Digital energy management method and platform and storage medium

Abstract

The invention relates to the technical field of energy management, in particular to a digital energy management method and platform and a storage medium, and the method comprises the steps: collecting process data, environmental parameters and gypsum board moisture content; according to the environmental parameters in the monitoring period and the temperature data of the dry kiln, determining an environmental interference factor of each subarea, and based on the environmental interference factor of each subarea, determining a control strategy of each subarea in the next monitoring period; constructing a drying quality coefficient according to the moisture content of the gypsum board in the monitoring period, and determining the drying quality; constructing an energy consumption index of each subarea based on the gas consumption and the electric energy consumption of each subarea in the monitoring period, and determining an energy consumption state of each subarea; and managing the control strategy of the next management period according to the analysis result of the drying quality and the energy consumption state of each monitoring period in the management period. The energy management efficiency of the gypsum board drying process is effectively improved, and the production quality of gypsum boards is improved.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a digital energy management method, platform and storage medium. Background Technology

[0002] In the industrial production of gypsum board, energy consumption management and quality control of drying kilns have long relied on manual experience and static process parameter settings, making it difficult to cope with dynamic interference factors such as fluctuations in environmental temperature and humidity and changes in raw material characteristics. Traditional methods mostly employ single-point monitoring and delayed control, lacking multi-dimensional data linkage analysis of the entire process from preheating to constant temperature to cooling, resulting in both energy waste and quality risks.

[0003] Existing technologies struggle to achieve a real-time balance between energy efficiency and quality due to issues such as the lack of quantitative models for environmental disturbances and delays in control response. There is an urgent need to build a data-driven dynamic optimization system to overcome process bottlenecks. Summary of the Invention

[0004] The purpose of this invention is to provide a digital energy management method, platform, and storage medium to solve at least one of the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A digital energy management method, comprising: Collect process data, environmental parameters, and moisture content of gypsum board; Based on the environmental parameters and drying kiln temperature data within the monitoring period, the environmental interference factors of each zone are determined, and the control strategies of each zone in the next monitoring period are determined based on the environmental interference factors of each zone. A drying quality coefficient was constructed based on the moisture content of gypsum board during the monitoring period, and the drying quality was determined. Based on the gas consumption and electricity consumption of each zone during the monitoring period, an energy consumption index for each zone is constructed, and the energy consumption status of each zone is determined. The control strategy for the next management cycle is based on the analysis results of the drying quality and energy consumption status of each monitoring cycle within the management cycle.

[0006] Optionally, the average ambient temperature collected during the monitoring period is calculated as Tj, the average ambient humidity collected during the monitoring period is calculated as Sj, the average temperature of the preheating zone collected during the monitoring period is calculated as Y1j, the average humidity of the preheating zone is calculated as D1j, the average temperature of the constant temperature zone collected during the monitoring period is calculated as Y2j, the average humidity of the constant temperature zone is calculated as D2j, the average temperature of the cooling zone collected during the monitoring period is calculated as Y3j, and the average humidity of the cooling zone is calculated as D3j; the expression for the environmental interference factor G1 of the preheating zone is: G1=(Y1j-Tj) / Y1j+|Sj-D1j|2 ; The expression for the environmental disturbance factor G2 in the constant temperature zone is: G2=(Y2j-Tj) / Y2j+|Sj-D2j| 2 ; The expression for the environmental disturbance factor G3 in the cooling zone is: G3=(Y3j-Tj) / Y3j+|Sj-D3j| 2 .

[0007] Optionally, the environmental interference factor G1 in the preheating zone is compared with the first interference threshold g1. When G1 is greater than or equal to the first interference threshold g1, the gypsum board conveying rate in the preheating zone of the next monitoring cycle is set to V1, where V1 = α1 × V0, α1 is the first adjustment factor, and V0 is the reference conveying rate of gypsum board. Otherwise, the gypsum board conveying rate in the preheating zone of the next monitoring cycle is set to V0. The environmental interference factor G2 of the constant temperature zone is compared with the second interference threshold g2. When G1 is greater than or equal to the second interference threshold g2, the fan speed of the constant temperature zone in the next monitoring cycle is set to ZS1. ZS1 is set to α2×ZSO, where α2 is the second adjustment factor and ZS0 is the reference speed of the fan in the constant temperature zone. Otherwise, the fan speed of the constant temperature zone in the next monitoring cycle is set to ZS0. The environmental interference factor G3 of the cooling zone is compared with the third interference threshold g3. When G3 is greater than the third interference threshold g3, the hot air curtain outlet temperature of the cooling zone in the next monitoring cycle is set to Tr. Tr is set to Te + Tz × lg[5 × (G3 - g3) + 1] / lg6, where Te is the reference outlet temperature of the hot air curtain in the cooling zone and Tz is the temperature adjustment threshold. Otherwise, the hot air curtain outlet temperature of the cooling zone in the next monitoring cycle is set to Te.

[0008] Optionally, the number of gypsum boards with a moisture content greater than the moisture content threshold Hy during the monitoring period is M1, the number of gypsum boards during the monitoring period is M2, the average moisture content of gypsum boards during the monitoring period is calculated as Hp, and the drying quality coefficient is determined as K. When the drying quality coefficient is less than or equal to the quality threshold k0, the drying quality of the current monitoring period is determined to be unqualified; when the drying quality coefficient is greater than the quality threshold k0, the drying quality of the current monitoring period is determined to be qualified.

[0009] Optionally, the historical monitoring period in which the ambient temperature and humidity are the same as those in the current monitoring period is taken as the target monitoring period, and the average gas consumption of each zone in the target monitoring period is recorded as Qi, and the average power consumption of each zone in the target monitoring period is recorded as Ei, where i is the zone number, i=1 is the preheating zone, i=2 is the constant temperature zone, and i=3 is the cooling zone. The gas consumption of each zone during the current monitoring period is denoted as Qri, and the electricity consumption of each zone during the current monitoring period is denoted as Eri.

[0010] Optionally, the gas factor of the i-th partition is determined based on the gas consumption Qri of each partition in the current monitoring period and the average gas consumption Qi of each partition in the target monitoring period. If Qri is less than or equal to Qi, the gas factor of the i-th partition is set to 0; otherwise, the gas factor of the i-th partition is set to (Qri-Qi) / Qi. The power factor of the i-th partition is determined based on the power consumption Eri of each partition in the current monitoring period and the average power consumption Ei of each partition in the target monitoring period. If Eri is less than or equal to Ei, the power factor of the i-th partition is set to 0; otherwise, the power factor of the i-th partition is set to (Eri-Ei) / Ei. The sum of the gas factor and the electrical energy factor of the i-th partition is taken as the energy consumption index of the i-th partition, denoted as Ni. The energy consumption index Ni of the i-th partition is compared with the energy consumption threshold n0 to determine the energy consumption status of each partition. If Ni is greater than or equal to n0, the energy consumption status of the i-th partition in the current monitoring period is determined to be abnormal energy consumption; otherwise, the energy consumption status of the i-th partition in the current monitoring period is determined to be normal energy consumption.

[0011] Optionally, the ratio of the number of monitoring cycles in the current management cycle with qualified drying quality to the number of monitoring cycles in the management cycle is p0, and the ratio of the number of monitoring cycles in the i-th partition of the current management cycle with normal energy consumption status to the number of monitoring cycles in the management cycle is pi. When p0 is less than or equal to the first preset ratio η1, the drying quality of the current management cycle is determined to be abnormal, and an early warning is issued to the user. When p0 is greater than the first preset ratio η1, the drying quality of the current management cycle is determined to be normal.

[0012] Optionally, if the drying quality is normal in the current management cycle, and if pi is less than or equal to the second preset ratio η2, the control strategy for the i-th partition in the next management cycle is not managed; otherwise, the control strategy for the i-th partition in the next management cycle is managed.

[0013] According to another aspect of this application, a digital energy management platform is provided, comprising: The data acquisition unit is used to collect process data, environmental parameters, and the moisture content of gypsum board. The management unit is used to determine the environmental interference factors of each zone based on the environmental parameters and drying kiln temperature data within the monitoring period, and to determine the control strategy of each zone for the next monitoring period based on the environmental interference factors of each zone. The quality assessment unit is used to construct a drying quality coefficient based on the moisture content of gypsum board during the monitoring period and to determine the drying quality. The energy consumption analysis unit is used to construct the energy consumption index of each zone based on the gas consumption and electricity consumption of each zone within the monitoring period, and to determine the energy consumption status of each zone. The update unit is used to manage the control strategy for the next management cycle based on the analysis results of the drying quality and energy consumption status of each monitoring cycle within the management cycle.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program is used to control an electronic device in which the computer-readable storage medium is located to perform the digital energy management method during runtime.

[0015] The beneficial effects of this invention are as follows: Through multi-source data fusion and dynamic control mechanisms, the energy efficiency and quality control capabilities of the gypsum board drying process are significantly improved. The system collects real-time data on kiln temperature and humidity, energy consumption, and board moisture content, constructs an environmental interference factor model, quantifies external temperature and humidity fluctuations into zoned control parameters, and achieves adaptive matching of core equipment such as transmission rate and fan speed, effectively offsetting the impact of environmental interference on process stability. Based on a dual evaluation system of drying quality coefficient and energy consumption index, abnormal moisture content trends and energy waste points can be accurately identified. Combined with a self-learning algorithm based on historical data, the control threshold is iteratively optimized, overcoming the lag of traditional experience-based decision-making. The long-term management mechanism further strengthens the sustainability of system operation. Through dynamic correction of adjustment factors and calibration of benchmark parameters, it balances short-term process fluctuation suppression with long-term equipment condition maintenance, ultimately achieving synergistic optimization of reduced quality defect rate, reduced unit capacity consumption, and production response agility, promoting the intelligent and green transformation of gypsum board manufacturing. Attached Figure Description

[0016] 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.

[0017] Figure 1 This is a flowchart illustrating the digital energy management method of this embodiment.

[0018] Figure 2 This is a flowchart illustrating the control strategy determination method in this embodiment.

[0019] Figure 3 This is a flowchart illustrating the control strategy management method of this embodiment.

[0020] Figure 4 This is a schematic diagram of the structure of the digital energy management platform in this embodiment. Detailed Implementation

[0021] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further clarifies the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Specifically, this embodiment is applied to the intelligent management and control scenario of an industrial gypsum board drying kiln. During the gypsum board drying process, it is necessary to dynamically balance dehydration efficiency, heat consumption, and the risk of board cracking. Traditional manual control is easily affected by changes in environmental temperature and humidity, leading to a surge in energy consumption or fluctuations in the finished product qualification rate. This embodiment quantifies the intensity of external disturbances in real time through multi-zone environmental interference factors, and dynamically adjusts the transmission rate, fan speed, and hot air temperature using a dual closed-loop model of drying quality coefficient and energy consumption index. A self-learning algorithm driven by historical data corrects the adjustment factors and interference thresholds, accurately suppressing the risk of sudden heat exchange anomalies or condensation and moisture regain. Simultaneously, it optimizes the comprehensive utilization efficiency of gas and electricity, avoiding rework losses caused by overheating cracking or insufficient moisture content. The solution is fully adaptable to complex operating conditions such as raw material characteristic fluctuations and seasonal climate differences, achieving the dual goals of stable quality and energy saving.

[0024] Please see Figure 1 As shown, it is a flowchart illustrating the digital energy management method of this embodiment, including: Step S101: Collect process data, environmental parameters, and gypsum board moisture content. The process data includes drying kiln temperature data and drying kiln humidity data. The energy consumption data includes gas consumption data and electricity consumption data. The environmental parameters include ambient temperature and ambient humidity. The drying kiln temperature data includes preheating zone temperature, constant temperature zone temperature, and cooling zone temperature. The drying kiln humidity data includes preheating zone humidity, constant temperature zone humidity, and cooling zone humidity. The gas consumption data includes gas consumption in the preheating zone, constant temperature zone, and cooling zone. The electricity consumption data... This includes the energy consumption of the preheating zone, the constant temperature zone, and the cooling zone. The ambient temperature and humidity are the ambient temperatures and humidity outside the drying kiln. The moisture content of the gypsum board is the moisture content of the gypsum board at the outlet of the cooling zone. The preheating zone is the initial section where the gypsum board enters the drying kiln. It uses medium-low temperature heating to initially evaporate the surface moisture of the board and activate the crystal structure of the gypsum. The constant temperature zone is the core reaction section of the drying process, maintaining a high temperature environment to complete the crystal transformation of the gypsum and the deep removal of internal moisture. The cooling zone is the section where the dried board is slowly cooled down.

[0025] For example, in this embodiment, temperature data, humidity data and environmental parameters of the drying kiln can be collected by a temperature and humidity sensor, gas consumption data can be collected by a gas mass flow meter, electricity consumption data can be collected by a smart meter, and the moisture content of gypsum board can be collected by a microwave moisture meter. In this embodiment, no specific limitation is made on the data collection method and the division of each zone. Those skilled in the art can set it freely according to their needs.

[0026] Specifically, by collecting real-time data on temperature, humidity, energy consumption, and moisture content of the boards in each zone of the drying kiln, a complete production monitoring network is constructed, providing fundamental data support for subsequent analysis. Its core advantage lies in comprehensively capturing the process operation status, eliminating the blind spots of traditional manual sampling, and establishing dynamic data benchmarks to ensure that parameters at each stage are traceable and comparable, laying an information foundation for anomaly diagnosis and process optimization.

[0027] Please continue reading. Figure 1 As shown, the digital energy management method further includes: Step S102: Determine the environmental interference factors for each zone based on the environmental parameters and drying kiln temperature data within the monitoring period, and determine the control strategy for each zone in the next monitoring period based on the environmental interference factors for each zone.

[0028] For example, in this embodiment, the monitoring period can be set to 3 minutes. This embodiment does not specifically limit the setting of the monitoring period, and those skilled in the art can set it freely according to their needs.

[0029] Please see Figure 2 As shown, the control strategy determination method includes: Step S201: Determine the environmental interference factors for each zone based on the environmental parameters and drying kiln temperature data collected during the monitoring period.

[0030] Specifically, the average ambient temperature collected during the monitoring period is calculated as Tj, the average ambient humidity collected during the monitoring period is calculated as Sj, the average temperature of the preheating zone collected during the monitoring period is calculated as Y1j, the average humidity of the preheating zone is calculated as D1j, the average temperature of the constant temperature zone collected during the monitoring period is calculated as Y2j, the average humidity of the constant temperature zone is calculated as D2j, and the average temperature of the cooling zone collected during the monitoring period is calculated as Y3j, and the average humidity of the cooling zone is calculated as D3j. The expression for the environmental disturbance factor G1 in the preheating zone is: G1=(Y1j-Tj) / Y1j+|Sj-D1j| 2 ; The expression for the environmental disturbance factor G2 in the constant temperature zone is: G2=(Y2j-Tj) / Y2j+|Sj-D2j| 2 ; The expression for the environmental disturbance factor G3 in the cooling zone is: G3=(Y3j-Tj) / Y3j+|Sj-D3j| 2 .

[0031] Specifically, the differences in environmental and process parameters are quantified into a zoned interference index, which objectively reflects the intensity and direction of the influence of the external environment. This step eliminates subjective experience-based judgment errors, accurately identifies the sensitivity differences of each zone, and provides a quantitative basis for differential regulation.

[0032] Please continue reading. Figure 2 As shown, the control strategy determination method further includes: Step S202: Determine the control strategy for each zone in the next monitoring cycle based on the environmental interference factors of each zone.

[0033] Specifically, the environmental interference factor G1 in the preheating zone is compared with the first interference threshold g1. When G1 is greater than or equal to the first interference threshold g1, the gypsum board conveying rate in the preheating zone of the next monitoring cycle is set to V1. V1 is set to α1 × V0, where α1 is the first adjustment factor and V0 is the reference conveying rate of gypsum board. Otherwise, the gypsum board conveying rate in the preheating zone of the next monitoring cycle is set to V0. The environmental interference factor G2 of the constant temperature zone is compared with the second interference threshold g2. When G1 is greater than or equal to the second interference threshold g2, the fan speed of the constant temperature zone in the next monitoring cycle is set to ZS1. ZS1 is set to α2×ZSO, where α2 is the second adjustment factor and ZS0 is the reference speed of the fan in the constant temperature zone. Otherwise, the fan speed of the constant temperature zone in the next monitoring cycle is set to ZS0. The environmental interference factor G3 of the cooling zone is compared with the third interference threshold g3. When G3 is greater than the third interference threshold g3, the hot air curtain outlet temperature of the cooling zone in the next monitoring cycle is set to Tr. Tr is set to Te + Tz × lg[5 × (G3 - g3) + 1] / lg6, where Te is the reference outlet temperature of the hot air curtain in the cooling zone and Tz is the temperature adjustment threshold. Otherwise, the hot air curtain outlet temperature of the cooling zone in the next monitoring cycle is set to Te.

[0034] Specifically, based on the interference index, a tiered response is implemented, dynamically adjusting core parameters such as transmission rate and fan power. Differentiated control achieves a dynamic balance between energy saving and performance, avoiding excessive energy consumption or localized failures caused by generalized, indiscriminate adjustments.

[0035] For example, in this embodiment, the first interference threshold can be set to 0.18, the second interference threshold can be set to 0.22, the third interference threshold can be set to 0.13, the first adjustment factor can be set to 0.9, the second adjustment factor can be set to 1.1, and the temperature adjustment threshold can be set to 10℃. This embodiment does not specifically limit the above settings, and those skilled in the art can set them freely according to their needs.

[0036] Specifically, in this embodiment, the ambient humidity is a dimensionless decimal, such as an ambient humidity of 0.12, which means a relative humidity of 12%. The reference transmission rate of the gypsum board, the reference speed of the fan in the constant temperature zone, and the reference outlet temperature of the hot air curtain in the cooling zone can be obtained interactively.

[0037] Please continue reading. Figure 1 As shown, the digital energy management method further includes: Step S103: Construct a drying quality coefficient based on the moisture content of gypsum board within the monitoring period, and determine the drying quality.

[0038] Specifically, the number of gypsum boards with a moisture content greater than the moisture content threshold Hy during the statistical monitoring period is M1, the number of gypsum boards during the statistical monitoring period is M2, and the average moisture content of gypsum boards during the monitoring period is calculated as Hp. The expression for the drying quality coefficient is: K=w1×(M2-M1) / M2+w2×[1-tanh(Hp / Hy)], where K is the drying quality coefficient, w1 is the first weighting factor, w2 is the second weighting factor, and w1+w2=1; When the drying quality coefficient is less than or equal to the quality threshold k0, the drying quality of the current monitoring period is determined to be unqualified; when the drying quality coefficient is greater than the quality threshold k0, the drying quality of the current monitoring period is determined to be qualified.

[0039] For example, in this embodiment, the first weighting factor can be set to 0.6, the second weighting factor can be set to 0.4, and the quality threshold can be set to 0.3. This embodiment does not specifically limit the above settings, and those skilled in the art can set them freely according to their needs.

[0040] Specifically, a composite quality coefficient is constructed based on the characteristics of moisture content distribution, overcoming the limitations of single-criteria judgment and enabling graded early warning of quality risks. By combining the frequency of exceedances with the trend of mean deviation, it can identify both occasional anomalies and continuously track systemic process degradation, triggering quality intervention in advance and reducing batch defect losses.

[0041] Please continue reading. Figure 1 As shown, the digital energy management method further includes: Step S104: Construct the energy consumption index of each zone based on the gas consumption and electricity consumption of each zone within the monitoring period, and determine the energy consumption status of each zone.

[0042] Specifically, the historical monitoring period in which the ambient temperature and humidity are the same as those in the current monitoring period is used as the target monitoring period. The average gas consumption of each zone in the target monitoring period is recorded as Qi, and the average electricity consumption of each zone in the target monitoring period is recorded as Ei. i is the zone number. When i=1, it is the preheating zone; when i=2, it is the constant temperature zone; and when i=3, it is the cooling zone. The gas consumption of each zone during the current monitoring period is denoted as Qri, and the electricity consumption of each zone during the current monitoring period is denoted as Eri. The process of constructing the energy consumption index for each partition is as follows: The gas factor of the i-th partition is determined based on the gas consumption Qri of each partition in the current monitoring period and the average gas consumption Qi of each partition in the target monitoring period. If Qri is less than or equal to Qi, the gas factor of the i-th partition is set to 0; otherwise, the gas factor of the i-th partition is set to (Qri-Qi) / Qi. The power factor of the i-th partition is determined based on the power consumption Eri of each partition in the current monitoring period and the average power consumption Ei of each partition in the target monitoring period. If Eri is less than or equal to Ei, the power factor of the i-th partition is set to 0; otherwise, the power factor of the i-th partition is set to (Eri-Ei) / Ei. The sum of the gas factor and the electrical energy factor of the i-th partition is taken as the energy consumption index of the i-th partition, denoted as Ni. The energy consumption index Ni of the i-th partition is compared with the energy consumption threshold n0 to determine the energy consumption status of each partition. If Ni is greater than or equal to n0, the energy consumption status of the i-th partition in the current monitoring period is determined to be abnormal energy consumption; otherwise, the energy consumption status of the i-th partition in the current monitoring period is determined to be normal energy consumption.

[0043] For example, in this embodiment, the energy consumption threshold can be set to 0.24. This embodiment does not specifically limit the above setting, and those skilled in the art can set it freely according to their needs.

[0044] Specifically, by comparing current energy consumption with historical benchmarks, high-energy-consuming zones or equipment anomalies can be accurately located. This step strengthens energy efficiency benchmarking management, quickly identifies hidden problems such as gas leaks and motor inefficiencies, and explores optimization potential for process parameters, promoting the precise implementation of energy-saving measures.

[0045] Please continue reading. Figure 1 As shown, the digital energy management method further includes: Step S105: Based on the analysis results of drying quality and energy consumption status of each monitoring cycle within the management cycle, manage the control strategy for the next management cycle.

[0046] For example, this embodiment does not specifically limit the setting of the management cycle. Those skilled in the art can set it freely according to their needs, such as setting the management cycle to 7 days.

[0047] Please see Figure 3 As shown, the control strategy management method includes: Step S301: Determine the drying quality of the current management cycle based on the drying quality of each monitoring cycle within the management cycle.

[0048] Specifically, the ratio of the number of monitoring cycles in the current management cycle with qualified drying quality to the number of monitoring cycles within the management cycle is p0, and the ratio of the number of monitoring cycles in the i-th partition of the current management cycle with normal energy consumption status to the number of monitoring cycles within the management cycle is pi. When p0 is less than or equal to the first preset ratio η1, the drying quality of the current management cycle is determined to be abnormal, and an early warning is issued to the user. When p0 is greater than the first preset ratio η1, the drying quality of the current management cycle is determined to be normal.

[0049] For example, in this embodiment, the first preset ratio can be set to 0.08. This embodiment does not specifically limit the above setting, and those skilled in the art can set it freely according to their needs.

[0050] Specifically, it aggregates quality data within the management cycle, identifies long-term trend risks, breaks through the limitations of single-point monitoring, systematically verifies the effectiveness of the quality control system, and provides a basis for decision-making for process upgrades or equipment maintenance.

[0051] Please see Figure 3 As shown, the control strategy management method includes: Step S302: Based on the analysis results of the drying quality and energy consumption status of each monitoring cycle within the management cycle, manage the control strategy for the next management cycle.

[0052] Specifically, when the drying quality is normal in the current management cycle, if pi is less than or equal to the second preset ratio η2, the control strategy for the i-th partition in the next management cycle is not managed; otherwise, the control strategy for the i-th partition in the next management cycle is managed. The management process for the control strategy of the first partition in the next management cycle is as follows: Set the first adjustment factor for the next management cycle to α1', and set α1'=α1×{1-exp[3×(p1-η2)-3}; The management process for the control strategy of the second partition in the next management cycle is as follows: The reference speed of the fan in the constant temperature zone for the next management cycle is adjusted to ZS0', and ZS0' = ZS0 × β is set, where β is the correction factor; The management process for the control strategy of the third zone in the next management cycle is as follows: The third interference threshold for the next management cycle is adjusted to g3', and g3' is set to g3+g0×ln[4×(p3-η2)+1] / ln5, where g0 is the adjustment threshold.

[0053] For example, in this embodiment, the second preset ratio can be set to 0.15, the correction factor can be set to 0.95, and the adjustment threshold can be set to 0.05. This embodiment does not specifically limit the above settings, and those skilled in the art can set them freely according to their needs.

[0054] Specifically, long-term parameters such as adjustment factors and speed benchmarks are adaptively optimized based on historical operating performance. Periodic calibration reduces the risk of strategy drift, continuously approaches the optimal process curve, and supports the production line in maintaining the best economic and quality output.

[0055] Please continue reading. Figure 4 As shown, the digital energy management platform includes: The data acquisition unit is used to collect process data, environmental parameters, and the moisture content of gypsum board. The management unit is used to determine the environmental interference factors of each zone based on the environmental parameters and drying kiln temperature data within the monitoring period, and to determine the control strategy of each zone for the next monitoring period based on the environmental interference factors of each zone. The quality assessment unit is used to construct a drying quality coefficient based on the moisture content of gypsum board during the monitoring period and to determine the drying quality. The energy consumption analysis unit is used to construct the energy consumption index of each zone based on the gas consumption and electricity consumption of each zone within the monitoring period, and to determine the energy consumption status of each zone. The update unit is used to manage the control strategy for the next management cycle based on the analysis results of the drying quality and energy consumption status of each monitoring cycle within the management cycle.

[0056] The digital energy management platform provided in this application embodiment can execute the digital energy management method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the execution method.

[0057] This application also provides a computer-readable storage medium, which is a tangible physical storage medium that can store the aforementioned computer program and various types of data used in the program; the physical storage medium includes, but is not limited to, existing physical storage media or combinations thereof, such as random access memory, read-only memory, optical disk, and hard disk.

[0058] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable programs, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable programs, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0059] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A digital energy management method, characterized in that, include: Collect process data, environmental parameters, and moisture content of gypsum board; Based on the environmental parameters and drying kiln temperature data within the monitoring period, the environmental interference factors of each zone are determined, and the control strategies for each zone in the next monitoring period are determined based on the environmental interference factors of each zone. A drying quality coefficient was constructed based on the moisture content of gypsum board during the monitoring period, and the drying quality was determined. Based on the gas consumption and electricity consumption of each zone during the monitoring period, an energy consumption index for each zone is constructed, and the energy consumption status of each zone is determined. The control strategy for the next management cycle is based on the analysis results of the drying quality and energy consumption status of each monitoring cycle within the management cycle.

2. The digital energy management method according to claim 1, characterized in that, The average ambient temperature collected during the monitoring period is calculated as Tj, the average ambient humidity collected during the monitoring period is calculated as Sj, the average temperature of the preheating zone is calculated as Y1j, the average humidity of the preheating zone is calculated as D1j, the average temperature of the constant temperature zone is calculated as Y2j, the average humidity of the constant temperature zone is calculated as D2j, the average temperature of the cooling zone is calculated as Y3j, and the average humidity of the cooling zone is calculated as D3j; the expression for the environmental interference factor G1 of the preheating zone is: G1=(Y1j-Tj) / Y1j+|Sj-D1j| 2 ; The expression for the environmental disturbance factor G2 in the constant temperature zone is: G2=(Y2j-Tj) / Y2j+|Sj-D2j| 2 ; The expression for the environmental disturbance factor G3 in the cooling zone is: G3=(Y3j-Tj) / Y3j+|Sj-D3j| 2 。 3. The digital energy management method according to claim 2, characterized in that, The environmental interference factor G1 in the preheating zone is compared with the first interference threshold g1. When G1 is greater than or equal to the first interference threshold g1, the gypsum board conveying rate in the preheating zone of the next monitoring cycle is set to V1. V1 is set to α1 × V0, where α1 is the first adjustment factor and V0 is the reference conveying rate of gypsum board. Otherwise, the gypsum board conveying rate in the preheating zone of the next monitoring cycle is set to V0. The environmental interference factor G2 of the constant temperature zone is compared with the second interference threshold g2. When G1 is greater than or equal to the second interference threshold g2, the fan speed of the constant temperature zone in the next monitoring cycle is set to ZS1. ZS1 is set to α2×ZSO, where α2 is the second adjustment factor and ZS0 is the reference speed of the fan in the constant temperature zone. Otherwise, the fan speed of the constant temperature zone in the next monitoring cycle is set to ZS0. The environmental interference factor G3 of the cooling zone is compared with the third interference threshold g3. When G3 is greater than the third interference threshold g3, the hot air curtain outlet temperature of the cooling zone in the next monitoring cycle is set to Tr. Tr is set to Te + Tz × lg[5 × (G3 - g3) + 1] / lg6, where Te is the reference outlet temperature of the hot air curtain in the cooling zone and Tz is the temperature adjustment threshold. Otherwise, the hot air curtain outlet temperature of the cooling zone in the next monitoring cycle is set to Te.

4. The digital energy management method according to claim 3, characterized in that, The number of gypsum boards with a moisture content greater than the moisture content threshold Hy during the monitoring period is M1, the number of gypsum boards during the monitoring period is M2, the average moisture content of gypsum boards during the monitoring period is calculated as Hp, and the drying quality coefficient is determined as K. When the drying quality coefficient is less than or equal to the quality threshold k0, the drying quality of the current monitoring period is determined to be unqualified; when the drying quality coefficient is greater than the quality threshold k0, the drying quality of the current monitoring period is determined to be qualified.

5. The digital energy management method according to claim 4, characterized in that, The historical monitoring period in which the ambient temperature and humidity are the same as those in the current monitoring period is taken as the target monitoring period. The average gas consumption of each zone in the target monitoring period is recorded as Qi, and the average electricity consumption of each zone in the target monitoring period is recorded as Ei. i is the zone number. When i=1, it is the preheating zone; when i=2, it is the constant temperature zone; and when i=3, it is the cooling zone. The gas consumption of each zone during the current monitoring period is denoted as Qri, and the electricity consumption of each zone during the current monitoring period is denoted as Eri.

6. The digital energy management method according to claim 5, characterized in that, The gas factor of the i-th partition is determined based on the gas consumption Qri of each partition in the current monitoring period and the average gas consumption Qi of each partition in the target monitoring period. If Qri is less than or equal to Qi, the gas factor of the i-th partition is set to 0; otherwise, the gas factor of the i-th partition is set to (Qri-Qi) / Qi. The power factor of the i-th partition is determined based on the power consumption Eri of each partition in the current monitoring period and the average power consumption Ei of each partition in the target monitoring period. If Eri is less than or equal to Ei, the power factor of the i-th partition is set to 0; otherwise, the power factor of the i-th partition is set to (Eri-Ei) / Ei. The sum of the gas factor and the electrical energy factor of the i-th partition is taken as the energy consumption index of the i-th partition, denoted as Ni. The energy consumption index Ni of the i-th partition is compared with the energy consumption threshold n0 to determine the energy consumption status of each partition. If Ni is greater than or equal to n0, the energy consumption status of the i-th partition in the current monitoring period is determined to be abnormal energy consumption; otherwise, the energy consumption status of the i-th partition in the current monitoring period is determined to be normal energy consumption.

7. The digital energy management method according to claim 6, characterized in that, The ratio of the number of monitoring cycles in the current management cycle where the drying quality is qualified to the number of monitoring cycles in the management cycle is p0. The ratio of the number of monitoring cycles in the i-th partition in the current management cycle where the energy consumption status is normal to the number of monitoring cycles in the management cycle is pi. When p0 is less than or equal to the first preset ratio η1, the drying quality of the current management cycle is determined to be abnormal, and an early warning is issued to the user. When p0 is greater than the first preset ratio η1, the drying quality of the current management cycle is determined to be normal.

8. The digital energy management method according to claim 7, characterized in that, When the drying quality is normal in the current management cycle, if pi is less than or equal to the second preset ratio η2, the control strategy for the i-th partition in the next management cycle is not managed; otherwise, the control strategy for the i-th partition in the next management cycle is managed.

9. A digital energy management platform, applied to the digital energy management method according to any one of claims 1-8, characterized in that, include: The data acquisition unit is used to collect process data, environmental parameters, and the moisture content of gypsum board. The management unit is used to determine the environmental interference factors of each zone based on the environmental parameters and drying kiln temperature data within the monitoring period, and to determine the control strategy of each zone for the next monitoring period based on the environmental interference factors of each zone. The quality assessment unit is used to construct a drying quality coefficient based on the moisture content of gypsum board during the monitoring period and to determine the drying quality. The energy consumption analysis unit is used to construct the energy consumption index of each zone based on the gas consumption and electricity consumption of each zone within the monitoring period, and to determine the energy consumption status of each zone. The update unit is used to manage the control strategy for the next management cycle based on the analysis results of the drying quality and energy consumption status of each monitoring cycle within the management cycle.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device on which the computer-readable storage medium is located to perform the digital energy management method according to any one of claims 1-8 during runtime.

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