Intelligent control method and device for lubricating grease production equipment and medium

Abstract

The application provides an intelligent control method and device for a lubricating grease production equipment and a medium, and the method comprises the following steps: performing prediction processing on target environment parameter information of the lubricating grease production equipment in a target time period, so as to determine predicted environment parameter information and predicted suction power in a future time period; according to actual environment parameter information of the lubricating grease production equipment in any sub time period in the future time period and the predicted environment parameter information in the sub time period, an environment parameter matching degree corresponding to the sub time period is determined; if the environment parameter matching degree corresponding to the sub time period is greater than or equal to a matching degree threshold, the suction power of a suction equipment in a next sub time period of the sub time period is adjusted to the predicted suction power corresponding to the next sub time period of the sub time period; otherwise, the suction power of the suction equipment in the next sub time period of the sub time period is adjusted to a preset suction power, so that the suction power of the suction equipment is dynamically adjusted.

Description

Technical Field

[0001] This invention relates to the field of production equipment, and in particular to an intelligent control method, equipment and medium for lubricating grease production equipment. Background Technology

[0002] Lubricating grease, also known as butter, is a ternary semi-solid lubricant composed of base oil (mineral oil or synthetic oil), thickener (soap-based or non-soap-based), and additives. It is suitable for lubricating bearings, gears, and other mechanical components. During its production, the heating process in the reaction vessel generates process waste gas. Therefore, current lubricating grease production equipment is equipped with a waste gas collection system. This system absorbs and purifies the generated waste gas to reduce environmental pollution caused by lubricating grease production. Existing waste gas collection systems typically have a fixed suction power setting for the suction equipment (used to absorb the waste gas generated during lubricating grease production). However, in actual production scenarios, abnormalities in certain lubricating grease production parameters may increase the amount of waste gas generated. If the suction equipment continues to operate at the fixed suction power, the waste gas absorption rate may decrease, leading to environmental pollution in the production workshop. Therefore, it is necessary to predict the amount of waste gas generated during lubricating grease production and dynamically adjust the suction power of the suction equipment to improve the waste gas absorption rate. Summary of the Invention

[0003] To address the aforementioned technical problems, the technical solution adopted by this invention is as follows:

[0004] According to one aspect of this application, an intelligent control method for a grease production equipment is provided, comprising:

[0005] Step S100: Based on the target environmental parameter information of the grease production equipment within the target time period, predictive processing is performed to determine the predicted environmental parameter information of the grease production equipment in the future time period and the predicted suction power of the suction equipment of the grease production equipment in the future time period; the duration of the target time period is a preset first duration; the end time of the target time period is the current time; the start time of the target time period is after the start time of the saponification reaction stage in the grease production process of the grease production equipment; the duration of the future time period is a preset second duration; the second duration is less than or equal to the first duration; the start time of the future time period is the time after the current time.

[0006] Step S200: Based on the actual environmental parameter information of the lubricating grease production equipment in any sub-time period within a future time period and the predicted environmental parameter information in that sub-time period, determine the environmental parameter matching degree corresponding to that sub-time period.

[0007] Step S300: If the matching degree of the environmental parameters corresponding to the sub-time period is greater than or equal to the preset matching degree threshold, then the inhalation power of the inhalation device in the next sub-time period of the sub-time period is adjusted to the predicted inhalation power corresponding to the next sub-time period of the sub-time period.

[0008] If the environmental parameter matching degree corresponding to the sub-time period is less than the preset matching degree threshold, the suction power of the suction device in the next sub-time period of the sub-time period will be adjusted to the preset suction power.

[0009] According to another aspect of this application, a non-transitory computer-readable storage medium is provided, wherein at least one instruction or at least one program is stored in the storage medium, the at least one instruction or the at least one program being loaded and executed by a processor to implement the aforementioned intelligent control method for grease production equipment.

[0010] According to another aspect of this application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.

[0011] The present invention has at least the following beneficial effects:

[0012] The intelligent control method for a grease production equipment of the present invention, within a target time period of the saponification reaction stage in the grease production process, predicts the target environmental parameters of the grease production equipment within the target time period to determine the predicted environmental parameters and the predicted suction power of the suction device within the future time period. Based on the actual environmental parameters and the predicted environmental parameters within any sub-time period within the future time period, the method determines the environmental parameter matching degree for that sub-time period. The environmental parameter matching degree represents the difference between the actual and predicted environmental parameters of the grease production equipment within the corresponding sub-time period. If the environmental parameter matching degree for that sub-time period is greater than or equal to a preset matching degree threshold, it indicates that the actual and predicted environmental parameters of the grease production equipment within that sub-time period are similar. If the differences between environmental parameter information are small, it can be assumed that the predicted environmental parameter information within this sub-time period is relatively close to the actual situation. Therefore, it can also be assumed that the predicted suction power of the next sub-time period predicted based on the predicted environmental parameter information within this sub-time period is also relatively close to the actual situation. In this case, the suction power of the suction equipment in the next sub-time period of this sub-time period will be adjusted to the predicted suction power corresponding to the next sub-time period. Conversely, if the matching degree of environmental parameters corresponding to this sub-time period is less than the preset matching degree threshold, it indicates that there is a large difference between the actual and predicted environmental parameter information of the grease production equipment within this sub-time period. Therefore, in order to improve the exhaust gas absorption rate, it is necessary to adjust the suction power of the suction equipment in the next sub-time period of this sub-time period to the preset suction power, so as to achieve dynamic adjustment of the suction power of the suction equipment and balance the suction power of the suction equipment and the exhaust gas absorption rate of the production environment. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart of an intelligent control method for a grease production equipment provided in an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This application proposes an intelligent control method for grease production equipment, such as... Figure 1 As shown, it includes:

[0017] Step S100: Based on the target environmental parameter information of the grease production equipment in the target time period, perform prediction processing to determine the predicted environmental parameter information of the grease production equipment in the future time period and the predicted suction power of the suction equipment of the grease production equipment in the future time period.

[0018] The target time period is the preset first duration; the end time of the target time period is the current time; the start time of the target time period is after the start time of the saponification reaction stage in the grease production process of the grease production equipment.

[0019] The duration of the future time period is the preset second duration; the second duration is less than or equal to the first duration; the start time of the future time period is the time after the current time.

[0020] The target environmental parameter information refers to the environmental parameter information of the grease production equipment within the target time period. The environmental parameter information can be the parameters that affect the generation of exhaust gas during the saponification reaction stage of the grease production equipment, such as the temperature parameters inside the reactor and the viscosity parameters of the produced grease.

[0021] Furthermore, step S100 includes steps S110-S120:

[0022] Step S110: Obtain the target environmental parameter information of the lubricating grease production equipment within the target time period to obtain the target environmental parameter information list A=(A1,A2,...,A...). i ,...,A m ); where i=1,2,...,m; m is the number of information collection moments within the target time period; the duration between any two adjacent information collection moments within the target time period is equal; A i This refers to the target environmental parameter information corresponding to the i-th information collection time point within the target time period for the lubricating grease production equipment.

[0023] Step S120: Input the target environmental parameter information list A into the preset data prediction model to obtain the predicted environmental parameter information list B and the predicted inhalation power list C output by the data prediction model;

[0024] Among them, the predicted environmental parameter information list B=(B1,B2,...,B j ,...,B k ); j=1,2,...,k; k is the number of information collection moments within the future time period; the duration between any two adjacent information collection moments within the future time period is equal to the duration between any two adjacent information collection moments within the target time period; B j This refers to the predicted environmental parameter information for the j-th information collection time within a future time period for the lubricating grease production equipment.

[0025] Predicted inspiratory power list C=(C1,C2,...,C j ,...,C k );C j This is the predicted suction power of the suction device of the lubricating grease production equipment at the j-th information collection time in a future time period.

[0026] Predicted environmental parameter information refers to the environmental parameter information for future time periods predicted based on the target environmental parameter information within the target time period.

[0027] Predicted inhalation power is the inhalation power that the inhalation device should generate in the future time period, based on the target environmental parameter information within the target time period.

[0028] The data prediction model is determined according to steps S121-S124:

[0029] Step S121: Obtain historical environmental parameter information of the lubricating grease production equipment within several first historical time periods to obtain a list of several first historical environmental parameter information D1, D2, ..., D e ,...,D f Where e = 1, 2, ..., f; f is the number of the first historical time period; D e This is a list of historical environmental parameter information for the grease production equipment during the e-th first historical time period.

[0030] The first historical time period is the historical time period within the saponification reaction stage of the grease production process in the grease production equipment; the duration of the first historical time period is equal to the duration of the target time period; the end time of the first historical time period is before the start time of the target time period.

[0031] Historical environmental parameter information refers to the actual environmental parameters of the grease production equipment during a historical period.

[0032] D e =(D e1 D e2,...,D ei ,...,D em );D ei This refers to the historical environmental parameter information of the lubricating grease production equipment at the i-th information collection time within the e-th first historical time period;

[0033] Step S122: Obtain historical environmental parameter information of the lubricating grease production equipment within several second historical time periods to obtain a list of several second historical environmental parameter information E1, E2, ..., E e ,...,E f Among them, E e This is a list of historical environmental parameter information for the grease production equipment during the e-th second historical time period.

[0034] The duration of the second historical time period is equal to the duration of the future time period; the start time of the e-th second historical time period is the time after the end time of the e-th first historical time period.

[0035] E e =(E e1 E e2 ,...,E ej ,...,E ek ); E ej This refers to the historical environmental parameter information of the lubricating grease production equipment at the j-th information collection time within the e-th second historical time period;

[0036] Step S123: Obtain the historical suction power of the suction equipment of the lubricating grease production equipment in each second historical time period to obtain several historical suction power lists F1, F2, ..., F e ,...,F f Among them, F e This is a list of historical inhalation power for the inhalation device during the eth second historical time period.

[0037] Historical inhalation power is the actual inhalation power of the inhalation device during the corresponding historical time period.

[0038] F e =(F e1 ,F e2 ,...,F ej ,...,F ek );F ej The historical inhalation power of the inhalation device at the j-th information collection time within the e-th second historical time period;

[0039] Step S124, D e As input samples, E e and F eAs output labels, supervised training is performed on a pre-defined neural network model to obtain a data prediction model.

[0040] The neural network model in this application can be a recurrent neural network model (such as an LSTM model, Long Short-Term Memory), and the method for training samples can be any existing supervised sample training method.

[0041] Step S200: Based on the actual environmental parameter information of the lubricating grease production equipment in any sub-time period within a future time period and the predicted environmental parameter information in that sub-time period, determine the environmental parameter matching degree corresponding to that sub-time period.

[0042] Actual environmental parameter information refers to the real environmental parameter information of the lubricating grease production equipment within the corresponding sub-time period.

[0043] Furthermore, step S200 includes steps S210-S250:

[0044] Step S210: Divide the future time period into several sub-time periods;

[0045] Each sub-time period has the same duration.

[0046] Step S220: When the end time of any sub-time period is reached at the current time, the sub-time period is determined as the target sub-time period;

[0047] Step S230: Encode the features of several actual environmental parameter information of the grease production equipment within the target sub-time period to obtain the corresponding actual information vector;

[0048] Step S240: Encode the features of several predicted environmental parameters of the grease production equipment within the target sub-time period to obtain the corresponding predicted information vector;

[0049] The existing vector feature encoding method can be used to encode environmental parameter information.

[0050] Step S250: The matching degree between the actual information vector and the predicted information vector corresponding to the target sub-time period is determined as the matching degree of the environmental parameters corresponding to the target sub-time period.

[0051] The matching degree between the actual information vector and the predicted information vector can be obtained using existing methods for determining vector matching degree, such as calculating the cosine distance between the actual information vector and the predicted information vector, and determining the matching degree between the two using the obtained cosine distance.

[0052] Step S300: If the matching degree of the environmental parameters corresponding to the sub-time period is greater than or equal to the preset matching degree threshold, then the inhalation power of the inhalation device in the next sub-time period of the sub-time period is adjusted to the predicted inhalation power corresponding to the next sub-time period of the sub-time period.

[0053] If the environmental parameter matching degree corresponding to the sub-time period is less than the preset matching degree threshold, the suction power of the suction device in the next sub-time period of the sub-time period will be adjusted to the preset suction power.

[0054] Specifically, step S300 includes steps S310-S322:

[0055] Step S310: If the matching degree of environmental parameters corresponding to the target sub-time period is greater than or equal to the preset matching degree threshold, then control the inhalation power of the inhalation device at each information collection moment in the next sub-time period of the target sub-time period to be the predicted inhalation power corresponding to each information collection moment.

[0056] If the matching degree of the environmental parameters corresponding to the target sub-time period is greater than or equal to the preset matching degree threshold, it means that the difference between the actual environmental parameter information and the predicted environmental parameter information of the grease production equipment in the target sub-time period is not large. It can be considered that the predicted environmental parameter information in the target sub-time period is more in line with the actual situation. Therefore, it can also be considered that the predicted suction power of the next sub-time period predicted based on the predicted environmental parameter information in the target sub-time period is also more in line with the actual situation. Then, the suction power of the suction equipment in the next sub-time period of the target sub-time period is adjusted to the predicted suction power corresponding to the next sub-time period of the target sub-time period.

[0057] Step S320: If the environmental parameter matching degree corresponding to the target sub-time period is less than the preset matching degree threshold, then obtain the predicted inhalation power of the inhalation device at each information collection time in the next sub-time period after the target sub-time period, so as to obtain the inhalation power list G=(G1,G2,...,G a ,...,G b ); where a=1,2,...,b; b is the number of information collection moments in the next sub-time period after the target sub-time period; G a The predicted inspiratory power of the inspiratory device at the a-th information collection time in the next sub-time period after the target sub-time period;

[0058] Step S321: Based on the sub-time period inhalation power list G, determine the corrected inhalation power corresponding to each information collection moment of the inhalation device in the next sub-time period after the target sub-time period.

[0059] Among them, the corrected inhalation power H of the inhalation device at the a-th information acquisition time in the subsequent sub-time period of the target sub-time period. a =y×G a ;

[0060] y is a preset correction coefficient, y > 1;

[0061] Step S322: Control the inhalation power of the inhalation device at each information acquisition moment in the next sub-time period after the target sub-time period to be the corrected inhalation power corresponding to each information acquisition moment.

[0062] If the matching degree of environmental parameters corresponding to the target sub-time period is less than the preset matching degree threshold, it indicates that there is a large difference between the actual environmental parameter information and the predicted environmental parameter information of the grease production equipment in the target sub-time period. Therefore, in order to improve the exhaust gas absorption rate, it is necessary to uniformly increase the suction power of the suction equipment in the next sub-time period after the target sub-time period to achieve dynamic adjustment of the suction power of the suction equipment.

[0063] The intelligent control method for a grease production equipment of the present invention, within a target time period of the saponification reaction stage in the grease production process, predicts the target environmental parameters of the grease production equipment within the target time period to determine the predicted environmental parameters and the predicted suction power of the suction device within the future time period. Based on the actual environmental parameters and the predicted environmental parameters within any sub-time period within the future time period, the method determines the environmental parameter matching degree for that sub-time period. The environmental parameter matching degree represents the difference between the actual and predicted environmental parameters of the grease production equipment within the corresponding sub-time period. If the environmental parameter matching degree for that sub-time period is greater than or equal to a preset matching degree threshold, it indicates that the actual and predicted environmental parameters of the grease production equipment within that sub-time period are similar. If the differences between environmental parameter information are small, it can be assumed that the predicted environmental parameter information within this sub-time period is relatively close to the actual situation. Therefore, it can also be assumed that the predicted suction power of the next sub-time period predicted based on the predicted environmental parameter information within this sub-time period is also relatively close to the actual situation. In this case, the suction power of the suction equipment in the next sub-time period of this sub-time period will be adjusted to the predicted suction power corresponding to the next sub-time period. Conversely, if the matching degree of environmental parameters corresponding to this sub-time period is less than the preset matching degree threshold, it indicates that there is a large difference between the actual and predicted environmental parameter information of the grease production equipment within this sub-time period. Therefore, in order to improve the exhaust gas absorption rate, it is necessary to adjust the suction power of the suction equipment in the next sub-time period of this sub-time period to the preset suction power, so as to achieve dynamic adjustment of the suction power of the suction equipment and balance the suction power of the suction equipment and the exhaust gas absorption rate of the production environment.

[0064] Embodiments of the present invention also provide a computer program product including program code, which, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above in various exemplary embodiments of the present invention.

[0065] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0066] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0067] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0068] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented in the following forms: entirely in hardware, entirely in software (including firmware, microcode, etc.), or in a combination of hardware and software, collectively referred to herein as “circuit,” “module,” or “system.”

[0069] An electronic device according to this embodiment of the invention. The electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the invention.

[0070] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0071] The storage device stores program code that can be executed by the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of the present invention.

[0072] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0073] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0074] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0075] Electronic devices can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable users to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed through input / output (I / O) interfaces. Furthermore, electronic devices can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters.

[0076] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0077] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0078] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0079] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0080] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0081] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0082] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0083] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An intelligent control method for a grease production equipment, characterized in that, include: Step S100: Based on the target environmental parameter information of the grease production equipment within the target time period, predictive processing is performed to determine the predicted environmental parameter information of the grease production equipment in the future time period and the predicted suction power of the suction device of the grease production equipment in the future time period; the duration of the target time period is a preset first duration; the end time of the target time period is the current time; the start time of the target time period is after the start time of the saponification reaction stage in the grease production process of the grease production equipment; the duration of the future time period is a preset second duration; the second duration is less than or equal to the first duration; the start time of the future time period is the time after the current time. Step S200: Based on the actual environmental parameter information of the lubricating grease production equipment in any sub-time period within the future time period and the predicted environmental parameter information in that sub-time period, determine the environmental parameter matching degree corresponding to that sub-time period. Step S300: If the matching degree of the environmental parameters corresponding to the sub-time period is greater than or equal to the preset matching degree threshold, then the inhalation power of the inhalation device in the next sub-time period of the sub-time period is adjusted to the predicted inhalation power corresponding to the next sub-time period of the sub-time period. If the environmental parameter matching degree corresponding to the sub-time period is less than the preset matching degree threshold, the air intake power of the air intake device in the next sub-time period of the sub-time period will be adjusted to the preset air intake power.

2. The method according to claim 1, characterized in that, Step S100 includes: Step S110: Obtain the target environmental parameter information of the lubricating grease production equipment within the target time period to obtain the target environmental parameter information list A=(A1,A2,...,A...). i ,...,A m ); where i = 1, 2, ..., m; m is the number of information collection moments within the target time period; the duration between any two adjacent information collection moments within the target time period is equal; A i This refers to the target environmental parameter information corresponding to the i-th information collection time point within the target time period for the lubricating grease production equipment. Step S120: Input the target environmental parameter information list A into the preset data prediction model to obtain the predicted environmental parameter information list B and the predicted inhalation power list C output by the data prediction model; Among them, the predicted environmental parameter information list B=(B1,B2,...,B j ,...,B k ); j=1,2,...,k; k is the number of information collection moments within the future time period; the duration between any two adjacent information collection moments within the future time period is equal to the duration between any two adjacent information collection moments within the target time period; B j The predicted environmental parameter information for the lubricating grease production equipment at the j-th information collection time within the future time period; Predicted inspiratory power list C=(C1,C2,...,C j ,...,C k );C j The predicted suction power of the suction device of the lubricating grease production equipment at the j-th information collection time within the future time period.

3. The method according to claim 2, characterized in that, The data prediction model is determined according to the following steps: Step S121: Obtain historical environmental parameter information of the lubricating grease production equipment within several first historical time periods to obtain a list of several first historical environmental parameter information D1, D2, ..., D e ,...,D f Where e = 1, 2, ..., f; f is the number of the first historical time period; D e This is a list of historical environmental parameter information for the grease production equipment during the e-th first historical time period. The first historical time period is the historical time period within the saponification reaction stage of the lubricating grease production process in the lubricating grease production equipment; the duration of the first historical time period is equal to the duration of the target time period; the end time of the first historical time period is before the start time of the target time period. D e =(D e1 D e2 ,...,D ei ,...,D em );D ei This refers to the historical environmental parameter information of the lubricating grease production equipment at the i-th information collection time within the e-th first historical time period; Step S122: Obtain historical environmental parameter information of the lubricating grease production equipment within several second historical time periods to obtain a list of several second historical environmental parameter information E1, E2, ..., E e ,...,E f Among them, E e This is a list of historical environmental parameter information for the grease production equipment during the e-th second historical time period. The duration of the second historical time period is equal to the duration of the future time period; the start time of the e-th second historical time period is the time after the end time of the e-th first historical time period; E e =(E e1 E e2 ,...,E ej ,...,E ek ); E ej This refers to the historical environmental parameter information of the lubricating grease production equipment at the j-th information collection time within the e-th second historical time period; Step S123: Obtain the historical suction power of the suction equipment of the lubricating grease production equipment within each second historical time period to obtain several historical suction power lists F1, F2, ..., F e ,...,F f Among them, F e This is a list of historical inhalation power of the inhalation device during the e-th second historical time period; F e =(F e1 ,F e2 ,...,F ej ,...,F ek );F ej The historical inhalation power of the inhalation device at the j-th information collection time within the e-th second historical time period; Step S124, D e As input samples, E e and F e As output labels, a pre-defined neural network model is subjected to supervised training to obtain the data prediction model.

4. The method according to claim 3, characterized in that, Step S200 includes: Step S210: Divide the future time period into several sub-time periods; each sub-time period has an equal duration. Step S220: When the end time of any sub-time period is reached at the current time, the sub-time period is determined as the target sub-time period; Step S230: Encode the features of several actual environmental parameter information of the grease production equipment during the target sub-time period to obtain the corresponding actual information vector; Step S240: Encode the features of several predicted environmental parameter information of the grease production equipment within the target sub-time period to obtain the corresponding predicted information vector; Step S250: The matching degree between the actual information vector and the predicted information vector corresponding to the target sub-time period is determined as the matching degree of the environmental parameters corresponding to the target sub-time period.

5. The method according to claim 4, characterized in that, Step S300 includes: Step S310: If the environmental parameter matching degree corresponding to the target sub-time period is greater than or equal to the preset matching degree threshold, then control the inhalation power of the inhalation device at each information collection moment in the next sub-time period after the target sub-time period to be the predicted inhalation power corresponding to each information collection moment.

6. The method according to claim 5, characterized in that, Step S300 further includes: Step S320: If the environmental parameter matching degree corresponding to the target sub-time period is less than a preset matching degree threshold, then obtain the predicted inhalation power of the inhalation device at each information collection time in the subsequent sub-time period of the target sub-time period, so as to obtain a sub-time period inhalation power list G=(G1,G2,...,G...). a ,...,G b ); where a=1,2,...,b; b is the number of information collection moments in the next sub-time period after the target sub-time period; G a The predicted inhalation power of the inhalation device at the a-th information collection time in the subsequent sub-time period of the target sub-time period; Step S321: Based on the sub-time period inhalation power list G, determine the corrected inhalation power of the inhalation device at each information collection time in the next sub-time period after the target sub-time period; Wherein, the corrected inhalation power H of the inhalation device corresponds to the a-th information acquisition time in the subsequent sub-time period of the target sub-time period. a =y×G a ; y is a preset correction coefficient, y > 1; Step S322: Control the inhalation power of the inhalation device at each information acquisition moment in the next sub-time period after the target sub-time period to be the corrected inhalation power corresponding to each information acquisition moment.

7. A non-transitory computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program segment, which is loaded and executed by a processor to implement the method as described in any one of claims 1-6.

8. An electronic device, characterized in that, Includes a processor and the non-transitory computer-readable storage medium as described in claim 7.

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