CN114254791A - Method and device for predicting oxygen content of flue gas - Google Patents
Method and device for predicting oxygen content of flue gas Download PDFInfo
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 94
- 239000001301 oxygen Substances 0.000 title claims abstract description 94
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 61
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 48
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- MCMNRKCIXSYSNV-UHFFFAOYSA-N Zirconium dioxide Chemical compound O=[Zr]=O MCMNRKCIXSYSNV-UHFFFAOYSA-N 0.000 description 4
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Abstract
The invention discloses a method and a device for predicting oxygen content of smoke, a computer readable storage medium and electronic equipment, wherein the method comprises the following steps: acquiring feature data of source equipment and feature data of target equipment, wherein the feature data of the source equipment is unshared data; calculating the data distribution of the characteristic data of the source equipment relative to the target equipment according to a hash table generated by the characteristic data of the target equipment; determining the weight of the characteristic data of the source equipment relative to the target equipment according to the data distribution of the characteristic data of the source equipment relative to the target equipment; establishing a relation between a regression model and the oxygen content of the flue gas according to the weight of the characteristic data of the source equipment relative to the target equipment and the historical flue gas content corresponding to the characteristic data of the source equipment to obtain a flue gas oxygen content prediction soft measurement model; and predicting the oxygen content of the smoke of the target equipment according to the soft measurement model for predicting the oxygen content of the smoke. By the technical scheme provided by the invention, the characteristic data of the source equipment is migrated to the target equipment, so that the characteristic data does not need to be shared between the source equipment and the target equipment, and the data security is ensured.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a method and a device for predicting oxygen content of smoke.
Background
The oxygen content of the flue gas is one of important marks for measuring whether the boiler operates safely, economically and environmentally, the oxygen content of the flue gas at the outlet of a hearth is usually monitored by using zirconia in industrial production, but the environment in the hearth is extremely complex, and a zirconia measuring point cannot work normally due to abrasion. Meanwhile, a pure hardware measurement system cannot detect the operation change characteristic of the oxygen content of the flue gas at the outlet of the hearth. Therefore, it is necessary to find a practical, reliable, accurate, and economical method for measuring oxygen content.
At present, for the prediction of a boiler to be predicted without a label, a mapping relation between characteristic data of the boiler to be predicted and the oxygen content of flue gas is established through data sharing between the boiler to be predicted and other boilers, a flue gas oxygen content prediction model is obtained, and the flue gas oxygen content prediction model is used for realizing the prediction of the oxygen content of the flue gas of the boiler to be predicted.
However, the above technical solution needs to share data among several boilers, thus resulting in low data security among several boilers.
Disclosure of Invention
The invention provides a method and a device for predicting the oxygen content of flue gas, a computer readable storage medium and electronic equipment aiming at the technical problems in the prior art, and the characteristic data of a boiler does not need to be shared between source equipment and target equipment, so that the data safety is ensured.
In a first aspect, the invention provides a method for predicting oxygen content of flue gas, comprising the following steps:
acquiring feature data of source equipment and feature data of target equipment, wherein the feature data of the source equipment is unshared data;
calculating the data distribution of the feature data of the source equipment relative to the target equipment according to a hash table generated by the feature data of the target equipment;
determining the weight of the feature data of the source device relative to the target device according to the data distribution of the feature data of the source device relative to the target device;
establishing a relation between a regression model and the oxygen content of the flue gas according to the weight of the characteristic data of the source equipment relative to the target equipment and the historical flue gas content corresponding to the characteristic data of the source equipment to obtain a flue gas oxygen content prediction soft measurement model;
and predicting the oxygen content of the smoke of the target equipment according to the soft measurement model for predicting the oxygen content of the smoke.
In a second aspect, the invention provides a method for predicting oxygen content of flue gas, comprising the following steps:
the data acquisition module is used for acquiring the feature data of source equipment and the feature data of target equipment, wherein the feature data of the source equipment is non-shared data;
the distribution calculation module is used for calculating the data distribution of the characteristic data of the source equipment relative to the target equipment according to a hash table generated by the characteristic data of the target equipment;
the weight determining module is used for determining the weight of the feature data of the source equipment relative to the target equipment according to the data distribution of the feature data of the source equipment relative to the target equipment;
the model determining module is used for establishing a relation between a regression model and the oxygen content of the smoke according to the weight of the characteristic data of the source equipment relative to the target equipment and the historical smoke content corresponding to the characteristic data of the source equipment to obtain a soft measurement model for predicting the oxygen content of the smoke;
and the prediction module is used for predicting the oxygen content of the smoke of the target equipment according to the soft measurement model for predicting the oxygen content of the smoke.
In a third aspect, the invention provides a computer-readable storage medium comprising executable instructions which, when executed by a processor of an electronic device, cause the processor to perform the method according to any one of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides a method, a device, a computer readable storage medium and an electronic device for predicting oxygen content of smoke, the method comprises the steps of obtaining characteristic data of source equipment and characteristic data of target equipment, wherein the characteristic data of the source equipment is unshared data, then calculating data distribution of the characteristic data of the source equipment relative to the target equipment according to a hash table generated by the characteristic data of the target equipment, then determining the weight of the characteristic data of the source equipment relative to the target equipment according to the data distribution of the characteristic data of the source equipment relative to the target equipment, then establishing a relation between a regression model and the oxygen content of the smoke according to the weight of the characteristic data of the source equipment relative to the target equipment and historical smoke content corresponding to the characteristic data of the source equipment to obtain a soft measurement model for predicting the oxygen content of the smoke, and then predicting the soft measurement model according to the oxygen content of the smoke, and predicting the oxygen content of the smoke of the target equipment. According to the technical scheme provided by the invention, the data association between the source equipment and the target equipment is established through the hash table of the unshared characteristic data of the source equipment and the characteristic data of the target equipment, the data of the source equipment is migrated to the target equipment, and the characteristic data of the equipment which is not required to be shared between the source equipment and the target equipment is not required, so that the data security is ensured.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flow chart of a method for predicting oxygen content in flue gas according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another method for predicting oxygen content in flue gas according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for predicting oxygen content in flue gas according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for predicting oxygen content in flue gas, which is applied to an edge node of a boiler to be predicted, and includes the following steps:
Specifically, the source device and the target device are both devices capable of generating flue gas, such as a gas boiler. The target device and the source device are of the same device type, for example, the target device and the source device are both gas boilers, but information such as production units, device models, and the like may be different.
Specifically, the characteristic data of the target device is multiple, each characteristic data includes a plurality of characteristic values of a plurality of characteristics respectively corresponding to the target device, the plurality of characteristics are influence factors influencing the oxygen content of the flue gas of the target device, and the determination needs to be specifically combined with an actual scene, for example, if the target device is a gas boiler, the plurality of characteristics include, but are not limited to, a gas flow, a gas temperature, a smoke exhaust temperature, a smoke flow, a gas pressure, a smoke humidity, a smoke pressure, and the like. The feature data of the source device are multiple, each feature data of the source device comprises a plurality of feature values of which the features respectively correspond to the source device, and the features corresponding to the plurality of features and the feature data of the target device are the same.
It should be noted that the feature data of the source device is unshared data, so that the data security of the feature data of the source device is ensured.
Optionally, a plurality of feature data of the target device are obtained through an edge node corresponding to the target device. The edge node of the target device may be understood as a node which is closest to the target device and can perform data processing and data interaction. The node includes, but is not limited to, any one or more of an edge server, an edge gateway, and an edge controller.
Optionally, the plurality of feature data of the source device are obtained through an edge node corresponding to the source device. The edge node of the source device may be understood as a node which is closest to the source device and can perform data processing and data interaction. The node includes, but is not limited to, any one or more of an edge server, an edge gateway, and an edge controller. Correspondingly, if the feature data of the source device is the unshared data, it indicates that the feature data of the source device does not exit the edge node corresponding to the source device.
And 102, calculating the data distribution of the feature data of the source equipment relative to the target equipment according to a hash table generated by the feature data of the target equipment.
In this embodiment, the data distribution of the feature data of the source device with respect to the feature data of the target device is calculated through a hash table generated by the feature data of the target device, thereby establishing a link between the feature data of the source device and the feature data of the target device.
Specifically, for each feature data of the target device, converting the feature data through a hash function to obtain a hash value of the feature data; and obtaining a hash table through the hash values of all the characteristic data of the target equipment. The hash value may be understood as a value obtained by performing keyless encryption on feature data of the target device through a hash function. It should be noted that, in consideration of the fact that the hash function can rapidly and simply implement the keyless encryption processing of the data, the embodiment of the present invention selects the hash function to perform the keyless encryption on the feature data of the target device.
Specifically, the data distribution may be a distribution probability, may be a distribution frequency, and is preferably a distribution probability.
In one embodiment, the data distribution of the feature data of the source device relative to the target device may be calculated by:
obtaining a hash value of feature data of source equipment; and determining the distribution probability of the feature data of the source device relative to the target device according to the hash table and the hash value generated by the feature data of the target device, and determining the distribution probability as the data distribution of the feature data of the source device relative to the target device.
In this embodiment, the data distribution is obtained based on the hash value and the hash table, and does not involve comparison between the feature data, and the feature data between the source device and the target device does not need to be shared, thereby ensuring data security.
Specifically, the hash value of the feature data of the source device may be understood as a value obtained by performing keyless encryption on the feature data of the source device, and the feature data of the source device does not need to be shared, and only information sharing is performed based on the hash value of the feature data of the source device, thereby ensuring data security between the source device and the target device. It should be noted that, when the parameters in the hash function are different, the analysis difficulty of the hash value and the hash table may be increased, and therefore, the hash value and the hash table are obtained by performing keyless encryption based on the same hash function, thereby reducing the analysis difficulty between the hash value and the hash table.
Optionally, the hash value of the feature data of the source device is obtained by an edge node of the source device corresponding to the internet of things, and the node may perform keyless encryption on the feature data of the source device based on a hash function generating a hash table to obtain the hash value of the feature data of the source device.
Optionally, the replacement data of the feature data of the source device is determined from the feature data of the target device according to a hash table and a hash value generated from the feature data of the target device, and the distribution probability of the replacement data in the feature data of the target device is determined as the distribution probability of the feature data of the source device relative to the target device. Here, the replacement data corresponding to the feature data of the source device is determined from the feature data of the target device simply and quickly through the hash value and the hash table, so that data association between the target device and the source device is established. As a possible implementation manner, based on a locality sensitive hashing algorithm, similarity analysis is performed on hash values of the hash table and the feature data of the source device to determine replacement data of the feature data of the source device from each feature data of the target device. When a plurality of pieces of replacement data of the feature data of the source device are determined from the respective feature data of the target device, one piece of replacement data may be randomly selected.
Here, considering that the non-parameter estimation method does not need to rely on any prior assumption of data, but is determined based on the data itself, and is simpler than the parameter estimation method, the embodiment of the present invention selects the non-parameter estimation method to determine the distribution probability, and it should be further noted that the present invention does not intend to limit the non-parameter estimation method, and may specifically combine with the actual situation to determine, considering that the data amount of the feature data in the embodiment of the present invention is relatively large, in order to estimate the data distribution more accurately, the kernel density estimation method is preferred.
Optionally, based on a kernel density estimation method, a probability of distribution of the replacement data in the feature data of the target device is determined.
In this embodiment, the data association between the target device and the source device is established by determining the weight of the feature data of the source device with respect to the target device, so that the data of the source device is migrated to the target device without sharing the feature data between the source device and the target device.
In one embodiment, the weight of the feature data of the source device relative to the target device may be determined specifically by:
determining a distribution probability of the feature data of the source device; and determining the ratio of the distribution probability of the feature data of the source device relative to the target device and the distribution probability of the feature data of the source device as the weight of the feature data of the source device relative to the target device.
In this embodiment, the weight of the feature data of the source device relative to the target device is determined by the ratio of the distribution probability of the feature data of the source device relative to the target device to the distribution probability of the feature data of the source device, so that the feature data of the source device is migrated to the target device, data migration between the target device and the source device is realized, data sharing is not required, and data security is ensured.
Specifically, an edge node of the source device sends a hash value of feature data of the source device to an edge node of the target device, the edge node of the target device determines a distribution probability of the feature data of the source device with respect to the target device according to the hash table and the hash value of the feature data of the source device, and sends the distribution probability to the edge node of the source device, and the edge node of the source device determines, for each feature data of the source device, a ratio of the distribution probability of the feature data with respect to the target device to a distribution probability in each feature data with respect to the source device, and determines the ratio as a weight of the feature data of the source device with respect to the target device.
Optionally, the distribution probability of the feature data of the source device is determined based on a non-parametric estimation method, which is preferably a kernel density estimation method. Specifically, a probability density model of feature data of the source equipment is determined based on a nonparametric estimation method; and substituting the characteristic data of the source equipment into the probability density model to determine the distribution probability of the characteristic data of the source equipment.
It should be noted that the edge node corresponding to the source device sends the hash value of the feature data of the source device, and the edge node corresponding to the target device sends the distribution probability of the feature data of the source device relative to the target device, and the target device and the source device are not involved in data sharing, so that data security is ensured.
And 104, establishing a relation between a regression model and the oxygen content of the flue gas according to the weight of the characteristic data of the source equipment relative to the target equipment and the historical flue gas content corresponding to the characteristic data of the source equipment to obtain a flue gas oxygen content prediction soft measurement model.
In the embodiment, the relationship between the regression model and the flue gas oxygen content is established through the weight of the characteristic data of the source equipment relative to the target equipment and the historical flue gas content corresponding to the characteristic data of the source equipment, so that the flue gas oxygen content prediction soft measurement model is obtained, the characteristic data of the source equipment does not need to be shared, and the data safety is ensured.
In one embodiment, the soft measurement model for predicting the oxygen content of the smoke can be determined by the following method:
and training a regression model according to the weight of the feature data of the source equipment relative to the target equipment and the historical smoke content corresponding to the feature data of the source equipment, determining the trained regression model as a smoke oxygen content prediction soft measurement model, wherein the smoke oxygen content prediction soft measurement model indicates the relationship between the feature data of the target equipment and the smoke oxygen content.
Specifically, the edge node corresponding to the source device trains the regression model based on the weights of the plurality of feature data of the source device relative to the target device and the corresponding historical smoke oxygen contents, and determines the trained regression model as a smoke oxygen content prediction soft measurement model, wherein the trained smoke oxygen content prediction soft measurement model takes the weights of the feature data of the source device relative to the target device into account, so that the relationship between the feature data of the target device and the smoke oxygen contents can be indicated.
Specifically, the feature data of the source device and the weight of the feature data of the source device relative to the target device can be used for migrating the data of the source device to the target device, and subsequently, parameters of the regression model are adjusted according to the weights corresponding to the feature data of the source device, so that the adjusted parameters of the regression model can reflect the relationship between the feature data of the target device and the oxygen content of the flue gas, and the data sharing between the source device and the target device is not involved, and the data security is ensured.
Specifically, the regression model may be any one of the regression models in the prior art, such as a neural network model, and is not limited herein.
The data of the target equipment and the source equipment are distributed at different edge nodes in the Internet of things, and the data security problem can be caused when the data are shared for model training.
And 105, predicting the oxygen content of the smoke of the target equipment according to the soft measurement model for predicting the oxygen content of the smoke.
Specifically, the current characteristic data of the target device is collected and substituted into the flue gas oxygen content prediction, so that the flue gas oxygen content of the target device can be determined.
In order to better understand the data processing procedure between the edge node of the target device and the edge node of the source device, for example, assume that the edge node of the target device is set as a, the edge node of the source device is set as B, B sends the hash value corresponding to each feature data of the source device to B, then, receiving the distribution probability of each characteristic data sent by A relative to the target equipment, determining the weight of each characteristic data relative to the target equipment by combining the distribution probability of the characteristic data in each characteristic data of the source equipment, then training a regression model by utilizing the weight corresponding to each characteristic data and the oxygen content of the historical flue gas, and B, obtaining a smoke oxygen content prediction soft measurement model, sending the smoke oxygen content prediction soft measurement model to A, and predicting the smoke oxygen content of the target equipment by the smoke oxygen content prediction soft measurement model.
According to the technical scheme, the embodiment of the invention has at least the following effective effects: the data association between the source device and the target device is established through the hash table of the feature data of the target device, the weight of the feature data of the source device relative to the target device is obtained, the feature data of the source device is migrated to the target device, the feature data between the source device and the target device does not need to be shared, and therefore data security is guaranteed.
Fig. 1 shows only a basic embodiment of the method of the present invention, and based on this, certain optimization and expansion can be performed, and other preferred embodiments of the method can also be obtained.
To more clearly illustrate the technical solution of the present invention, please refer to fig. 2, an embodiment of the present invention provides another method for predicting oxygen content in flue gas, and this embodiment is further described with reference to specific application scenarios on the basis of the foregoing embodiment. In this embodiment, the method may specifically include the following steps:
Assuming that an edge node corresponding to the target device is a and an edge node corresponding to the source device is B, the a can acquire all feature data of the target device, and the B can acquire all feature data of the source device.
And A, carrying out keyless encryption on each feature data of the target equipment according to the hash function to obtain a hash table, and sending the hash function to B.
And B, receiving the hash function sent by A, carrying out keyless encryption on the feature data of the source equipment according to the hash function to obtain a hash value of the feature data of the source equipment, and sending the hash value to A.
And A receives the hash value of the feature data of the source device sent by B, and performs similarity analysis on the hash table and the hash value of the feature data of the source device based on a locality sensitive hashing algorithm to determine replacement data of the feature data of the target device from the feature data of the source device.
A sends the distribution probability of the feature data of the source device relative to the target device to B.
And B, calculating the distribution probability of the feature data in the feature data of the source equipment based on a kernel density estimation algorithm.
And B, calculating the ratio of the distribution probability of the feature data of the source device relative to the target device and the distribution probability of the feature data relative to the source device, and determining the ratio as the weight of the source device relative to the target device.
And 206, training a regression model according to the weight of the feature data of the source equipment relative to the target equipment and the historical smoke content corresponding to the feature data of the source equipment, and determining the trained regression model as a smoke oxygen content prediction soft measurement model which indicates the relationship between the feature data of the target equipment and the smoke oxygen content.
B, training a regression model according to the weight of the feature data of the source equipment relative to the target equipment and the historical smoke content corresponding to the feature data of the source equipment, and sending the trained regression model serving as a smoke oxygen content prediction soft measurement model to A.
And step 207, predicting the oxygen content of the smoke of the target equipment according to the smoke oxygen content prediction soft measurement model.
And A receives the smoke oxygen content prediction soft measurement model sent by B, and predicts the smoke oxygen content of the target equipment by using the smoke oxygen content prediction soft measurement model.
According to the technical scheme, the beneficial effects of the embodiment are as follows: according to the data distribution of the feature data of the two nodes in the Internet of things, the weight of the feature data of the source equipment is calculated based on the hash value and the hash table, so that the feature data of the source equipment is migrated to the target equipment, and the data between the target equipment and the source equipment cannot leave the edge node of the data in the Internet of things, so that the privacy of the data is guaranteed.
Based on the same concept as the method for predicting the oxygen content in the flue gas provided by the embodiment of the method of the present invention, referring to fig. 3, the present invention implements a flue gas oxygen content prediction apparatus, which is applied to an edge node corresponding to a boiler to be predicted, and includes:
a data obtaining module 301, configured to obtain feature data of a source device and feature data of a target device, where the feature data of the source device is non-shared data;
a distribution calculation module 302, configured to calculate, according to a hash table generated by the feature data of the target device, data distribution of the feature data of the source device with respect to the target device;
a weight determining module 303, configured to determine a weight of the feature data of the source device relative to the target device according to a data distribution of the feature data of the source device relative to the target device;
a model determining module 304, configured to establish a relationship between a regression model and a smoke oxygen content according to the weight of the feature data of the source device relative to the target device and a historical smoke content corresponding to the feature data of the source device, so as to obtain a smoke oxygen content prediction soft measurement model;
and the prediction module 305 is configured to predict the oxygen content of the flue gas of the target device according to the flue gas oxygen content prediction soft measurement model.
In an embodiment of the present invention, the distribution calculating module 302 includes: a hash value calculation unit and a distribution calculation unit; wherein,
the hash value calculation unit is used for calculating the hash value of the feature data of the source equipment;
and the distribution calculation unit is used for determining the distribution probability of the feature data of the source device relative to the target device according to a hash table and the hash value generated by the feature data of the target device, and determining the distribution probability as the data distribution of the feature data of the source device relative to the target device.
In an embodiment of the present invention, the distribution calculating unit includes: a retrieval subunit and a probability determination subunit; wherein,
the retrieval subunit is configured to determine, according to a hash table generated by the feature data of the target device and the hash value, replacement data of the feature data of the source device from the feature data of the target device;
the probability determining subunit is configured to determine, as the distribution probability of the feature data of the source device with respect to the target device, the distribution probability of the replacement data in the feature data of the target device.
In an embodiment of the present invention, the retrieving subunit is configured to perform, based on a locality sensitive hashing algorithm, a similarity analysis on a hash table generated according to the feature data of the target device and a hash value of the feature data of the source device, so as to determine, from the feature data of the target device, replacement data of the feature data of the source device.
In an embodiment of the present invention, the weight determining module 302 includes: a probability determination unit and a weight determination unit; wherein,
the probability determination unit is used for determining the distribution probability of the feature data of the source equipment;
the weight determination unit is configured to determine a ratio of a distribution probability of the feature data of the source device with respect to the target device to a distribution probability of the feature data of the source device as a weight of the feature data of the source device with respect to the target device.
In an embodiment of the present invention, the probability determination unit is configured to determine a probability density model of the feature data of the source device based on a non-parameter estimation method; and determining the distribution probability of the feature data of the source equipment according to the probability density model.
In an embodiment of the present invention, the model determining module 304 is configured to perform training of a regression model according to the weight of the feature data of the source device relative to the target device and the historical smoke content corresponding to the feature data of the source device, and determine the trained regression model as a smoke oxygen content prediction soft measurement model, where the smoke oxygen content prediction soft measurement model indicates a relationship between the feature data of the target device and the smoke oxygen content.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device includes a processor 401 and a memory 402 storing execution instructions, and optionally an internal bus 403 and a network interface 404. The Memory 402 may include a Memory 4021, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory 4022 (e.g., at least 1 disk Memory); the processor 401, the network interface 404, and the memory 402 may be connected to each other by an internal bus 403, and the internal bus 403 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like; the internal bus 403 may be divided into an address bus, a data bus, a control bus, etc., which is indicated by only one double-headed arrow in fig. 4 for convenience of illustration, but does not indicate only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 401 executes execution instructions stored by the memory 402, the processor 401 performs the method in any of the embodiments of the present invention and at least is used to perform the method as shown in fig. 1 or fig. 2.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the execution instruction, and can also obtain the corresponding execution instruction from other equipment so as to form a prediction device of the oxygen content of the smoke on a logic level. The processor executes the execution instruction stored in the memory, so that the prediction method of the oxygen content of the flue gas provided by any embodiment of the invention is realized through the executed execution instruction.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention further provide a computer-readable storage medium, which includes an execution instruction, and when a processor of an electronic device executes the execution instruction, the processor executes a method provided in any one of the embodiments of the present invention. The electronic device may specifically be the electronic device shown in fig. 4; the execution instruction is a computer program corresponding to the prediction method of the oxygen content of the smoke.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The multiple embodiments of the present invention are described in a progressive manner, and the same and similar parts among the multiple embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A method for predicting oxygen content of flue gas is characterized by comprising the following steps:
acquiring feature data of source equipment and feature data of target equipment, wherein the feature data of the source equipment is unshared data;
calculating the data distribution of the feature data of the source equipment relative to the target equipment according to a hash table generated by the feature data of the target equipment;
determining the weight of the feature data of the source device relative to the target device according to the data distribution of the feature data of the source device relative to the target device;
establishing a relation between a regression model and the oxygen content of the flue gas according to the weight of the characteristic data of the source equipment relative to the target equipment and the historical flue gas content corresponding to the characteristic data of the source equipment to obtain a flue gas oxygen content prediction soft measurement model;
and predicting the oxygen content of the smoke of the target equipment according to the soft measurement model for predicting the oxygen content of the smoke.
2. The method of claim 1, wherein the calculating the data distribution of the feature data of the source device relative to the target device from the hash table generated from the feature data of the target device comprises:
calculating a hash value of the feature data of the source device;
and determining the distribution probability of the feature data of the source equipment relative to the target equipment according to a hash table and the hash value generated by the feature data of the target equipment, and determining the distribution probability as the data distribution of the feature data of the source equipment relative to the target equipment.
3. The method of claim 2, wherein determining the probability of the distribution of the feature data of the source device with respect to the target device according to the hash table and the hash value generated from the feature data of the target device comprises:
determining replacement data of the feature data of the source device from the feature data of the target device according to a hash table generated by the feature data of the target device and the hash value;
and determining the distribution probability of the replacement data in the feature data of the target device as the distribution probability of the feature data of the source device relative to the target device.
4. The method according to claim 3, wherein the determining replacement data of the feature data of the source device from the feature data of the target device according to the hash table and the hash value generated from the feature data of the target device comprises:
and based on a locality sensitive hashing algorithm, performing similarity analysis on a hash table generated according to the feature data of the target device and the hash value of the feature data of the source device to determine replacement data of the feature data of the source device from the feature data of the target device.
5. The method of claim 2, wherein determining the weight of the feature data of the source device relative to the target device according to the data distribution of the feature data of the source device relative to the target device comprises:
determining a distribution probability of the feature data of the source device;
and determining the ratio of the distribution probability of the feature data of the source device relative to the target device and the distribution probability of the feature data of the source device as the weight of the feature data of the source device relative to the target device.
6. The method of claim 5, wherein determining the distribution probability of the feature data of the source device comprises:
determining a probability density model of the feature data of the source device based on a nonparametric estimation method;
and determining the distribution probability of the feature data of the source equipment according to the probability density model.
7. The method of claim 1, wherein the establishing a relationship between a regression model and the flue gas oxygen content according to the weight of the characteristic data of the source device relative to the target device and the historical flue gas content corresponding to the characteristic data of the source device to obtain a flue gas oxygen content prediction soft measurement model comprises:
and training a regression model according to the weight of the feature data of the source equipment relative to the target equipment and the historical smoke content corresponding to the feature data of the source equipment, and determining the trained regression model as a smoke oxygen content prediction soft measurement model which indicates the relationship between the feature data of the target equipment and the smoke oxygen content.
8. A prediction device of oxygen content of flue gas is characterized by comprising:
the data acquisition module is used for acquiring the feature data of source equipment and the feature data of target equipment, wherein the feature data of the source equipment is non-shared data;
the distribution calculation module is used for calculating the data distribution of the characteristic data of the source equipment relative to the target equipment according to a hash table generated by the characteristic data of the target equipment;
the weight determining module is used for determining the weight of the feature data of the source equipment relative to the target equipment according to the data distribution of the feature data of the source equipment relative to the target equipment;
the model determining module is used for establishing a relation between a regression model and the oxygen content of the smoke according to the weight of the characteristic data of the source equipment relative to the target equipment and the historical smoke content corresponding to the characteristic data of the source equipment to obtain a soft measurement model for predicting the oxygen content of the smoke;
and the prediction module is used for predicting the oxygen content of the smoke of the target equipment according to the soft measurement model for predicting the oxygen content of the smoke.
9. A readable medium comprising executable instructions which, when executed by a processor of an electronic device, cause the electronic device to perform the method of any of claims 1 to 7.
10. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of any of claims 1-7 when the processor executes the execution instructions stored by the memory.
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