensions within that target sub-segment includes:

[0038] Designate any preset step as a marked step, and designate any target sub-segment as a marked sub-segment;

[0039] The marking link determines any one of the target anomaly dimensions under the marking sub-segment as the marked anomaly dimension, and the marking link determines each target anomaly dimension under the marking sub-segment other than the marked anomaly dimension as the reference anomaly dimension;

[0040] The entire dimension data of the marked anomaly dimension within the marked sub-segment are used to form the marked dimension data sequence of the marked anomaly dimension under the marked sub-segment.

[0041] All dimension data within the labeled segment for each reference anomaly dimension are used to form the reference dimension data sequence for each reference anomaly dimension under the labeled segment.

[0042] The absolute value of the Pearson correlation coefficient between the labeled dimension data sequence and each reference dimension data sequence is determined as the reference correlation index;

[0043] The mean of all relevant reference indicators is used as the initial abnormal feature factor corresponding to the marked abnormal dimension under the marked sub-segment in the marked step.

[0044] In a possible implementation manner of the first aspect, the target abnormal link corresponding to each target sub-period is determined according to the abnormal feature value of each preset link in each target sub-period, and the target abnormal link corresponding to each target sub-period is determined according to the abnormal feature value of each preset link in each target sub-period.

[0045] Any one of the preset links is determined as a marker link, and any one of the target sub-periods is determined as a marker sub-period.

[0046] If the abnormal feature value of the marker link under the marker sub-period is greater than the preset link abnormal threshold, the marker link is determined as the target abnormal link corresponding to the marker sub-period.

[0047] In a possible implementation manner of the first aspect, the comprehensive abnormal characteristic quantity corresponding to each preset link is determined according to the abnormal distribution of each preset link in the preset dimension under different target sub-periods.

[0048] If the abnormal trend feature value of the preset dimension under the target sub-period is greater than the preset sub-period abnormal threshold, the target sub-period is determined as the dimension abnormal sub-period corresponding to the preset dimension.

[0049] The local abnormal characteristic quantity corresponding to each preset dimension is determined according to the mean value of the abnormal trend feature value of each preset dimension under all dimension abnormal sub-periods corresponding to the preset dimension, the number of the dimension abnormal sub-periods corresponding to each preset dimension, and the time interval between the first dimension abnormal sub-period and the last dimension abnormal sub-period corresponding to each preset dimension.

[0050] If the abnormal feature value of the preset link under the target sub-period is greater than the preset sub-period abnormal threshold, the target sub-period is determined as the link abnormal sub-period corresponding to the preset link.

[0051] The set composed of all dimension abnormal sub-periods corresponding to each preset dimension is determined as the dimension abnormal sub-period set corresponding to each preset dimension.

[0052] The comprehensive abnormal characteristic quantity corresponding to each preset link is determined according to the mean value of the local abnormal characteristic quantity corresponding to all preset dimensions of each preset link, the number of the link abnormal sub-periods corresponding to each preset link, and the number of elements in the intersection of the dimension abnormal sub-period sets corresponding to all preset dimensions of each preset link.

[0053] In a possible implementation manner of the first aspect, the current running state evaluation value corresponding to the marine working system is determined according to the comprehensive abnormal characteristic quantity corresponding to all preset links.

[0054] Each dimension abnormal sub-period in the intersection of the dimension abnormal sub-period sets corresponding to all preset dimensions of all preset links is determined as an overall abnormal sub-period.

[0055] The current operation condition evaluation value of the marine working system is determined according to the mean value of the comprehensive abnormality characteristic quantity corresponding to all preset links, the number of elements in the intersection of the dimension abnormality sub-segment set corresponding to all preset dimensions of all preset links, and the mean value of the abnormality trend characteristic value of all preset dimensions of all preset links under all overall abnormality sub-segments.

[0056] In a second aspect, the present application provides a data processing device for intelligently controlling a marine working system, the device comprising:

[0057] The acquisition and equalization module is configured to acquire dimension data of different preset dimensions of different preset links of the marine working system in a current time period, and equalize the current time period to obtain target sub-segments.

[0058] The abnormality trend characteristic value determination module is configured to determine an abnormality trend characteristic value of each preset dimension under each target sub-segment according to the distribution of the dimension data of each preset dimension in each target sub-segment.

[0059] The abnormal dimension screening module is configured to screen out a target abnormal dimension of each preset link under each target sub-segment from all preset dimensions according to the abnormality trend characteristic value.

[0060] The abnormality trend characteristic value determination module is configured to determine an abnormality trend characteristic value of each preset dimension under each target sub-segment according to the distribution of the dimension data of each preset dimension in each target sub-segment.

[0061] The abnormal link screening module is configured to screen out a target abnormal link corresponding to each target sub-segment from all preset links according to the abnormality trend characteristic value.

[0062] The comprehensive abnormality characteristic quantity determination module is configured to determine a comprehensive abnormality characteristic quantity corresponding to each preset link according to the abnormality distribution of the preset dimensions of each preset link under different target sub-segments.

[0063] The current operation condition evaluation value determination module is configured to determine a current operation condition evaluation value of the marine working system corresponding to all preset links according to the comprehensive abnormality characteristic quantity corresponding to all preset links.

[0064] In a third aspect, a server is provided, comprising a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0065] In a fourth aspect, a computer program product is provided, which comprises computer program codes, when the computer program codes are run on a computer, the computer is caused to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0066] In a fifth aspect, a computer readable storage medium is provided, which stores computer program codes, when the computer program codes are run on a computer, the computer is caused to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0067] The present application has the following beneficial effects:

[0068] The data processing method for intelligently controlling the marine working system provided by the present application realizes the evaluation of the running state of the marine working system by analyzing the different dimension data in the current time period, solves the technical problem that the rationality of evaluating the running state of the marine working system is poor, and improves the rationality of evaluating the running state of the marine working system. Specifically, the dimension data in the current time period under different preset dimensions obtained by the present application can represent the real running state of the marine working system to a certain extent, which weakens the influence of accidental data anomalies to a certain extent, and the present application comprehensively considers multiple indexes related to the running state, such as abnormal trend characteristic values, abnormal characteristic values and comprehensive abnormal representation quantities, so as to realize the quantification of the current running state evaluation value and improve the rationality of evaluating the running state of the marine working system. BRIEF DESCRIPTION OF DRAWINGS

[0069] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0070] Figure 1 The flow chart of the data processing method for intelligently controlling the marine working system of the present application;

[0071] Figure 2 The composition structure schematic diagram of the data processing device for intelligently controlling the marine working system of the present application;

[0072] Figure 3 The structure schematic diagram of the computer device of the present application. DETAILED DESCRIPTION

[0073] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the specific implementation, structure, features and effects of the technical solutions proposed according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0074] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0075] Reference Figure 1 , shows the flow of some embodiments of a data processing method of an intelligent control sea cucumber processing system. The data processing method of the intelligent control sea cucumber processing system comprises the following steps:

[0076] Step S1, obtaining dimension data of different preset dimensions of different preset links of the sea cucumber processing system in the current time period, and equally dividing the current time period to obtain a target sub-period.

[0077] The sea cucumber processing system can be a system for processing sea cucumbers, which can include multiple links. The preset link can be a link included in the sea cucumber processing system. For example, the preset link can be, but is not limited to, a cooking link, a cooling link and a sterilization link. The number of preset links can be pre-set, which can be 3. The preset dimension can be a pre-set monitoring dimension in the different preset links related to the operation of the sea cucumber processing system. Each preset link can include multiple preset dimensions. The number of preset dimensions included in different preset links can be different, and the number of preset dimensions included in the preset link can be pre-set. For example, the preset dimensions of the cooking link can be, but are not limited to, the cooking temperature dimension, the steam pressure dimension and the cooking voltage dimension. The dimension data can be the normalized value of the preset dimension value. For example, the dimension data under the cooking temperature dimension can be the normalized value of the cooking temperature. The current time period can be a time period ending at the current time, and the corresponding duration can be 2 hours. The target sub-period can be a sub-time period obtained by equally dividing the current time period. For example, the duration corresponding to the target sub-period can be 5 minutes.

[0078] As an example, taking the cooking temperature dimension of the cooking link as an example, in the current time period, the cooking temperature can be collected in real time by the temperature sensor installed in the cooking pot, and the normalized cooking temperature is recorded as the dimension data under the cooking temperature dimension of the cooking link.

[0079] Step S2, according to the distribution of the dimension data of each preset dimension in each target sub-section, determine the abnormal situation characteristic value of each preset dimension in each target sub-section.

[0080] It should be noted that in the processing of sea cucumber, each link has its process standard, that is, each parameter of each link has a certain standard, but in actual operation, it is often inevitably disturbed by various factors, such as changes in environmental temperature and humidity, effects of equipment wear and tear, equipment failure, human major errors, etc. Among them, some factors often cause some small or accidental parameter fluctuations, which can be handled by the system itself or conventional fine-tuning measures, and will not cause substantial damage to the final quality of the product. Some large amplitude parameter fluctuations often mean that there are more serious problems, which will have a greater impact on product quality, therefore, it is often necessary to analyze the fluctuation of each dimension parameter of each link.

[0081] As an example, the present step can include the following steps:

[0082] Firstly, any one of the preset links is determined as a marked link, and any one of the preset dimensions of the above marked link is determined as a marked dimension, and any one of the target sub-sections is determined as a marked sub-section.

[0083] Secondly, the theoretical standard value corresponding to the above marked dimension is obtained as a marked theoretical standard value.

[0084] Among them, the theoretical standard value corresponding to the marked dimension can be the value required on the marked dimension when the sea cucumber processing system is running normally.

[0085] For example, the better cooking temperature range of the sea cucumber processing system when running normally is 95-100℃, and the middle value of 95-100℃ is 97.5, so the theoretical standard value corresponding to the cooking temperature dimension can be 97.5.

[0086] Thirdly, the dimension data of the above marked dimension in the historical running time period of the sea cucumber processing system is obtained.

[0087] Among them, the historical running time period can be a time period in which the sea cucumber processing system has been running normally in the past, and the corresponding time length can be 1 month.

[0088] Fourthly, the mean value of all dimension data of the above marked dimension obtained in the historical running time period is determined as a marked actual standard value.

[0089] Fifthly, the data reference range under the above marked dimension can be constructed according to the above marked theoretical standard value and the above marked actual standard value, which can include the following sub-steps:

[0090] A first sub-step, determining the absolute value of the difference between the above-mentioned label theoretical standard value and the above-mentioned label actual standard value as the allowable fluctuation error under the above-mentioned label dimension.

[0091] A second sub-step, taking the above-mentioned label theoretical standard value as the intermediate value and the above-mentioned allowable fluctuation error as the step size to construct the data reference range under the above-mentioned label dimension.

[0092] For example, the data reference range under the label dimension can be wherein A is the label theoretical standard value, is the allowable fluctuation error under the label dimension.

[0093] A sixth step, if the dimension data under the above-mentioned label dimension does not belong to the above-mentioned data reference range, determining the dimension data under the above-mentioned label dimension as error data.

[0094] A seventh step, according to the situation that each error data under the above-mentioned label dimension exceeds the data reference range, determining the initial deviation factor corresponding to each error data under the above-mentioned label dimension can include the following sub-steps:

[0095] A first sub-step, determining any one error data under the above-mentioned label dimension as label error data.

[0096] A second sub-step, if the above-mentioned label error data is greater than the maximum value of the above-mentioned data reference range, determining the difference between the above-mentioned label error data and the maximum value of the above-mentioned data reference range as the initial deviation factor corresponding to the above-mentioned label error data.

[0097] A third sub-step, if the above-mentioned label error data is less than the minimum value of the above-mentioned data reference range, determining the difference between the minimum value of the above-mentioned data reference range and the above-mentioned label error data as the initial deviation factor corresponding to the above-mentioned label error data.

[0098] An eighth step, according to the number of error data under the above-mentioned label dimension in the above-mentioned label sub-section, the cumulative value of the initial deviation factors corresponding to all error data under the above-mentioned label dimension in the above-mentioned label sub-section, and the number of extreme values in the fitting curve formed by all error data under the above-mentioned label dimension in the above-mentioned label sub-section, determining the abnormal state trend characteristic value under the above-mentioned label dimension in the above-mentioned label sub-section.

[0099] Wherein, the method for obtaining the fitting curve formed by all error data under the label dimension in the label sub-section can be: taking time as the horizontal coordinate and the error data under the label dimension in the label sub-section as the vertical coordinate to construct the fitting curve, which is the fitting curve formed by all error data under the label dimension in the label sub-section.

[0100] For example, the formula for determining the abnormal state trend characteristic value under the label dimension in the label sub-section can be:

[0101] ;

[0102] B is the abnormal situation characteristic value of the marking dimension under the marking sub-section. is a normalization function. is the cumulative value of the initial deviation factors corresponding to all error data of the marking dimension within the marking sub-section. b is the number of error data of the marking dimension within the marking sub-section. c is the number of maximum values in the fitting curve formed by all error data of the marking dimension within the marking sub-section.

[0103] It should be noted that, when is larger, it often means that the error data of the marking dimension within the marking sub-section exceeds the standard to a greater extent, and often means that the abnormality is larger. When b is larger, it often means that there are more error data of the marking dimension within the marking sub-section, and often means that there are more abnormal data of the marking dimension within the marking sub-section. When c is larger, it often means that the fluctuation of the error data is larger. For example, a large fluctuation in the cooking water temperature often causes the sea cucumber to experience a sharp change in temperature in a short time, resulting in uneven internal and external cooking, such as the surface of the sea cucumber being rapidly cooked or even becoming tough, while the inside is not yet fully cooked; the subsequent low temperature interrupts the cooking process, which not only seriously affects the taste and texture of the sea cucumber, but also may cause some areas to be ineffective in killing microorganisms, increasing the risk of food safety. Therefore, when B is larger, it often means that there is more likely to be an abnormality in the marking dimension within the marking sub-section, and it is more likely to affect the running state of the system. For example, when the abnormality of the cooking water temperature is large, it often causes the processing effect of the cooking link to deviate from the expected standard, which may result in poor product quality consistency, and the temperature abnormality of the cooking link may affect the stability of the subsequent cooling, sterilization and other links due to changes in the state of the raw materials, thereby reducing the stability of the entire system.

[0104] Step S3: According to the abnormal situation characteristic value, the target abnormal dimension of each target sub-section under each preset link is screened from all preset dimensions.

[0105] As an example, the present step can include the following steps:

[0106] Firstly, any one preset link is determined as a marking link, and any one preset dimension of the above marking link is determined as a marking dimension, and any one target sub-section is determined as a marking sub-section.

[0107] Secondly, if the abnormal situation characteristic value of the above marking dimension under the above marking sub-section is greater than the preset dimension abnormal threshold value, the above marking dimension is determined as the target abnormal dimension of the above marking sub-section under the above marking link.

[0108] The preset dimension anomaly threshold can be a preset threshold, which can be 0.6.

[0109] Step S4, according to the dimension data correlation between the target abnormal dimension under each target sub-section and other target abnormal dimensions in the target sub-section, determine the abnormal characteristic value of each preset link under each target sub-section.

[0110] As an example, the present step can include the following steps:

[0111] First, according to the dimension data correlation between each target abnormal dimension under each target sub-section and other target abnormal dimensions in the target sub-section, determine the initial abnormal characteristic factor corresponding to each target abnormal dimension of each preset link under each target sub-section.

[0112] It should be noted that in the marine processing process, since multiple links are processed at the same time, there may be multiple types of data of multiple links that are abnormal in the target sub-section, and the links of the marine processing process are closely connected, the output of the previous link is often the input of the next link, and part of the resources and equipment are often shared by multiple links, Therefore, there is often a certain correlation between different dimensions.

[0113] For example, determining the initial abnormal characteristic factor corresponding to each target abnormal dimension of each preset link under each target sub-section can include the following sub-steps:

[0114] First sub-step, determine any one preset link as a marker link, and determine any one target sub-section as a marker sub-section.

[0115] Second sub-step, determine any one target abnormal dimension of the marker link in the marker sub-section as a marker abnormal dimension, and determine each target abnormal dimension of the marker link in the marker sub-section except the marker abnormal dimension as a reference abnormal dimension.

[0116] Third sub-step, all dimension data of the marker abnormal dimension in the marker sub-section form a marker dimension data sequence of the marker abnormal dimension in the marker sub-section.

[0117] The marker dimension data sequence can be a time sequence.

[0118] Fourth sub-step, all dimension data of each reference abnormal dimension in the marker sub-section form a reference dimension data sequence of each reference abnormal dimension in the marker sub-section.

[0119] The reference dimension data sequence can be a time sequence.

[0120] In a fifth sub-step, an absolute value of a Pearson correlation coefficient between the marked dimension data sequence and each reference dimension data sequence is determined as a reference correlation index.

[0121] In a sixth sub-step, a mean value of all the reference correlation indexes is determined as an initial abnormal characteristic factor corresponding to the marked abnormal dimension under the marked sub-section of the marked section.

[0122] It should be noted that the greater the initial abnormal characteristic factor corresponding to the marked abnormal dimension, the more likely the marked abnormal dimension is associated with other dimensions in the marked sub-section, the more likely there is a chain effect, and the more likely the marked abnormal dimension causes abnormal defects of other dimensions in the marked sub-section.

[0123] In a second step, according to the initial abnormal characteristic factor corresponding to each target abnormal dimension of each preset section under each target sub-section and the abnormal trend characteristic value of the target abnormal dimension under the target sub-section, a target abnormal characteristic factor corresponding to each target abnormal dimension of each preset section under each target sub-section is determined.

[0124] For example, a formula for determining the target abnormal characteristic factor corresponding to the target abnormal dimension of the preset section under the target sub-section can be:

[0125] ;

[0126] wherein, is the target abnormal characteristic factor corresponding to the a-th target abnormal dimension of the i-th preset section under the j-th target sub-section. i is the serial number of the preset section. j is the serial number of the target sub-section. a is the serial number of the target abnormal dimension of the i-th preset section under the j-th target sub-section. is the abnormal trend characteristic value of the a-th target abnormal dimension under the j-th target sub-section under the j-th target sub-section. is the initial abnormal characteristic factor corresponding to the a-th target abnormal dimension of the i-th preset section under the j-th target sub-section.

[0127] It should be noted that, the greater the target abnormal characteristic factor, the more likely the a-th target abnormal dimension is associated with other dimensions in the j-th target sub-section, the more likely there is a chain effect, and the more likely the a-th target abnormal dimension causes abnormal defects of other dimensions in the j-th target sub-section. When the greater the target abnormal characteristic factor, the more likely the a-th target abnormal dimension has an abnormality in the j-th target sub-section, and the more likely it affects the running state of the system. Therefore, when The greater the abnormal characteristic value is, the greater the abnormal influence caused by the i-th preset link in the j-th target sub-section is, and the more likely the system running state is affected.

[0128] In a third step, according to the mean value of the target abnormal characteristic factor corresponding to all target abnormal dimensions of each preset link in each target sub-section and the number of target abnormal dimensions of each preset link in each target sub-section, the abnormal characteristic value of each preset link in each target sub-section is determined.

[0129] For example, the formula for determining the abnormal characteristic value of the preset link in the target sub-section can be:

[0130] ;

[0131] wherein, is the abnormal characteristic value of the i-th preset link in the j-th target sub-section. i is the serial number of the preset link. j is the serial number of the target sub-section. is a normalization function. is the number of target abnormal dimensions of the i-th preset link in the j-th target sub-section. is the number of preset dimensions of the i-th preset link. is the mean value of the target abnormal characteristic factor corresponding to all target abnormal dimensions of the i-th preset link in the j-th target sub-section.

[0132] It should be noted that when The greater the abnormal characteristic value is, the greater the abnormal influence caused by the i-th preset link in the j-th target sub-section is, and the more likely the system running state is affected. When The greater the abnormal characteristic value is, the more abnormal dimensions of the i-th preset link in the j-th target sub-section occur, and the more likely the abnormal situation of the i-th preset link in the j-th target sub-section exists. Therefore, when The greater the abnormal characteristic value is, the greater the abnormal influence caused by the i-th preset link in the j-th target sub-section is, and the more likely the system running state is affected.

[0133] In step S5, according to the abnormal characteristic value, the target abnormal link corresponding to each target sub-section is screened from all preset links.

[0134] As an example, this step can include the following steps:

[0135] In a first step, any one preset link is determined as a marker link, and any one target sub-section is determined as a marker sub-section.

[0136] Secondly, if the abnormal characteristic value of the marked link under the marked sub-segment is greater than the preset link abnormal threshold, the marked link is determined as the target abnormal link corresponding to the marked sub-segment.

[0137] The preset link abnormal threshold can be a preset threshold, which can be 0.7.

[0138] Step S6: determining a comprehensive abnormality representation of each preset link according to the abnormal distribution of the preset dimension of each preset link under different target sub-segments.

[0139] As an example, the present step can include the following steps:

[0140] Firstly, if the abnormal situation characteristic value of the preset dimension under the target sub-segment is greater than the preset sub-segment abnormal threshold, the target sub-segment is determined as the dimension abnormal sub-segment corresponding to the preset dimension.

[0141] The preset sub-segment abnormal threshold can be a preset threshold, which can be 0.6.

[0142] Secondly, according to the mean value of the abnormal situation characteristic value of each preset dimension under all the dimension abnormal sub-segments corresponding to the preset dimension, the number of the dimension abnormal sub-segments corresponding to each preset dimension, and the time interval between the first dimension abnormal sub-segment and the last dimension abnormal sub-segment corresponding to each preset dimension, a local abnormality representation corresponding to each preset dimension is determined.

[0143] For example, the formula for determining the local abnormality representation corresponding to the preset dimension can be:

[0144] ;

[0145] H is the local abnormality representation corresponding to the preset dimension. Y is the mean value of the abnormal situation characteristic value of the preset dimension under all the dimension abnormal sub-segments corresponding to the preset dimension. n is the number of the dimension abnormal sub-segments corresponding to the preset dimension. is the time interval between the first dimension abnormal sub-segment and the last dimension abnormal sub-segment corresponding to the preset dimension.

[0146] It should be noted that the greater H is, the more time periods of abnormality occur on the preset dimension, the more obvious the abnormal situation is, and the larger the distribution range can be, which means that the abnormality of the preset dimension in the current time period is more obvious.

[0147] Thirdly, if the abnormal characteristic value of the preset link under the target sub-segment is greater than the preset sub-segment abnormal threshold, the target sub-segment is determined as the link abnormal sub-segment corresponding to the preset link.

[0148] Fourthly, a set of dimension abnormal sub-segments corresponding to each preset dimension is determined.

[0149] Fifthly, a comprehensive abnormal characteristic quantity corresponding to each preset link is determined according to the mean value of the local abnormal characteristic quantity corresponding to all preset dimensions of each preset link, the number of link abnormal sub-segments corresponding to each preset link, and the number of elements in the intersection of the set of dimension abnormal sub-segments corresponding to all preset dimensions of each preset link.

[0150] For example, the formula corresponding to the determination of the comprehensive abnormal characteristic quantity corresponding to the preset link can be:

[0151] ;

[0152] Wherein, is the comprehensive abnormal characteristic quantity corresponding to the ith preset link. i is the serial number of the preset link. is the number of link abnormal sub-segments corresponding to the ith preset link. is the number of elements in the intersection of the set of dimension abnormal sub-segments corresponding to all preset dimensions of the ith preset link. is the mean value of the local abnormal characteristic quantity corresponding to all preset dimensions of the ith preset link.

[0153] It should be noted that when is larger, it often means that there are more abnormal time periods in the ith preset link. When is larger, it often means that there are more time periods in which all dimensions in the ith preset link are abnormal. When is larger, it often means that the abnormality of the preset dimensions of the ith preset link in the current time period is more obvious. Therefore, when is larger, it often means that the number of times of simultaneous abnormality of different dimensions in the ith preset link is more, and the abnormality is larger, which often means that the mutual influence between different data is larger, and the influence on the entire marine working system is also larger.

[0154] Step S7, according to the comprehensive abnormal characteristic quantity corresponding to all preset links, determine the current running state evaluation value corresponding to the marine working system.

[0155] As an example, this step can include the following steps:

[0156] Firstly, each dimension abnormal sub-segment in the intersection of the set of dimension abnormal sub-segments corresponding to all preset dimensions of all preset links is determined as an overall abnormal sub-segment.

[0157] Secondly, according to the mean of the comprehensive abnormal characteristic quantity corresponding to all preset links, the number of elements in the intersection of the dimension abnormal sub-segment set corresponding to all preset dimensions of all preset links, and the mean of the abnormal state feature value of all preset dimensions of all preset links under all overall abnormal sub-segments, a current running condition evaluation value corresponding to the marine working system is determined.

[0158] For example, the formula corresponding to the determination of the current running condition evaluation value of the marine working system can be:

[0159] ;

[0160] Wherein, F is the current running condition evaluation value corresponding to the marine working system. is a normalization function. D is the mean of the comprehensive abnormal characteristic quantity corresponding to all preset links. S is the number of elements in the intersection of the dimension abnormal sub-segment set corresponding to all preset dimensions of all preset links, that is, the number of overall abnormal sub-segments. m is the mean of the abnormal state feature value of all preset dimensions of all preset links under all overall abnormal sub-segments.

[0161] It should be noted that when D is larger, it often means that the number of simultaneous abnormality of different dimensions within the preset link is larger, and the abnormality is larger, which often means that the mutual influence between different data is larger, and the influence on the entire marine working system is also larger. When S is larger, it often means that the time period of simultaneous abnormality of all dimensions within the entire marine working system is larger. When m is larger, it often means that the entire marine working system is more likely to exist abnormality. Therefore, when F is larger, it often means that the running of the marine working system at the current time is more likely to have abnormality, and it is more necessary to remind the staff to perform maintenance inspection.

[0162] Reference Figure 2 Based on the same inventive concept as the above method embodiment, the present application provides a data processing device for intelligently controlling a marine working system, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the above computer program is executed by the processor, the steps of the data processing method for intelligently controlling the marine working system are realized, which can specifically include:

[0163] The acquisition and equalization module 201 is used for acquiring the dimension data of different preset dimensions of different preset links of the marine working system within the current time period, and equally dividing the current time period to obtain a target sub-segment;

[0164] The abnormal state feature value determination module 202 is used for determining the abnormal state feature value of each preset dimension under each target sub-segment according to the distribution of the dimension data of each preset dimension within each target sub-segment;

[0165] The abnormal dimension screening module 203 is configured to screen, according to the abnormal state feature value, a target abnormal dimension of each target sub-section under each preset link from all preset dimensions;

[0166] The abnormal feature value determination module 204 is configured to determine, according to a dimension data correlation condition between the target abnormal dimension under each target sub-section and other target abnormal dimensions in the target sub-section, an abnormal feature value of each preset link under each target sub-section.

[0167] The abnormal link screening module 205 is configured to screen, according to the abnormal feature value, a target abnormal link corresponding to each target sub-section from all preset links.

[0168] The comprehensive abnormal representation quantity determination module 206 is configured to determine a comprehensive abnormal representation quantity corresponding to each preset link according to abnormal distribution conditions of the preset dimensions of each preset link under different target sub-sections.

[0169] The current operation condition evaluation value determination module 207 is configured to determine a current operation condition evaluation value corresponding to the marine working system according to the comprehensive abnormal representation quantity corresponding to all preset links.

[0170] Figure 3 is a structural schematic diagram of a computer device provided by an embodiment of the present application. As shown in the example, Figure 3 the computer device 300 includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein the processor 302 executes the computer program 303, so that the computer device can execute any of the data processing methods for intelligently controlling the marine working system introduced above.

[0171] Based on the same inventive concept as the above method embodiment, the present application provides a server including a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and run the executable program code from the memory, so that the device executes any of the data processing methods for intelligently controlling the marine working system described above.

[0172] Based on the same inventive concept as the above method embodiment, the present application provides a computer program product, which includes computer program code. When the computer program code runs on a computer, it makes the computer execute any of the data processing methods for intelligently controlling the marine working system described above.

[0173] Based on the same inventive concept as the above method embodiments, the present application provides a computer readable storage medium storing computer program code, which, when executed on a computer, causes the computer to perform any of the above data processing methods for intelligently controlling a marine working system.

[0174] To sum up, the dimension data of different preset dimensions in the current time period obtained by the present application can represent the real running condition of the marine working system to a certain extent, which weakens the influence of accidental data anomalies to a certain extent, and the present application comprehensively considers multiple indicators related to the running condition, such as abnormal situation characteristic values, abnormal characteristic values, and comprehensive abnormal characteristic values, thereby realizing the quantification of the current running condition evaluation value and improving the rationality of the running condition evaluation of the marine working system.

[0175] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A data processing method for an intelligent control sea cucumber processing system, characterized in that, The method comprises the following steps: acquiring dimension data of different preset dimensions of different preset links of the marine working system in a current time period, and equally dividing the current time period to obtain target sub-periods; determining an abnormal situation characteristic value of each preset dimension in each target sub-period according to a distribution of the dimension data of each preset dimension in each target sub-period; screening a target abnormal dimension of each preset link in each target sub-period from all preset dimensions according to the abnormal situation characteristic value; determining an abnormal characteristic value of each preset link in each target sub-period according to a dimension data correlation between the target abnormal dimension of each target sub-period and other target abnormal dimensions in the target sub-period; screening a target abnormal link corresponding to each target sub-period from all preset links according to the abnormal characteristic value; determining a comprehensive abnormal representation quantity corresponding to each preset link according to an abnormal distribution of the preset dimensions of each preset link in different target sub-periods; determining a current running condition evaluation value corresponding to the marine working system according to the comprehensive abnormal representation quantity corresponding to all preset links; a formula corresponding to the comprehensive abnormal representation quantity of the preset link is: ； wherein, is the integrated abnormality characteristic quantity corresponding to the ith preset link; i is the serial number of the preset link; is the number of link anomaly sub-sections corresponding to the i-th preset link; is the number of elements in the intersection of the dimension anomaly sub-section sets corresponding to all preset dimensions of the i-th preset link; is the mean value of the local anomaly feature quantities corresponding to all preset dimensions of the i-th preset link.

2. The data processing method for intelligently controlling the marine processing system according to claim 1, wherein, The determination of the abnormal situation characteristic value of each preset dimension in each target sub-period comprises: determining any one preset link as a marker link, and determining any one preset dimension of the marker link as a marker dimension, and determining any one target sub-period as a marker sub-period; acquiring a theoretical standard value corresponding to the marker dimension as a marker theoretical standard value; acquiring dimension data of the marker dimension in a historical running time period of the marine working system; determining a mean value of all dimension data of the marker dimension acquired in the historical running time period as a marker actual standard value; constructing a data reference range of the marker dimension according to the marker theoretical standard value and the marker actual standard value; if the dimension data of the marker dimension does not belong to the data reference range, determining the dimension data of the marker dimension as error data; determining an initial deviation factor corresponding to each error data of the marker dimension according to a condition that each error data of the marker dimension exceeds the data reference range; determining an abnormal situation characteristic value of the marker dimension in the marker sub-period according to a number of error data of the marker dimension in the marker sub-period, an accumulated value of the initial deviation factors corresponding to all error data of the marker dimension in the marker sub-period, and a number of extreme values in a fitting curve formed by all error data of the marker dimension in the marker sub-period.

3. The data processing method for intelligently controlling the marine processing system according to claim 2, wherein, The construction of the data reference range of the marker dimension according to the marker theoretical standard value and the marker actual standard value comprises: determining an allowed fluctuation error of the marker dimension as an absolute value of a difference between the marker theoretical standard value and the marker actual standard value; constructing the data reference range of the marker dimension by taking the marker theoretical standard value as a middle value and taking the allowed fluctuation error as a step.

4. The data processing method for intelligently controlling the marine processing system according to claim 2, wherein, The initial deviation factor corresponding to each error data in the marking dimension is determined according to whether each error data in the marking dimension exceeds the data reference range, and the initial deviation factor corresponding to each error data in the marking dimension is determined according to whether each error data in the marking dimension exceeds the data reference range. Any error data in the marking dimension is determined as a marking error data; If the marking error data is greater than the maximum value of the data reference range, the difference between the marking error data and the maximum value of the data reference range is determined as the initial deviation factor corresponding to the marking error data; If the marking error data is less than the minimum value of the data reference range, the difference between the minimum value of the data reference range and the marking error data is determined as the initial deviation factor corresponding to the marking error data.

5. The data processing method for intelligently controlling the marine processing system according to claim 1, wherein, The target abnormal dimension of each target sub-section under each preset link is screened from all preset dimensions according to the abnormal situation characteristic value, and the target abnormal dimension of each target sub-section under each preset link is screened from all preset dimensions according to the abnormal situation characteristic value. Any one preset link is determined as a marking link, and any one preset dimension of the marking link is determined as a marking dimension, and any one target sub-section is determined as a marking sub-section. If the abnormal situation characteristic value of the marking dimension in the marking sub-section is greater than a preset dimension abnormal threshold value, the marking dimension is determined as the target abnormal dimension of the marking link in the marking sub-section.

6. The data processing method for intelligently controlling the marine processing system according to claim 1, wherein, The abnormal characteristic value of each preset link in each target sub-section is determined according to the dimension data correlation between each target abnormal dimension in each target sub-section and other target abnormal dimensions in the target sub-section, and the abnormal characteristic value of each preset link in each target sub-section is determined according to the dimension data correlation between each target abnormal dimension in each target sub-section and other target abnormal dimensions in the target sub-section. The initial abnormal characteristic factor corresponding to each target abnormal dimension of each preset link in each target sub-section is determined according to the dimension data correlation between each target abnormal dimension in each target sub-section and other target abnormal dimensions in the target sub-section. The target abnormal characteristic factor corresponding to each target abnormal dimension of each preset link in each target sub-section is determined according to the initial abnormal characteristic factor corresponding to each target abnormal dimension of each preset link in each target sub-section and the abnormal situation characteristic value of the target abnormal dimension in the target sub-section. The abnormal characteristic value of each preset link in each target sub-section is determined according to the mean value of the target abnormal characteristic factors corresponding to all target abnormal dimensions of each preset link in each target sub-section and the number of target abnormal dimensions of each preset link in each target sub-section.

7. The data processing method for intelligently controlling the marine processing system according to claim 6, wherein, The initial abnormal characteristic factor corresponding to each target abnormal dimension of each preset link in each target sub-section is determined according to the dimension data correlation between each target abnormal dimension in each target sub-section and other target abnormal dimensions in the target sub-section. Any one preset link is determined as a marking link, and any one target sub-section is determined as a marking sub-section. Any one target abnormal dimension of the marking link in the marking sub-section is determined as a marking abnormal dimension, and each target abnormal dimension of the marking link in the marking sub-section except the marking abnormal dimension is determined as a reference abnormal dimension. The all dimension data of the marked abnormal dimension in the marked sub-stage is constructed as a marked dimension data sequence of the marked abnormal dimension under the marked sub-stage; The all dimension data of each reference abnormal dimension in the marked sub-stage is constructed as a reference dimension data sequence of each reference abnormal dimension under the marked sub-stage; The absolute value of the Pearson correlation coefficient between the marked dimension data sequence and each reference dimension data sequence is determined as a reference correlation index; The mean value of all reference correlation indexes is determined as an initial abnormal characteristic factor corresponding to the marked abnormal dimension under the marked sub-stage of the marked link. 8.The data processing method of the intelligent control marine processing system according to claim 1, wherein, The abnormal characteristic value is used for screening a target abnormal link corresponding to each target sub-stage from all preset links, and the screening includes: Any one preset link is determined as a marked link, and any one target sub-stage is determined as a marked sub-stage; If the abnormal characteristic value of the marked link under the marked sub-stage is greater than a preset link abnormal threshold, the marked link is determined as a target abnormal link corresponding to the marked sub-stage.

9. The data processing method for intelligently controlling the marine processing system according to claim 1, wherein, The abnormal distribution of a preset dimension under different target sub-stages of each preset link is used for determining a comprehensive abnormal representation quantity corresponding to each preset link, and the determining includes: If an abnormal trend characteristic value of a preset dimension under a target sub-stage is greater than a preset sub-stage abnormal threshold, the target sub-stage is determined as a dimension abnormal sub-stage corresponding to the preset dimension; The mean value of the abnormal trend characteristic values of each preset dimension under all dimension abnormal sub-stages corresponding to the preset dimension, the number of the dimension abnormal sub-stages corresponding to each preset dimension, and the time interval between the first dimension abnormal sub-stage and the last dimension abnormal sub-stage corresponding to each preset dimension are used for determining a local abnormal representation quantity corresponding to each preset dimension; If an abnormal characteristic value of a preset link under a target sub-stage is greater than a preset sub-stage abnormal threshold, the target sub-stage is determined as a link abnormal sub-stage corresponding to the preset link; A set composed of all dimension abnormal sub-stages corresponding to each preset dimension is determined as a dimension abnormal sub-stage set corresponding to each preset dimension; The mean value of the local abnormal representation quantities of all preset dimensions of each preset link, the number of the link abnormal sub-stages corresponding to each preset link, and the number of elements in the intersection of the dimension abnormal sub-stage sets corresponding to all preset dimensions of each preset link are used for determining a comprehensive abnormal representation quantity corresponding to each preset link.

10. The data processing method for intelligently controlling the marine processing system according to claim 9, wherein, The current operation condition evaluation value corresponding to the marine working system is determined according to the comprehensive abnormal representation quantities of all preset links, and the determining includes: Each dimension abnormal sub-stage in the intersection of the dimension abnormal sub-stage sets corresponding to all preset dimensions of all preset links is determined as an overall abnormal sub-stage; The mean value of the comprehensive abnormal representation quantities of all preset links, the number of elements in the intersection of the dimension abnormal sub-stage sets corresponding to all preset dimensions of all preset links, and the mean value of the abnormal trend characteristic values of all preset dimensions of all preset links under all overall abnormal sub-stages are used for determining the current operation condition evaluation value corresponding to the marine working system.

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