Energy scheduling system and energy consumption optimization method for smart quartz processing factory

Abstract

An energy scheduling system and energy consumption optimization method for a smart quartz processing factory. The method comprises: collecting historical energy consumption data of each annealing furnace device in a factory, and establishing an annealing temperature dataset required for quartz devices to be processed on that day; performing matching in the annealing temperature dataset to obtain a target operating temperature required for the first run of each annealing furnace device on that day; dynamically scheduling energy of the annealing furnace devices on the basis of an energy scheduling strategy; and transmitting annealing furnace device information matched with said quartz devices to a factory workshop where the quartz devices are located, and notifying quartz device transportation personnel in the factory to transport the quartz devices to the matching annealing furnace devices. The method solves the problem that a quartz processing factory needs to consume a large amount of time and energy before daily operation to heat annealing furnaces from room temperature to target processing temperatures or adjust between different target processing temperatures.

Description

Energy scheduling system and energy consumption optimization method for intelligent quartz processing plant TECHNICAL FIELD

[0001] The present application relates to the technical field of energy consumption monitoring, in particular to an energy scheduling system and an energy consumption optimization method for an intelligent quartz processing plant. BACKGROUND

[0002] A quartz processing plant refers to a plant that extracts quartz raw materials from quartz ore and produces various quartz products through a series of processing technologies; quartz processing usually requires high-temperature melting, drying, annealing or sintering, etc. These high-temperature processing processes consume a large amount of energy.

[0003] The annealing furnace of a quartz processing plant is a device used for heat treatment of quartz products. Annealing is a heat treatment process that controls the heating time and temperature to ensure that the quartz products achieve the desired heat treatment effect during the annealing process. After annealing, the chemical and physical properties of the quartz products can be improved, internal stress can be reduced, and stability and durability can be improved. In this processing technology, the annealing furnace plays a very important role in production and is the key to quality control and performance improvement of quartz products. In existing production and processing, the annealing furnace needs to consume a large amount of time and energy to raise the temperature from room temperature to the target processing temperature or to adjust between different target processing temperatures required by different quartz devices before starting work every day. How to optimize the production scheduling strategy and reduce energy waste generated by the annealing furnace is an important problem that needs to be solved in the quartz processing industry.

[0004] In the existing disclosed invention technology, a method for detecting quartz glass defects based on multivariate fine burning energy consumption optimization is disclosed in Chinese Patent No. CN117705827A, which includes obtaining historical fine burning data of quartz glass, constructing a fine burning energy consumption model of quartz glass, and solving specific fine burning energy consumption model parameters through historical fine burning data; dividing the fine burning task of quartz glass into multiple fine burning batches, and substituting the fine burning task data into the fine burning energy consumption model to calculate the fine burning parameters of the fine burning task; detecting the quality of each fine burning batch of quartz glass, constructing an appearance defect evaluation index of quartz glass, and adjusting the fine burning parameters during the fine burning of quartz glass, and detecting the physical property defects of quartz glass.

[0005] The above-mentioned patent lacks real-time monitoring and adjustment of energy consumption in the quartz processing process, which leads to the inability to timely reflect and adjust the energy consumption fluctuation in actual production, requiring a large amount of time and energy. SUMMARY

[0006] The technical problem to be solved by the present application is that in the prior art, a quartz processing factory needs to consume a large amount of time and energy when an annealing furnace is heated from room temperature to a target processing temperature or adjusted between different target processing temperatures before starting work every day.

[0007] In order to achieve the above-mentioned purpose, the technical scheme of the energy consumption optimization method for the intelligent quartz processing factory of the present application comprises the following steps: S1: collecting historical energy consumption data of each annealing furnace equipment in the factory, and calculating the energy consumption rate of each annealing furnace equipment; S2: extracting the annealing temperature value required by the quartz device to be processed in the factory on the same day, and sequentially arranging the annealing temperature value from large to small to form an annealing temperature data set required by the quartz device to be processed on the same day; S3: matching the target working temperature required by each annealing furnace equipment to be reached for the first time on the same day in the annealing temperature data set according to the energy consumption rate of each annealing furnace equipment; S4: when the furnace temperature of the annealing furnace equipment reaches and stably maintains the target working temperature, continue to match the annealing temperature value required by the next quartz device to be processed by the annealing furnace equipment in the annealing temperature data set required by the quartz device to be processed on the same day, and dynamically schedule the energy of the annealing furnace equipment according to the energy scheduling strategy; S5: transmitting the annealing furnace equipment information matched by the quartz device to be processed in the step S4 to the factory workshop where the quartz device to be processed is located, and notifying the quartz device transportation personnel in the factory to transport the quartz device to be processed to the matched annealing furnace equipment.

[0008] Specifically, the intelligent quartz processing factory comprises S annealing furnace equipment; in the step S1, the historical energy consumption data of each annealing furnace equipment comprises: in the last working day, the energy consumption of the annealing furnace equipment in the temperature rising working stage is W up , and the average time length of the furnace temperature rising 1 degree is T up ; the historical energy consumption data of each annealing furnace equipment further comprises: in the last working day, the energy consumption of the annealing furnace equipment in the temperature falling working stage is W down , and the average time length of the furnace temperature falling 1 degree is T down .

[0009] Specifically, in the step S1, the energy consumption rate of each annealing furnace equipment comprises a temperature rising energy consumption rate and a temperature falling energy consumption rate; the calculation strategy of the temperature rising energy consumption rate is specifically as follows: The calculation strategy of the temperature falling energy consumption rate is specifically as follows: Wherein, θ s,up , θ s,down are the temperature rising energy consumption rate and the temperature falling energy consumption rate of the s-th annealing furnace equipment; s≤S; Tn T y are the ambient temperature values of the annealing furnace equipment working environment on the day and the last working day, respectively.

[0010] Specifically, the step S3 comprises the following specific steps: S31: extracting the temperature rising energy consumption rate and annealing temperature data set {t1, t2...t d ...t D} of each annealing furnace equipment, and arranging the temperature rising energy consumption rate of each annealing furnace equipment in descending order to form a temperature rising energy consumption rate set {θ 1,up ,θ 2,up ...θ s,up ...θ S,up}; wherein, t d is the required annealing temperature value of the dth quartz device to be processed; t 1,up is the required annealing temperature value of the Dth quartz device to be processed; D is the number of quartz devices to be processed on the day, d≤D; θ D is the temperature rising energy consumption rate of the Sth annealing furnace equipment; S32: extracting the number D of quartz devices to be processed on the day; when D<S, executing step S33; when D≥S, executing step S34; S33: inputting a maximum annealing temperature data matching instruction in the required annealing temperature data set of the quartz devices to be processed on the day, and matching the quartz devices to be processed with the required annealing temperature data t1 to the annealing furnace equipment with the maximum temperature rising energy consumption rate θ s,d , and sequentially matching the required annealing temperature data of the quartz devices to be processed on the day to each annealing furnace equipment, wherein the quartz devices to be processed with the required annealing temperature data t d are matched to the S-Dth annealing furnace equipment; S34: inputting a segmented annealing temperature lookup instruction in the annealing temperature data set of the quartz devices to be processed on the day, and performing matching processing of the target working temperature to be reached by the annealing furnace equipment on the first day of operation; wherein, the annealing temperature data set is divided into X segments, is a down rounding symbol, the maximum annealing temperature data in each segment of the annealing temperature data set is sequentially matched to the S annealing furnace equipment; wherein, the dth quartz device to be processed is matched to the s th annealing furnace equipment for annealing processing; S35: calculating the pre-start time required for the target working temperature to be reached by the annealing furnace equipment on the first day of operation, and performing timing pre-start control of each equipment annealing furnace equipment before the factory on the day.

[0011] Specifically, in the step S35, the calculation strategy of the pre-start time required for the target working temperature to be reached by the annealing furnace equipment on the first day of operation is as follows: μ s,d =|t d -T n |×Tup ; wherein μ s,d is the pre-startup time of the s-th annealing furnace device for the d-th quartz device to be processed.

[0012] Specifically, the step S4 comprises the following specific steps: S41: when the in-furnace temperature of the annealing furnace device reaches and stably maintains the target working temperature t d,mb , match the two annealing temperature values closest to the target working temperature of the annealing furnace device at the moment in the annealing temperature data set required by the quartz devices to be processed on the day, respectively; S42: import the two annealing temperature values matched into the energy scheduling strategy, and calculate the energy scheduling coefficient; wherein the energy scheduling coefficient comprises a heating scheduling coefficient and a cooling scheduling coefficient; the calculation strategy of the heating scheduling coefficient r is: wherein x1, x2 are subscripts and x1, x2 ∈ {1, 2...d...D}; S42: import the two annealing temperature values matched into the energy scheduling strategy, and calculate the energy scheduling coefficient; wherein the energy scheduling coefficient comprises a heating scheduling coefficient and a cooling scheduling coefficient; the calculation strategy of the heating scheduling coefficient r up is: the calculation strategy of the cooling scheduling coefficient r down is: S43: dynamically schedule the energy of the annealing furnace device; when r up ≥ r down , preferentially perform annealing treatment on the quartz devices to be processed with the annealing temperature value t ; when r up < r down , preferentially perform annealing treatment on the quartz devices to be processed with the annealing temperature value t .

[0013] In addition, the energy scheduling system for the intelligent quartz processing factory comprises the following modules: a historical energy consumption data acquisition module, used for acquiring historical energy consumption data of each annealing furnace device in the factory and calculating energy consumption rates of the annealing furnace devices; an annealing temperature data set module, used for extracting annealing temperature values required by quartz devices to be processed on the day and sequentially arranging the annealing temperature values from large to small to form an annealing temperature data set required by the quartz devices to be processed on the day; a first running temperature determination module, used for matching target working temperatures required by each annealing furnace device to be first run on the day in the annealing temperature data set according to the energy consumption rates of the annealing furnace devices; an energy dynamic scheduling module, used for, when the furnace temperature of the annealing furnace device reaches and stably maintains the target working temperature, continuing to match annealing temperature values required by the next quartz device to be processed of the annealing furnace device in the annealing temperature data set required by the quartz devices to be processed on the day and dynamically scheduling energy of the annealing furnace device according to an energy scheduling strategy; and an annealing furnace device information sharing module, used for transmitting annealing furnace device information matched by the quartz device to be processed to a factory workshop where the quartz device to be processed is located and notifying a quartz device transportation personnel in the factory to transport the quartz device to be processed to the matched annealing furnace device.

[0014] The application further discloses a storage medium, wherein instructions are stored in the storage medium, and when a computer reads the instructions, the computer executes the energy consumption optimization method for the intelligent quartz processing factory.

[0015] The application further discloses an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor executes the computer program to realize the energy consumption optimization method for the intelligent quartz processing factory.

[0016] Compared with the prior art, the technical effects of the application are as follows: 1. The application considers the problem that a large amount of time and energy is consumed when the annealing furnace is heated from room temperature to a target processing temperature or adjusted between different target processing temperatures before the quartz processing factory starts work every day, and the energy is dynamically scheduled by matching the target working temperature according to the energy consumption rate of each annealing furnace device, so that the energy consumption can be effectively saved and the energy consumption cost can be reduced.

[0017] 2. The annealing temperature data set is sequentially arranged and dynamically scheduled, so that the waiting time can be effectively reduced, the production efficiency can be improved, and the production capacity can be increased.

[0018] 3. The annealing temperature data set is sequentially arranged and dynamically scheduled, so that the waiting time can be effectively reduced, the production efficiency can be improved, and the production capacity can be increased.

[0019] 4、The quartz device to be processed is matched to the corresponding annealing furnace equipment, and the transport personnel is informed to transport, so that the coordination and efficiency of the transportation process can be improved, the transportation time can be reduced, and the production progress can be ensured. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort. Among them: Fig. 1 is a flowchart of the energy consumption optimization method for the intelligent quartz processing factory of the present application; Fig. 2 is a structural diagram of the energy scheduling system for the intelligent quartz processing factory of the present application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0022] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0024] Embodiment one As shown in Fig. 1, the energy consumption optimization method for the intelligent quartz processing factory of the present application embodiment includes the following specific steps: S1: collecting the historical energy consumption data of each annealing furnace equipment in the factory, and calculating the energy consumption rate of each annealing furnace equipment; In S1, the intelligent quartz processing factory includes S annealing furnace equipment.

[0025] In this embodiment, E quartz devices to be processed are annealed in a working day on each annealing furnace equipment.

[0026] The historical energy consumption data of each annealing furnace equipment includes: in the last working day, the energy consumption of the annealing furnace equipment in the temperature rising working stage is W up , the average time T up used for the temperature rising of 1 degree in the furnace.

[0027] The historical energy consumption data for each annealing furnace also includes: during the cooling phase of the annealing furnace on the previous working day, the energy consumed to reduce the furnace temperature by 1 degree Celsius was W. down The average time T taken for the furnace temperature to decrease by 1 degree Celsius down .

[0028] In S1, the energy consumption rate of each annealing furnace includes: heating energy consumption rate and cooling energy consumption rate.

[0029] The specific strategy for calculating the heating energy consumption rate is as follows: The specific strategy for calculating the cooling energy consumption rate is as follows: Where, θ s,up ,θ s,down Let S and t be the heating energy consumption rate and cooling energy consumption rate of the s-th annealing furnace, respectively, where s ≤ S.

[0030] T n ,T y These are the ambient temperature values ​​of the annealing furnace equipment operating on the current day and the ambient temperature values ​​of the annealing furnace equipment operating on the previous working day, respectively.

[0031] S2: Extract the annealing temperature values ​​required for the quartz devices to be processed in the factory on that day, and arrange the annealing temperature values ​​in descending order to form a dataset of the annealing temperatures required for the quartz devices to be processed on that day.

[0032] S3: Based on the energy consumption rate of each annealing furnace, match the target operating temperature that each annealing furnace needs to achieve for its first operation of the day in the annealing temperature dataset.

[0033] S31: Extract the heating energy consumption rate and annealing temperature dataset {t1,t2...t} of each annealing furnace. d ...t D The heating energy consumption rates of each annealing furnace are arranged in descending order to form a heating energy consumption rate set {θ}. 1,up ,θ 2,up ...θ s,up ...θ S,up}; where t d is the required annealing temperature for the d-th quartz device to be processed; is the required annealing temperature for the D-th quartz device to be processed; D is the number of quartz devices to be processed on that day, d≤D; is the heating energy consumption rate of the S-th annealing furnace.

[0034] S32: Extract the number D of quartz devices to be processed on the same day; When D < S, proceed to step S33; When D ≥ S, proceed to step S34.

[0035] S33: input the maximum annealing temperature data matching instruction in the annealing temperature data set required by the quartz devices to be processed on the day, and match the quartz devices to be processed with the required annealing temperature data t1 to the annealing furnace equipment with the maximum temperature rising energy consumption rate θ 1,up , and sequentially match the annealing temperature data required by the quartz devices to be processed on the day to each annealing furnace equipment, wherein the quartz devices to be processed with the required annealing temperature data t D are matched to the S-Dth annealing furnace equipment.

[0036] S34: input the segmented annealing temperature lookup instruction in the annealing temperature data set of the quartz devices to be processed on the day, and perform matching processing of the target working temperature required to be reached by the annealing furnace equipment on the first day of operation; wherein the annealing temperature data set is divided into X segments, is a down rounding symbol,

[0037] match the maximum annealing temperature data in each segment of the annealing temperature data set to the S annealing furnace equipment in sequence; wherein the dth quartz device to be processed is matched to the st annealing furnace equipment for annealing treatment.

[0038] S35: calculate the pre-start time required by the target working temperature reached by each annealing furnace equipment on the first day of operation, and perform timing pre-start control on each equipment annealing furnace equipment before the factory starts on the day.

[0039] In S35, the calculation strategy of the pre-start time required by the target working temperature reached by each annealing furnace equipment on the first day of operation is as follows: μ s,d = |t d -T n | × T up ; wherein μ s,d is the pre-start time of the st annealing furnace equipment for the dth quartz device to be processed.

[0040] S4: when the furnace temperature of the annealing furnace equipment reaches and stably maintains the target working temperature, continue to match the annealing temperature value required by the next quartz device to be processed in the annealing temperature data set required by the quartz devices to be processed on the day, and dynamically schedule the energy of the annealing furnace equipment according to the energy scheduling strategy.

[0041] S41: when the furnace temperature of the annealing furnace equipment reaches and stably maintains the target working temperature t d,mb , match the two annealing temperature values closest to the target working temperature of the annealing furnace equipment on the spot in the annealing temperature data set required by the quartz devices to be processed on the day, respectively wherein, x1, x2 are subscripts and x1, x2 ∈ {1, 2...d...D}.

[0042] S42: import the two annealing temperature values matched into the energy scheduling strategy, and calculate the energy scheduling coefficient.

[0043] The energy scheduling coefficient includes a temperature rise scheduling coefficient and a temperature drop scheduling coefficient.

[0044] The temperature rise scheduling coefficient r up The calculation strategy is:

[0045] The temperature drop scheduling coefficient r down The calculation strategy is:

[0046] S43: dynamically schedule the energy of the annealing furnace equipment; when r up ≥ r down , the quartz device to be processed with an annealing temperature value of is preferentially annealed; when r up < r down , the quartz device to be processed with an annealing temperature value of is preferentially annealed.

[0047] S5: transmit the annealing furnace equipment information matched by the quartz device to be processed in S4 to the factory workshop where the quartz device to be processed is located, and notify the quartz device transport personnel in the factory to transport the quartz device to be processed to the matched annealing furnace equipment.

[0048] Embodiment two As shown in FIG. 2, the energy scheduling system for the intelligent quartz processing factory in the embodiment of the application includes the following modules: a historical energy consumption data acquisition module, an annealing temperature data set module, a first running temperature determination module, an energy dynamic scheduling module, and an annealing furnace equipment information sharing module.

[0049] The historical energy consumption data acquisition module is used to acquire the historical energy consumption data of each annealing furnace equipment in the factory, and calculate the energy consumption rate of each annealing furnace equipment.

[0050] The annealing temperature data set module is used to extract the annealing temperature value required by the quartz device to be processed in the factory on the day, and sequentially arrange the annealing temperature values from large to small to form the annealing temperature data set required by the quartz device to be processed on the day.

[0051] The first running temperature determination module matches the target working temperature required by each annealing furnace equipment to reach on the day according to the energy consumption rate of each annealing furnace equipment in the annealing temperature data set.

[0052] The energy dynamic scheduling module is configured to continue matching the annealing temperature value required by the next quartz device to be processed in the annealing furnace device from the annealing temperature data set required by the quartz device to be processed in the day when the furnace temperature of the annealing furnace device reaches and stably maintains the target working temperature, and dynamically scheduling the energy of the annealing furnace device according to the energy scheduling strategy.

[0053] The annealing furnace device information sharing module is configured to transmit the annealing furnace device information matched by the quartz device to be processed to the factory workshop where the quartz device to be processed is located, and notify the quartz device transport personnel in the factory to transport the quartz device to be processed to the matched annealing furnace device.

[0054] Embodiment three provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor.

[0055] The processor executes the energy consumption optimization method for the smart quartz processing factory by calling the computer program stored in the memory.

[0056] The electronic device can have great differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to realize the energy consumption optimization method for the smart quartz processing factory provided by the above method embodiments. The electronic device can also include other components for realizing the functions of the device, for example, the electronic device can also have a wired or wireless network interface and an input and output interface, etc., so as to input and output data. This embodiment will not be described here.

[0057] Embodiment four provides a computer readable storage medium, which stores an erasable computer program; when the computer program runs on a computer device, the computer device executes the energy consumption optimization method for the smart quartz processing factory.

[0058] For example, the computer readable storage medium can be a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a read-only compact disc (Compact Disc Read-Only Memory, CD-ROM), a magnetic tape, a floppy disk and an optical data storage device, etc.

[0059] It should be understood that the magnitude of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0060] It should be understood that determining B according to A does not mean that B is determined only according to A, but B can also be determined according to A and / or other information.

[0061] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the flow or function according to the embodiments of the present application is wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired network or / and wireless network. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center and the like containing one or more available medium collections. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, DVD) or a semiconductor medium. The semiconductor medium can be a solid state disk.

[0062] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the disclosed embodiments of the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0063] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0064] In several embodiments of the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is merely illustrative, and the unit division can be changed in other manners during actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units can be indirect couplings or communication connections through some interfaces, devices or unit intermediaries, and can be in electrical, mechanical or other forms.

[0065] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0066] In addition, the functional units in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit.

[0067] In the description of the specification, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0068] In summary, compared with the prior art, the technical effects of the present application are as follows: 1. The present application considers the problem that a large amount of time and energy is consumed when the annealing furnace is heated from room temperature to the target processing temperature or adjusted between different target processing temperatures before the quartz processing plant starts each day. By matching the target working temperature according to the energy consumption rate of each annealing furnace device, dynamic scheduling of energy is realized, which can effectively save energy consumption and reduce energy consumption cost.

[0069] 2. The present application can effectively reduce waiting time and improve production efficiency by orderly arranging and dynamically scheduling the annealing temperature data set, thereby increasing production capacity.

[0070] 3. The present application can maximize the utilization rate of the device, reduce the idle condition and improve the production efficiency according to the matching of the demand of the quartz device and the energy consumption rate of the annealing furnace device.

[0071] 4、The quartz device to be processed is matched to the corresponding annealing furnace equipment, and a transport personnel is informed to transport, so that the coordination and efficiency of the transportation process can be improved, the transportation time is reduced, and the production progress is ensured.

[0072] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for energy consumption optimization for a smart quartz processing plant, characterized in that, The method comprises the following specific steps: S1: collecting historical energy consumption data of each annealing furnace equipment in the factory, and calculating energy consumption rates of each annealing furnace equipment; S2: extracting annealing temperature values required by quartz devices to be processed on the day in the factory, and sequentially arranging the annealing temperature values from large to small to form an annealing temperature data set required by the quartz devices to be processed on the day; S3: matching target working temperatures required by each annealing furnace equipment to be first operated on the day in the annealing temperature data set according to the energy consumption rates of each annealing furnace equipment; S4: when the furnace temperature of the annealing furnace equipment reaches and stably maintains the target working temperature, continuing to match the annealing temperature values required by the next quartz device to be processed in the annealing temperature data set required by the quartz devices to be processed on the day, and dynamically scheduling the energy of the annealing furnace equipment according to an energy scheduling strategy; S5: transmitting the annealing furnace equipment information matched by the quartz device to be processed in the step S4 to a factory workshop where the quartz device to be processed is located, and notifying a quartz device transportation personnel in the factory to transport the quartz device to be processed to the matched annealing furnace equipment.

2. The energy consumption optimization method for a smart quartz processing plant according to claim 1, wherein, The intelligent quartz processing factory comprises S annealing furnace devices; in step S1, the historical energy consumption data of each annealing furnace device comprises: in the last working day, the energy consumption of the annealing furnace device in the temperature rising working stage when the temperature in the furnace rises by 1 degree is W up , and the average time T up used when the temperature in the furnace rises by 1 degree. The historical energy consumption data of each annealing furnace device further comprises: in the last working day, the energy consumption of the annealing furnace device in the temperature reduction working stage when the furnace temperature is reduced by 1 degree is W down , and the average time length T down used when the furnace temperature is reduced by 1 degree.

3. The energy consumption optimization method for a smart quartz processing plant according to claim 2, wherein, In the step S1, the energy consumption rates of each annealing furnace equipment include temperature rising energy consumption rates and temperature falling energy consumption rates; The calculation strategy of the temperature rise energy consumption rate is specifically as follows: The calculation strategy of the cooling energy consumption rate is specifically as follows: wherein θ s,up , θ s,down are the heating and cooling energy consumption rates of the s-th annealing furnace device, respectively; s≤S. T n ,T y are the value of the environmental temperature of the annealing lehr plant in operation on the day and the value of the environmental temperature of the annealing lehr plant in operation on the previous working day, respectively.

4. The energy consumption optimization method for a smart quartz processing plant according to claim 3, wherein, The step S3 comprises the following specific steps: S31: Extract the heating energy consumption rate and annealing temperature data set {t1, t2...t d ...t D} of each annealing furnace equipment, and arrange the heating energy consumption rate of each annealing furnace equipment from large to small in turn to form the heating energy consumption rate set {θ 1,up ,θ 2,up ,...,θ s,up ,...,θ S,up}; wherein t d is the required annealing temperature value of the dth quartz device to be processed; is the required annealing temperature value of the Dth quartz device to be processed; D is the number of quartz devices to be processed on the day, d≤D; is the heating energy consumption rate of the Sth annealing furnace device; S32: extracting the number D of quartz devices to be processed on the day; When D < S, the step S33 is executed; When D ≥ S, the step S34 is executed; S33: input the maximum annealing temperature data matching instruction in the annealing temperature data set required for the quartz devices to be processed on the day, and match the quartz devices to be processed with the required annealing temperature data t1 to the annealing furnace equipment with the maximum temperature rising energy consumption rate θ 1,up , and sequentially match the annealing temperature data required for the quartz devices to be processed on the day to each annealing furnace equipment, wherein the quartz devices to be processed with the required annealing temperature data t D are matched to the S-Dth annealing furnace equipment; S34: input the segmented annealing temperature lookup instruction in the annealing temperature data set of the quartz device to be processed on the day, and perform matching processing of the target working temperature to be reached by the first operation of the annealing furnace equipment on the day; wherein the annealing temperature data set is divided into X segments, to the nearest lower integer, The maximum annealing temperature data in each annealing temperature data set are sequentially matched to S annealing furnace equipments; wherein the dth quartz device to be processed is matched to the st annealing furnace equipment for annealing treatment; S35: calculating pre-starting time required by the target working temperature reached by each annealing furnace equipment to be first operated on the day, and performing timing pre-starting control on each annealing furnace equipment before the factory is started on the day.

5. The energy consumption optimization method for a smart quartz processing plant according to claim 4, wherein, The calculation strategy of the pre-start-up time required for the target working temperature reached by each annealing furnace equipment at the first operation of the day in the step S35 is specifically as follows: μ s,d = |t d -T n | × T up ; wherein μ s,d is the pre-startup time of the s-th annealing furnace device for the d-th quartz device to be processed. 6.The energy consumption optimization method for a smart quartz processing factory according to claim 5, wherein, The step S4 comprises the following specific steps: S41: When the temperature in the furnace of the annealing furnace device reaches and stably maintains the target working temperature t d,mb , match two annealing temperature values closest to the target working temperature of the annealing furnace device at the moment in the annealing temperature data set required by the quartz devices to be processed on the day, respectively wherein x1, x2 are subscripts and x1, x2 ∈ {1, 2... d... D}; S42: obtaining two annealing temperature values matched An energy scheduling strategy is imported, and an energy scheduling coefficient is calculated; The energy scheduling coefficient includes temperature rising scheduling coefficients and temperature falling scheduling coefficients; The temperature-rising scheduling coefficient r up The calculation strategy is: The cooling scheduling coefficient r down The calculation strategy is: S43: dynamically scheduling the energy of the annealing furnace equipment; When r up ≥ r down , the annealing temperature value is The quartz device to be processed is annealed; When r up When r down When r The quartz device to be processed is annealed.

7. An energy scheduling system for a smart quartz processing plant, which is implemented based on the energy consumption optimization method for a smart quartz processing plant according to any one of claims 1-6, characterized in that, The system comprises the following modules: A historical energy consumption data collection module is configured to collect historical energy consumption data of each annealing furnace equipment in the factory, and calculate energy consumption rates of each annealing furnace equipment; An annealing temperature data set module is configured to extract annealing temperature values required by quartz devices to be processed on the day in the factory, and sequentially arrange the annealing temperature values from large to small to form an annealing temperature data set required by the quartz devices to be processed on the day; A first operation temperature determination module is configured to match target working temperatures required by each annealing furnace equipment to be first operated on the day in the annealing temperature data set according to the energy consumption rates of each annealing furnace equipment; The energy dynamic scheduling module is configured to, when the inner temperature of the annealing furnace device reaches and stably maintains the target working temperature, continue to match the annealing temperature value required by the next quartz device to be processed in the annealing furnace device from the annealing temperature data set required by the quartz device to be processed in the day, and dynamically schedule the energy of the annealing furnace device according to the energy scheduling strategy. The annealing furnace device information sharing module is configured to transmit the annealing furnace device information matched by the quartz device to be processed to the factory workshop where the quartz device to be processed is located, and notify the quartz device transport personnel in the factory to transport the quartz device to be processed to the matched annealing furnace device.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the energy consumption optimization method for the intelligent quartz processing factory as claimed in any one of claims 1-6.

9. An electronic device, comprising: The computer program product comprises: a memory for storing instructions; a processor for executing the instructions, so that the device executes the steps of the energy consumption optimization method for the intelligent quartz processing factory as claimed in any one of claims 1-6.

Images