Method and device for determining acid value, storage medium and electronic device

Abstract

The invention discloses a method and device for determining an acid value, a storage medium and an electronic device, and relates to the field of computers.The method for determining the acid value comprises the steps that a first object is sampled multiple times, first object data of the first object changing along with time are obtained, the sampling period of the first object data is a non-equal time interval, and the sampling period of the first object data is a non-equal time interval; the first object data is used for indicating the acid value of the first object; processing the first object data, and constructing a prediction model for performing object data prediction on the first object according to the obtained second object data; and performing object data prediction on the first object according to the prediction model, and determining the acid value of the first object at the target moment according to third object data obtained through prediction.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular, to a method and device for determining an acid value, a storage medium, and an electronic device. Background Art

[0002] Lubricating oil is used in the mechanical contact of various transmission gears and bearings of wind turbines. It forms an oil film on the contact surface to reduce friction and wear, extend the service life of the machinery, and also plays the role of cooling, rust and corrosion prevention, cleaning, sealing and buffering. The gearbox of the wind turbine is an important component of the wind turbine. The gearbox is prone to tooth surface wear, pitting and lubricating oil aging during high-speed rotation, which can seriously lead to gearbox accidents. Therefore, the gearbox is an important lubrication part of the wind turbine. The working temperature of the lubricating oil is relatively high, and the effects of mechanical wear, moisture and impurities accelerate the oxidation process of the oil. After the lubricating oil is oxidized, it is easy to cause the viscosity of the oil to increase, and non-oil-soluble polymers to form deposits such as paint films, so the anti-oxidation ability of the lubricating oil is required to be high. When the oil is oxidized to a certain extent, the oil is difficult to effectively lubricate mechanical equipment such as gears and bearings. Timely prediction of changes in the oxidation performance of the oil can carry out operation and maintenance work in advance.

[0003] Since there may be some acidic components in the oil in the form of additives or degradation products (such as oxidation products) during the use of the oil, the acid value of the lubricating oil represents the degree of oxidation and deterioration of the oil. It can be measured by the amount of alkali used when the sample is titrated to the specified end point in the specified solvent, and the unit is mg / g. As the additives are gradually consumed, the acid value will decrease slightly in the initial use, and as the oil oxidizes and deteriorates, the acid value will gradually increase. Therefore, the trend of the acid value of the oil does not have a single linear characteristic. At the same time, due to the long on-site sampling cycle and the influence of various operation and maintenance factors, the offline detection data is generally at non-equal time intervals, and the data samples are insufficient.

[0004] Regarding the related technologies, the trend of changes in the acid value of oil products does not have a single linear feature, and due to the influence of various operation and maintenance factors, the offline detection data is generally at unequal time intervals. The existing technology is difficult to effectively predict the acid value content, and no effective solution has been proposed so far.

[0005] Therefore, it is necessary to improve the related technology to overcome the above-mentioned defects in the related technology. Summary of the invention

[0006] The embodiments of the present application provide a method and device for determining an acid value, a storage medium and an electronic device, so as to at least solve the problem in the related art that the trend of the acid value change of oil products does not have a single linear characteristic, and due to the influence of various operation and maintenance factors, the offline detection data is generally at unequal time intervals, and the existing technology is difficult to effectively predict the acid value content.

[0007] According to one aspect of an embodiment of the present application, a method for determining an acid value is provided, comprising: sampling a first object multiple times to obtain first object data of the first object that changes with time, wherein the sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate the acid value of the first object; processing the first object data, and constructing a prediction model for predicting object data of the first object based on the obtained second object data; predicting object data of the first object based on the prediction model, and determining the acid value of the first object at a target time based on the predicted third object data.

[0008] In an exemplary embodiment, the first object data is processed, and a prediction model for object data prediction of the first object is constructed based on the obtained second object data, including: processing the first object data to obtain the second object data; constructing a data matrix B and a vector Y based on the second object data, and calculating a model parameter P of the prediction model based on the data matrix B and the vector Y; and constructing the prediction model based on the model parameter P.

[0009] In an exemplary embodiment, the first object data is processed to obtain the second object data, including: constructing a raw data sequence X with unequal time intervals according to the first object data (0) =(x (0) (t1), x (0) (t2),…,x (0) (t n )), wherein the time interval Δt of the original data sequence i =t i -t i-1 ≠const, i=2, 3, ..., n, Δt1=1; construct an accumulation generation sequence X with equal time intervals according to the original data sequence (1) =(x (1) (t1), x (1) (t2),…,x (1) (t n )),in, Calculate the adjacent mean sequence M corresponding to the cumulative generated sequence (1) =(m (1) (t1), m (1) (t2),…m (1) (t n )), where m (1) (t k )=(x (1) (t k-1 )+x (1) (t k)) / 2, k=2, 3, ..., n; determine the original data sequence and the adjacent mean value sequence as the second object data.

[0010] In an exemplary embodiment, constructing a data matrix B and a vector Y according to the second object data, and calculating the model parameter P of the prediction model according to the data matrix B and the vector Y, including: constructing the data matrix according to the adjacent mean sequence And construct the vector according to the original data sequence The model parameter P is calculated based on the data matrix B and the vector Y, where: P includes a, b and u.

[0011] In an exemplary embodiment, constructing the prediction model according to the model parameter P includes: determining the prediction model according to a, b and u

[0012] In an exemplary embodiment, after performing object data prediction on the first object according to the prediction model and determining the acid value of the first object at a target time according to the predicted third object data, the method further includes: determining a damage state of a second object according to the acid value of the first object at the target time, wherein the first object is arranged around the second object and the damage state of the second object is proportional to the acid value; and adjusting the first object and the second object according to the damage state.

[0013] According to another aspect of an embodiment of the present application, there is also provided an acid value determination device, comprising: a sampling module, used to perform multiple sampling on a first object to obtain first object data of the first object that changes with time, wherein the sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate the acid value of the first object; a construction module, used to process the first object data, and to construct a prediction model for predicting object data of the first object based on the obtained second object data; and a determination module, used to predict object data of the first object based on the prediction model, and to determine the acid value of the first object at a target time based on the predicted third object data.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned method for determining the acid value when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the acid value determination method through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0017] Through the present application, a first object is sampled multiple times at non-equal time intervals to obtain first object data of the first object that changes with time, wherein the first object data is used to indicate the acid value of the first object; the first object is processed to obtain second object data, and a prediction model for predicting object data of the first object is constructed based on the obtained second object data; finally, the prediction model is used to predict object data of the first object, and the acid value of the first object at a target time is determined based on the predicted third object data; the above scheme is adopted to predict the acid value content of the gearbox lubricating oil (first object) based on the grey linear combination model (i.e., the above prediction model) at non-equal time intervals, and the change in the oxidation performance of the oil is predicted in a timely and accurate manner; thereby solving the problem in the related art that the trend of the acid value change of the oil does not have a single linear feature, and due to the influence of various operation and maintenance factors, the offline detection data is generally at non-equal time intervals, and the prior art is difficult to effectively predict the acid value content. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 It is a hardware structure block diagram of a computer terminal of a method for determining an acid value in an embodiment of the present application;

[0021] Figure 2 is a flow chart of a method for determining an acid value according to an embodiment of the present application;

[0022] Figure 3 is a schematic flow chart of an optional oil acid value prediction method according to an embodiment of the present application;

[0023] Figure 4 It is a structural block diagram of a device for determining an acid value according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure block diagram of a computer terminal for determining an acid value according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used for storing computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the determination methods of acid values ​​in the embodiments of the present application, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, i.e., realizing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to a computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and combinations thereof.

[0028] A wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a method for determining an acid value is provided. Figure 2 is a flow chart of a method for determining an acid value according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps S202-S208:

[0030] Step S202: sampling the first object multiple times to obtain first object data of the first object that changes with time, wherein the sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate the acid value of the first object;

[0031] Optionally, according to the on-site operation and maintenance work schedule, the lubricating oil of the wind turbine (the first object) can be sampled regularly and sent to the laboratory for offline testing. The oil acid value can be measured by an automatic positioning titrator to obtain oil acid value data that changes with time (i.e., the first object data), where the sampling period is a non-equal time interval.

[0032] Step S204: Processing the first object data, and constructing a prediction model for predicting object data of the first object according to the obtained second object data;

[0033] Step S206: performing object data prediction on the first object according to the prediction model, and determining the acid value of the first object at the target time according to the predicted third object data.

[0034] Through the above steps, the first object is sampled multiple times at non-equal time intervals to obtain first object data of the first object that changes with time, wherein the first object data is used to indicate the acid value of the first object; the first object is processed to obtain second object data, and a prediction model for predicting object data of the first object is constructed based on the obtained second object data; finally, the object data of the first object is predicted using the prediction model, and the acid value of the first object at the target time is determined based on the predicted third object data; the above scheme is adopted to predict the acid value content of the gearbox lubricating oil (first object) based on the grey linear combination model with non-equal time intervals (i.e., the above prediction model), and the change in the oxidation performance of the oil is predicted in a timely and accurate manner; thereby solving the problem in the related art that the change trend of the acid value of the oil does not have a single linear feature, and due to the influence of various operation and maintenance factors, the offline detection data is generally at non-equal time intervals, and the prior art is difficult to effectively predict the acid value content.

[0035] In an exemplary embodiment, the first object data is processed, and a prediction model for object data prediction of the first object is constructed based on the obtained second object data, including: processing the first object data to obtain the second object data; constructing a data matrix B and a vector Y based on the second object data, and calculating a model parameter P of the prediction model based on the data matrix B and the vector Y; and constructing the prediction model based on the model parameter P.

[0036] The grey linear combination model (i.e., prediction model) of the present application requires that the input data have the characteristic of equal time intervals, while the offline detection data collected in the prior art is generally of unequal time intervals. Therefore, it is necessary to process the first object data so that it meets the data requirements of the prediction model to obtain the second object data, and then construct the data matrix B and vector Y based on the second object data, and then calculate the model parameters P of the prediction model, and finally construct a prediction model that meets the change characteristics of the first object data based on the model parameters P.

[0037] Optionally, processing the first object data to obtain the second object data includes: constructing an original data sequence X with unequal time intervals according to the first object data (0) =(x (0) (t1), x (0) (t2), …x (0) (t n )), wherein the time interval Δt of the original data sequence i =t i -t i-1≠const, i=2, 3, ..., n, Δt1=1; construct an accumulation generation sequence X with equal time intervals according to the original data sequence (1) =(x (1) (t1), x (1) (t2), …x (1) (t n )),in, Calculate the adjacent mean sequence M corresponding to the cumulative generated sequence (1) =(m (1) (t1), m (1) (t2),…m (1) (t n )), where m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k=2, 3, ..., n; determine the original data sequence and the adjacent mean value sequence as the second object data.

[0038] Specifically, it is necessary to first construct the original sequence of oil acid values ​​with unequal time intervals (i.e., original data sequence) X (0) =(x (0) (t1), x (0) (t2), …x (0) (t n )), Δt i =t i -t i-1 ≠const, i=2, 3, ..., n, Δt1=1; then construct the cumulative sequence (i.e., the generated sequence) X (1) =(x (1) (t1), x (1) (t2), …x (1) (t n )),in Then x (1) (t) = x (1) (t k ) is an equally spaced sequence; finally, the adjacent mean sequence is calculated, and the data sequence X (1) =(x (1) (t1), x (1) (t2), …x (1) (t n The adjacent mean sequence of )) is: M (1) =(m (1) (t1), m (1) (t2),…m (1) (t n )), where m (1) (tk )=((x (1) (t k-1 )+x (1) (t k )) / 2, k = 2, 3, ..., n

[0039] It should be noted that in this application, the original sequence is based on the fan gear oil acid value data with non-equal time intervals, which means that the time point of each data collection is not fixed and there may be large time differences. In order to apply the grey linear combination model, it is necessary to convert this non-equal time interval sequence into an equal time interval sequence. This is because the grey model requires the sequence data to follow a certain time series law when calculating and predicting, and this law is easier to be modeled and predicted in data with equal time intervals.

[0040] Specifically, the above-mentioned cumulative sequence, namely the 1-AGO sequence, is constructed by adding the data in the original sequence one by one. This accumulation operation actually generates a new sequence, in which each data point is the sum of all the data from the first data point to the current data point in the original sequence. The accumulation operation itself does not change the time interval, but it provides a basis for constructing a sequence with equal time intervals in subsequent steps.

[0041] When converting to an equal time interval sequence, it is actually achieved by introducing virtual time points. Assuming that the time intervals between two adjacent time points in the original sequence are not equal, we can insert virtual time points in these unequal time intervals so that the intervals between each virtual time point and the next actual time point are equal. In this way, we can construct a new sequence based on these virtual and actual time points, in which the time interval between each time point and the previous time point is equal. This new sequence is the so-called equal time interval sequence.

[0042] It should be noted that the use of inserting virtual time points to convert non-equal time interval series into equal time interval series will lead to an apparent increase in the amount of data, but this is mainly to meet the needs of model calculation and will not introduce actual observation data. The data at the inserted virtual time points are usually estimated by interpolation methods, such as linear interpolation, spline interpolation, etc., in order to make the data meet the input requirements of the grey linear combination model.

[0043] In the grey linear combination model, the equal time interval of data is an important premise, because some calculation steps in the model, such as the accumulation generation operation (AGO), require the equal time difference between data points to ensure the accuracy of the calculation and the applicability of the model. By interpolating and generating virtual data points, the original data can be made more consistent with the input format of the model, so that more accurate predictions can be made.

[0044] However, it should be noted that although the amount of data has increased on the surface, the data at these virtual points are not real observations, but speculations based on existing data. Therefore, in subsequent data analysis or model verification, these virtual points should be distinguished from real observation points to avoid attributing the model's prediction errors to the introduction of virtual point data. In practical applications, the frequency and method of inserting virtual points need to be selected according to the specific situation to ensure the accuracy and reliability of model predictions. For example, if the time interval between two observation points is very long, it may be necessary to insert multiple virtual points to better approximate the actual change trend, but too many virtual points may also introduce unnecessary complexity and affect the model's prediction effect. Therefore, this is a decision-making process that requires balance.

[0045] Optionally, constructing a data matrix B and a vector Y according to the second object data, and calculating a model parameter P of the prediction model according to the data matrix B and the vector Y, including: constructing the data matrix according to the adjacent mean sequence And construct the vector according to the original data sequence The model parameter P is calculated based on the data matrix B and the vector Y, where: P includes a, b and u.

[0046] Construct the data matrix B and vector Y, and define Then use B and Y to calculate the least squares estimated parameter column (i.e. model parameter P),

[0047] Optionally, constructing the prediction model according to the model parameter P includes: determining the prediction model according to a, b and u

[0048] Finally, substitute P into the prediction formula of the acid value series to obtain the prediction model

[0049] Optionally, after performing object data prediction on the first object according to the prediction model and determining the acid value of the first object at the target moment according to the predicted third object data, the method further includes: determining a damage state of the second object according to the acid value of the first object at the target moment, wherein the first object is arranged around the second object and the damage state of the second object is proportional to the acid value; and adjusting the first object and the second object according to the damage state.

[0050] After obtaining the model, input any time in the future to predict the corresponding acid value data, determine the iron content in the lubricating oil based on the acid value data, judge the wear state of the gear (i.e. the above-mentioned damage state) based on the element content, and formulate corresponding operation and maintenance measures.

[0051] This application proposes a fan gear oil oxidation prediction method based on a gray linear combination model with non-equidistant time intervals. The acid value of the lubricating oil reflects the degree of oil oxidation and deterioration. The trend of the oil acid value change has both exponential characteristics and linear characteristics. Offline detection data is generally non-equidistant time intervals and the data sample is insufficient. In response to this difficulty, this application uses gray system theory to specifically study the characteristics of small data and poor information, and combines the distribution combination characteristics of the data to use a gray linear combination model based on non-equidistant time intervals to predict the acid value content of the gearbox lubricating oil, and timely predict the changes in the oxidation performance of the oil.

[0052] In an optional embodiment, the present application embodiment provides an optional oil acid value prediction method, the process of which is as follows: Figure 3 As shown, the following steps are included:

[0053] Step 1: Take a sample of lubricating oil and test it to obtain the acid value data of the oil;

[0054] Step 2, obtaining multiple historical sampling and test results from the oil acid value database to obtain multiple oil acid value data;

[0055] Step 3: Based on the multiple oil acid value data, a sequence of oil acid value data that changes over time is constructed, namely, the original sequence X (0) =(x (0) (t1), x (0) (t2), …x (0) (t n )), wherein the time interval Δt of the original data sequence i =t i -t i-1 ≠const, i=2, 3,..., n, Δt1=1;

[0056] Step 4: Construct the cumulative generation sequence X (1) =(x (1) (t1), x (1) (t2), …x (1) (t n )),in,

[0057] Step 5: Convert to a sequence of equal time intervals;

[0058] Step 6: Calculate the adjacent mean sequence M (1) =(m (1) (t1), m (1) (t2),…m (1) (t n )), where m (1) (t k )=(x (1) (tk-1 )+x (1) (t k )) / 2, k = 2, 3, ..., n;

[0059] Step 7: Construct the data matrix and adjacent

[0060] Step 8: Estimate parameters using the least squares method Then the prediction model is constructed using the parameter P;

[0061] Step 9: Use the prediction model to predict data;

[0062] Step 10: Output the results.

[0063] The purpose of this application is to propose a fan gear oil oxidation prediction method based on a grey linear combination model with non-equal time intervals. The acid value of the lubricating oil reflects the degree of oil oxidation and deterioration, but the trend of the oil acid value change does not have a single linear feature. At the same time, due to the long on-site sampling cycle and the influence of various operation and maintenance factors, the offline detection data is generally non-equal time intervals, and the data samples are insufficient. The grey system theory is used to specifically study the characteristics of small data and poor information, and combined with the distribution and combination characteristics of the data, the grey linear combination model based on non-equal time intervals is used to predict the acid value content of the gearbox lubricating oil. The timely prediction of the changes in the oxidation performance of the oil can carry out operation and maintenance work in advance.

[0064] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0065] In the present embodiment, a device for determining an acid value is also provided, and the device is used to implement the above-mentioned embodiment and the preferred embodiment, and the description thereof will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the equipment described in the following embodiments is preferably implemented with software, the implementation of hardware, or a combination of software and hardware is also possible and contemplated.

[0066] Figure 4: is a structural block diagram of a device for determining an acid value according to an embodiment of the present application, the device comprising:

[0067] A sampling module 42, configured to perform multiple sampling on a first object to obtain first object data of the first object that changes with time, wherein the sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate the acid value of the first object;

[0068] A construction module 44, configured to process the first object data and construct a prediction model for predicting object data of the first object according to the obtained second object data;

[0069] The determination module 46 is used to predict the object data of the first object according to the prediction model, and determine the acid value of the first object at the target time according to the predicted third object data.

[0070] Through the above-mentioned device, the first object is sampled multiple times at non-equal time intervals to obtain first object data of the first object that changes with time, wherein the first object data is used to indicate the acid value of the first object; the first object is processed to obtain second object data, and a prediction model for predicting object data of the first object is constructed based on the obtained second object data; finally, the object data of the first object is predicted using the prediction model, and the acid value of the first object at the target time is determined based on the predicted third object data; the above-mentioned scheme is adopted to predict the acid value content of the gearbox lubricating oil (first object) based on the grey linear combination model (i.e., the above-mentioned prediction model) at non-equal time intervals, and the change in the oxidation performance of the oil is predicted in a timely and accurate manner; thereby solving the problem in the related art that the change trend of the acid value of the oil does not have a single linear feature, and due to the influence of various operation and maintenance factors, the offline detection data is generally at non-equal time intervals, and the prior art is difficult to effectively predict the acid value content.

[0071] Optionally, the above-mentioned construction module 44 is also used to process the first object data to obtain the second object data; construct a data matrix B and a vector Y according to the second object data, and calculate the model parameter P of the prediction model according to the data matrix B and the vector Y; and construct the prediction model according to the model parameter P.

[0072] Optionally, the construction module 44 is further configured to construct an original data sequence X with unequal time intervals according to the first object data. (0) =(x (0) (t1), x (0) (t2), …x (0) (t n )), wherein the time interval Δt of the original data sequence i =ti -t i-1 ≠const, i=2,3,…,n, Δt1=1; construct an accumulation generation sequence X with equal time intervals according to the original data sequence (1) =(x (1) (t1), x (1) (t2), …x (1) (t n )),in, Calculate the adjacent mean sequence M corresponding to the cumulative generated sequence (1) =(m (1) (t1), m (1) (t2),…m (1) (t n )), where m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k=2, 3, ..., n; determine the original data sequence and the adjacent mean value sequence as the second object data.

[0073] Optionally, the construction module 44 is further used to construct the data matrix according to the adjacent mean sequence. And construct the vector according to the original data sequence The model parameter P is calculated based on the data matrix B and the vector Y, where: P includes a, b and u.

[0074] Optionally, the construction module 44 is further used to determine the prediction model according to a, b and u.

[0075] Finally, substitute P into the prediction formula of the acid value series to obtain the prediction model

[0076] Optionally, the above-mentioned determination module 46 is also used to determine the damage state of the second object according to the acid value of the first object at the target moment, wherein the first object is arranged around the second object, and the damage state of the second object is proportional to the acid value; and adjust the first object and the second object according to the damage state.

[0077] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0078] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0079] S1, sampling a first object multiple times to obtain first object data of the first object that changes with time, wherein a sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate an acid value of the first object;

[0080] S2, processing the first object data, and constructing a prediction model for predicting object data of the first object according to the obtained second object data;

[0081] S3, performing object data prediction on the first object according to the prediction model, and determining the acid value of the first object at the target time according to the predicted third object data.

[0082] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0083] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0084] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0085] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0086] S1, sampling a first object multiple times to obtain first object data of the first object that changes with time, wherein a sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate an acid value of the first object;

[0087] S2, processing the first object data, and constructing a prediction model for predicting object data of the first object according to the obtained second object data;

[0088] S3, performing object data prediction on the first object according to the prediction model, and determining the acid value of the first object at the target time according to the predicted third object data.

[0089] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0090] An embodiment of the present application further provides a computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program product, and when the computer program is executed by a processor, the steps of the method described in each embodiment of the present application are implemented.

[0091] Optionally, in this embodiment, the above computer program may be configured to implement the following steps when executed by a processor:

[0092] S1, sampling a first object multiple times to obtain first object data of the first object that changes with time, wherein a sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate an acid value of the first object;

[0093] S2, processing the first object data, and constructing a prediction model for predicting object data of the first object according to the obtained second object data;

[0094] S3, performing object data prediction on the first object according to the prediction model, and determining the acid value of the first object at the target time according to the predicted third object data.

[0095] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0096] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0097] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining an acid value, characterized in that: include: Sampling a first object multiple times to obtain first object data of the first object that changes with time, wherein a sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate an acid value of the first object; Processing the first object data, and constructing a prediction model for predicting object data of the first object according to the obtained second object data; Object data prediction is performed on the first object according to the prediction model, and the acid value of the first object at the target time is determined according to the predicted third object data.

2. The method according to claim 1, characterized in that Processing the first object data and constructing a prediction model for predicting object data of the first object according to the obtained second object data, including: Processing the first object data to obtain the second object data; Constructing a data matrix B and a vector Y according to the second object data, and calculating a model parameter P of the prediction model according to the data matrix B and the vector Y; The prediction model is constructed according to the model parameter P.

3. The method according to claim 2, characterized in that Processing the first object data to obtain the second object data includes: According to the first object data, an original data sequence X with unequal time intervals is constructed. (0) =(x (0) (t1), x (0) (t2), …x (0) (t n )), wherein the time interval Δt of the original data sequence i =t i -t i-1 ≠const, i=2, 3,..., n, Δt1=1; According to the original data sequence, an accumulation generation sequence X with equal time intervals is constructed (1) =(x (1) (t1), x (1) (t2), …x (1) (t n )),in, Calculate the adjacent mean sequence M corresponding to the cumulative generated sequence (1) =(m (1) (t1), m (1) (t2),…m (1) (t n )), where m (1) (t k )=(x (1) (t k-1 )+x (1) (t k )) / 2, k=2, 3, ..., n; The original data sequence and the adjacent mean value sequence are determined as the second object data.

4. The method according to claim 3, characterized in that Constructing a data matrix B and a vector Y according to the second object data, and calculating a model parameter P of the prediction model according to the data matrix B and the vector Y, including: The data matrix is ​​constructed according to the adjacent mean sequence And construct the vector according to the original data sequence The model parameter P is calculated based on the data matrix B and the vector Y, where: P includes a, b and u.

5. The method according to claim 4, characterized in that Constructing the prediction model according to the model parameter P includes: Determine the prediction model based on a, b and u 6. The method according to claim 5, characterized in that After performing object data prediction on the first object according to the prediction model and determining the acid value of the first object at the target time according to the predicted third object data, the method further includes: determining a damage state of a second object according to the acid value of the first object at the target time, wherein the first object is arranged around the second object, and the damage state of the second object is proportional to the acid value; The first object and the second object are adjusted according to the damage status.

7. A device for determining an acid value, characterized in that: include: A sampling module, used for sampling a first object multiple times to obtain first object data of the first object that changes with time, wherein the sampling period of the first object data is a non-equal time interval, and the first object data is used to indicate the acid value of the first object; A construction module, configured to process the first object data and construct a prediction model for predicting object data of the first object according to the obtained second object data; The determination module is used to predict the object data of the first object according to the prediction model, and determine the acid value of the first object at the target time according to the predicted third object data.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 6 when executed.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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