Internet of Things control method and system for digital intelligent plant

Abstract

The invention relates to the technical field of factory control, and discloses an Internet of Things control method and system for a digital intelligent chemical plant, and the method comprises the steps: obtaining each resource consumption sequence of the chemical plant in a production process, determining a stability result of each resource consumption sequence through autocorrelation analysis, and obtaining a stability result of each resource consumption sequence; calculating the fluctuation degree of each resource consumption sequence to obtain a correlation degree value between the resource consumption sequences, and comparing the correlation degree value with a difference value between maximum correlation degrees to determine a correlation resource consumption sequence set between the resource consumption sequences; and further obtaining each resource consumption sequence which may have a fault and calculating stable decline degree data of each resource consumption sequence which may have a fault, and finally performing resource optimization allocation of the chemical plant by using a genetic algorithm. The method has the following effects: core factors with problems in the control of the Internet of Things of the chemical plant can be determined in time, and optimal allocation of resources is realized.

Description

Technical Field

[0001] The present invention relates to the field of factory control technology, and in particular to an Internet of Things control method and system for a digital factory. Background Art

[0002] At present, in the process of IoT control, some common control methods include: rule-based control: using predefined rules and logic to control the operation of equipment; feedback-based control: adjusting the operation of equipment in real time according to the data fed back by sensors; model-based control: using mathematical models to predict the best control strategy for equipment. The main application areas of IoT control include smart home, smart manufacturing, smart grid, smart transportation, smart city, etc. In these fields, IoT control can realize functions such as remote monitoring, automatic control, fault detection of equipment, and improve the efficiency and reliability of the system; for chemical plants, IoT control can be used to effectively monitor and manage various resources in the production process and monitor and control them in real time. Chemical plants can optimize resource utilization, improve production efficiency, and better maintain the stability of equipment and systems.

[0003] In one existing technology, most studies set a single threshold for target parameters to control, without considering the adaptive setting of parameter thresholds, which makes it difficult to ensure the stability of production. At the same time, the core factors affecting production cannot be accurately determined during the production process, and resource allocation is limited.

[0004] The existing technology has the problem of being unable to timely determine the core factors of problems in the Internet of Things control of chemical plants, resulting in the inability to optimize the allocation of resources. Summary of the invention

[0005] The present invention provides an Internet of Things control method and system for a digital chemical plant, so as to timely determine the core factors of problems arising in the Internet of Things control of a chemical plant and achieve optimal allocation of resources.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides an Internet of Things control method for a digital factory, comprising: Obtain the resource consumption sequence of each chemical plant during the production process; According to each resource consumption sequence, determining the stationarity result of each resource consumption sequence through autocorrelation analysis; According to the stationarity results of the resource consumption sequences, the fluctuation degree of the resource consumption sequences is calculated to obtain the correlation degree values ​​between the resource consumption sequences; Determine a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation values ​​between the resource consumption sequences and the difference between the maximum correlation values; According to the set of related resource consumption sequences, obtaining each resource consumption sequence that may fail and calculating stable decrease degree data of each resource consumption sequence that may fail; According to the stable decline degree data, the resource allocation of the chemical plant is optimized by using a genetic algorithm.

[0007] In an optional implementation, determining the stationarity result of each resource consumption sequence through autocorrelation analysis according to each resource consumption sequence includes: Obtaining the initial mean value of each resource consumption sequence; Comparing the initial mean value with the final mean values ​​of the resource consumption sequences at all different time points in each resource consumption sequence, to obtain difference values ​​of each resource consumption sequence; According to the difference values ​​of the various resource consumption sequences, the inverse of the sum of all the difference values ​​is taken as the stationarity result of the various resource consumption sequences.

[0008] In an optional implementation, calculating the fluctuation degree of each resource consumption sequence according to the stationarity result of each resource consumption sequence to obtain the correlation degree value between each resource consumption sequence includes: According to the stationarity results of each resource consumption sequence, the fluctuation degree of each resource consumption sequence is calculated by the following formula: ; in, Indicates the degree of fluctuation of resource consumption sequence, represents the mean of the resource consumption series, Represents the time index of each data in the resource consumption sequence. Indicates that the resource consumption sequence is The consumption of time, The index identifier of the resource consumption sequence. Indicates that the resource consumption sequence is and The relevance of the data at the moment The larger the value, the higher the resource consumption sequence. and The greater the correlation of the data at each moment, σ represents the standard deviation; dividing a plurality of resource consumption sequences with similar fluctuation degrees of the resource consumption sequences into a new resource consumption sequence group; Aggregation is performed based on correlation coefficient values ​​between any two resource consumption sequences in the new resource consumption sequence group to obtain correlation degree values ​​between the resource consumption sequences.

[0009] In an optional implementation, determining a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation degree values ​​between the resource consumption sequences with the difference between the maximum correlation degree values ​​includes: Obtaining a maximum correlation value among the correlations between the resource consumption sequences; Subtracting the correlation degree values ​​between the resource consumption sequences from the maximum correlation degree value to obtain a correlation degree difference value; According to the correlation degree difference, a set of related resource consumption sequences between the resource consumption sequences is determined by the following method: When the correlation degree difference is greater than β, all resource consumption sequences are determined as the same resource consumption sequence set; When the correlation degree difference is less than or equal to β, it is determined that the resource consumption sequence does not belong to any resource consumption sequence set; Among them, β is the stable correlation value completed in advance.

[0010] In an optional implementation, acquiring each resource consumption sequence that may fail and calculating stable decrease degree data of each resource consumption sequence that may fail according to the set of related resource consumption sequences includes: Obtaining a mutation degree of a resource consumption sequence in the resource consumption sequence set; According to the mutation degree, anomaly detection is performed to generate resource consumption sequences of the various possible failures; Predicting based on the resource consumption sequences that may fail, to obtain predicted consumption parameters; According to the predicted consumption parameters, the stable decrease degree data of each of the resource consumption sequences that may fail is calculated by the following formula: ; in, is the stable descent degree data, It is The number of data points in a resource consumption sequence, Indicates A resource consumption sequence at time point and The difference between .

[0011] In an optional implementation, the optimizing resource allocation of the chemical plant using a genetic algorithm according to the stable decrease degree data includes: According to the stable decrease degree data, the stable decrease threshold value is compared with the determined stable decrease threshold value to determine the resource consumption units that may fail corresponding to all resource consumption sequences that may fail; When the total production consumption of the resource consuming units that may fail and other resource consuming units that are unlikely to fail is the lowest, the genetic algorithm is used to optimize the allocation of resources in the chemical plant.

[0012] In an optional implementation, obtaining the mutation degree of the resource consumption sequence in the resource consumption sequence set includes: According to the consumption trend and the actual consumption trend change slope, the actual consumption trend change slope is normalized to obtain a mutation degree of a resource consumption sequence in the resource consumption sequence set; The standardization includes: ; in, represents the mutation degree of the resource consumption sequence in the resource consumption sequence set, The proportional coefficient representing the degree of mutation is used to adjust the sensitivity of the degree of mutation. represents the actual consumption trend change slope of the resource consumption sequence in the resource consumption sequence set, Indicates the standard consumption trend change slope of the resource consumption sequence in the resource consumption sequence set.

[0013] In a second aspect, the present invention provides an Internet of Things control system for a digital factory, comprising: The data acquisition module is used to obtain the resource consumption sequence of each chemical plant during the production process; A stationarity determination module, used to determine the stationarity result of each resource consumption sequence through autocorrelation analysis according to each resource consumption sequence; A fluctuation degree calculation module, used to calculate the fluctuation degree of each resource consumption sequence according to the stationarity results of each resource consumption sequence, and obtain the correlation degree values ​​between each resource consumption sequence; A resource consumption sequence set determination module, configured to determine a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation degree values ​​between the resource consumption sequences with the difference between the maximum correlation degrees; A stable descent degree calculation module, used to obtain each resource consumption sequence that may fail according to the relevant resource consumption sequence set and calculate the stable descent degree data of each resource consumption sequence that may fail; The optimization allocation module is used to optimize the resource allocation of the chemical plant by using a genetic algorithm according to the stable decline degree data.

[0014] In the third aspect, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the Internet of Things control method for a digital factory as described in any one of the above.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the Internet of Things control method for a digital factory described in any one of the above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses an Internet of Things control method for a digital chemical plant, the method comprising obtaining each resource consumption sequence in the production process of the chemical plant, determining the stationarity results of each resource consumption sequence through autocorrelation analysis, thereby calculating the degree of fluctuation of each resource consumption sequence, obtaining the correlation degree value between each resource consumption sequence, and determining the set of related resource consumption sequences between resource consumption sequences by comparing with the difference between the maximum correlation degree, and then obtaining each resource consumption sequence that may fail and calculating the stable decline degree data of each resource consumption sequence that may fail, and finally using a genetic algorithm to optimize the resource allocation of the chemical plant. The method has the following effects: the core factors of the problems in the Internet of Things control of the chemical plant can be determined in time, and the optimal allocation of resources can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of the Internet of Things control method for a digital factory provided by the first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of the Internet of Things control system of a digital factory provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] At present, in the process of IoT control, some common control methods include: rule-based control: using predefined rules and logic to control the operation of equipment; feedback-based control: adjusting the operation of equipment in real time according to the data fed back by sensors; model-based control: using mathematical models to predict the best control strategy for equipment. The main application areas of IoT control include smart home, smart manufacturing, smart grid, smart transportation, smart city, etc. In these fields, IoT control can realize functions such as remote monitoring, automatic control, fault detection of equipment, and improve the efficiency and reliability of the system; for chemical plants, IoT control can be used to effectively monitor and manage various resources in the production process and monitor and control them in real time. Chemical plants can optimize resource utilization, improve production efficiency, and better maintain the stability of equipment and systems.

[0020] In one existing technology, most studies set a single threshold value for the target parameter to control it, without considering the adaptive setting of the parameter threshold value, which makes it difficult to ensure the stability of production. At the same time, in the production process, there is a problem that the core factors of the problems in the IoT control of chemical plants cannot be determined in time, resulting in the inability to optimize the allocation of resources.

[0021] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides an Internet of Things control method for a digital factory, comprising the following steps: S11, obtaining each resource consumption sequence of the chemical plant during the production process; S12, determining the stationarity result of each resource consumption sequence through autocorrelation analysis according to each resource consumption sequence; S13, calculating the fluctuation degree of each resource consumption sequence according to the stationarity result of each resource consumption sequence, and obtaining the correlation degree value between each resource consumption sequence; S14, determining a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation degree values ​​between the resource consumption sequences with the difference between the maximum correlation degrees; S15, acquiring each resource consumption sequence that may fail according to the set of related resource consumption sequences and calculating stable decrease degree data of each resource consumption sequence that may fail; S16, optimizing resource allocation of the chemical plant using a genetic algorithm based on the stable decrease degree data.

[0022] The present invention discloses an Internet of Things control method for a digital chemical plant, the method comprising obtaining each resource consumption sequence in the production process of the chemical plant, determining the stationarity results of each resource consumption sequence through autocorrelation analysis, thereby calculating the degree of fluctuation of each resource consumption sequence, obtaining the correlation degree value between each resource consumption sequence, and determining the set of related resource consumption sequences between resource consumption sequences by comparing with the difference between the maximum correlation degree, and then obtaining each resource consumption sequence that may fail and calculating the stable decline degree data of each resource consumption sequence that may fail, and finally using a genetic algorithm to optimize the resource allocation of the chemical plant. The method has the following effects: the core factors of the problems in the Internet of Things control of the chemical plant can be determined in time, and the optimal allocation of resources can be achieved.

[0023] In step S11, each resource consumption sequence in the production process of the chemical plant is obtained.

[0024] It should be noted that the resource consumption sequence generally refers to the changes in the use of various resources such as raw materials, energy, water, etc. by chemical plants during the production process over time. The resource consumption sequence is generally composed of raw material consumption, energy consumption, water consumption, waste generation, and equipment usage. The resource consumption sequence includes energy consumption data, raw material consumption data, and production process data; each resource consumption state sequence corresponding to the resource consumption data includes resource consumption indicator data corresponding to multiple different time points; multiple historical resource consumption state sequences include historical resource consumption indicator data corresponding to multiple different time points. Usually, an automated data acquisition system and manual recording can be used to monitor and record the use of various resources.

[0025] In step S12, the stationarity results of the resource consumption sequences are determined through autocorrelation analysis according to the resource consumption sequences.

[0026] Obtaining the initial mean value of each resource consumption sequence; Comparing the initial mean value with the final mean values ​​of the resource consumption sequences at all different time points in each resource consumption sequence, to obtain difference values ​​of each resource consumption sequence; According to the difference values ​​of the various resource consumption sequences, the inverse of the sum of all the difference values ​​is taken as the stationarity result of the various resource consumption sequences.

[0027] It should be noted that autocorrelation analysis is a statistical method that can discover the periodic patterns of resource consumption sequences. Autocorrelation analysis can also reveal the trend changes of resource consumption sequences over time, help managers understand the changing trends of resource utilization efficiency, and take corresponding measures to optimize them.

[0028] To obtain the initial mean of each resource consumption sequence, it is necessary to first collect the resource consumption sequences within a certain time range and remove some abnormal values; for each resource consumption sequence, Excel or Python tools can be used to calculate the initial mean of the resource consumption sequence within the time range.

[0029] The difference values ​​of the resource consumption sequences are obtained by comparing the initial mean value with the final mean values ​​of the resource consumption sequences at all different time points in the resource consumption sequences. For each time point, the mean value of the resource consumption sequence at the time point is compared with the initial mean value, and the difference value is calculated.

[0030] The stationarity of a resource consumption series means that the characteristics of the series in time do not change over time, that is, the statistical characteristics of the series, such as the mean, variance, and autocorrelation, remain constant in time. In resource consumption analysis, stationarity means that the long-term average and volatility of resource consumption are stable, which helps long-term planning and resource allocation.

[0031] In step S13, according to the stationarity results of the resource consumption sequences, the fluctuation degree of the resource consumption sequences is calculated to obtain the correlation degree values ​​between the resource consumption sequences.

[0032] According to the stationarity results of each resource consumption sequence, the fluctuation degree of each resource consumption sequence is calculated by the following formula: ; in, Indicates the degree of fluctuation of resource consumption sequence, represents the mean of the resource consumption series, Represents the time index of each data in the resource consumption sequence. Indicates that the resource consumption sequence is The consumption of time, The index identifier representing the resource consumption sequence. Indicates that the resource consumption sequence is and The relevance of the data at the moment The larger the value, the higher the resource consumption sequence. and The greater the correlation of the data at each moment, σ represents the standard deviation; dividing a plurality of resource consumption sequences with similar fluctuation degrees of the resource consumption sequences into a new resource consumption sequence group; Aggregation is performed based on correlation coefficient values ​​between any two resource consumption sequences in the new resource consumption sequence group to obtain correlation degree values ​​between the resource consumption sequences.

[0033] It should be noted that the degree of fluctuation of the resource consumption series over time usually refers to the degree of dispersion of the series values ​​around its mean, which can be measured by variance, standard deviation, coefficient of variation, range, and interquartile range. The degree of fluctuation of the resource consumption series reflects the stability and predictability of resource consumption over time. Understanding the degree of fluctuation of the resource consumption series helps enterprises to carry out resource planning and inventory management to avoid resource shortages or surpluses.

[0034] Mean of resource consumption series Calculated by the following formula: ; in, Indicates that the resource consumption sequence is The consumption of time, is the total number of data points in the resource consumption sequence.

[0035] Standard Deviation It is an important concept in statistics. It is used to measure the degree of dispersion of a set of data, that is, the degree of deviation between the data value and its average value (mean). Specifically, the standard deviation is defined as the square root of the arithmetic mean (i.e., variance) of the squared deviations of all data values ​​from their mean. The standard deviation can be calculated by the following steps: : S131. Calculate the average value (mean) of all data; S132. For each data value, calculate its difference (deviation) from the average value, and square the difference; S133. Calculate the average value of these square differences, i.e., the variance; S134. Calculate the square root of the variance to get the standard deviation .

[0036] Standard Deviation Quantifies the variability or dispersion of the data. A smaller standard deviation means that the data points are more closely distributed around the mean, while a larger standard deviation means that the data points are more dispersed around the mean. In a normal distribution, the standard deviation It also depends on the percentage distribution of the data. For example, about 68% of the data fall within one standard deviation of the mean. Within the range, about 95% of the data falls within two standard deviations About 99.7% of the data falls within three standard deviations. Inside.

[0037] Aggregate the correlation coefficient values ​​between the two resource consumption sequences in the new resource consumption sequence group to obtain the correlation degree values ​​between the resource consumption sequences. First, for each pair of resource consumption sequences in the new resource consumption sequence group, observe the characteristics and distribution of their data, and calculate the correlation coefficient between them through the Pearson correlation coefficient, the Spearman rank correlation coefficient or the Kendall rank correlation coefficient; for all resource consumption sequences in the new resource consumption sequence group, aggregate the correlation coefficients between them through the average, the median, and the weighted average, and the correlation degree values ​​between the resource consumption sequences can be obtained.

[0038] In step S14, a set of correlated resource consumption sequences between the resource consumption sequences is determined by comparing the correlation values ​​between the resource consumption sequences with the difference between the maximum correlation values.

[0039] Obtaining a maximum correlation value among the correlations between the resource consumption sequences; Subtracting the correlation degree values ​​between the resource consumption sequences from the maximum correlation degree value to obtain a correlation degree difference value; According to the correlation degree difference, a set of related resource consumption sequences between the resource consumption sequences is determined by the following method: When the correlation degree difference is greater than β, all resource consumption sequences are determined as the same resource consumption sequence set; When the correlation degree difference is less than or equal to β, it is determined that the resource consumption sequence does not belong to any resource consumption sequence set; Among them, β is the stable correlation value completed in advance.

[0040] It should be noted that the maximum correlation value among the correlations between the resource consumption sequences is obtained by observing the correlation values ​​of all resource consumption sequences and selecting the one with the largest correlation value as the maximum correlation value.

[0041] Stable correlation refers to the correlation between two or more variables that remains relatively constant over a certain period of time. This correlation will not fluctuate significantly over time. In the analysis of resource consumption sequences, stable correlation usually refers to the correlation between different resource consumption indicators remaining stable over a long period of time. It can understand the stable correlation between different resource consumption sequences and help enterprises optimize resource allocation. β is the pre-trained stable correlation value, which can be 0.8, 0.85, or 0.9.

[0042] In step S15, according to the set of related resource consumption sequences, each resource consumption sequence that may fail is obtained and stable decrease degree data of each resource consumption sequence that may fail is calculated.

[0043] Obtaining a mutation degree of a resource consumption sequence in the resource consumption sequence set; According to the mutation degree, anomaly detection is performed to generate resource consumption sequences of the various possible failures; Predicting based on the resource consumption sequences that may fail, to obtain predicted consumption parameters; According to the predicted consumption parameters, the stable decrease degree data of each of the resource consumption sequences that may fail is calculated by the following formula: ; in, is the stable descent degree data, It is The number of data points in a resource consumption sequence, Indicates A resource consumption sequence at time point and The difference between .

[0044] It should be noted that the mutation degree is a concept that describes the degree or magnitude of a sharp change in a system, process or variable at a certain point in time or within a period of time, and is usually significantly different from the normal behavior or historical trend of the system. The mutation degree can be used to identify mutations in resource consumption and inefficient links in the production process, so that measures can be taken to optimize operations and improve resource utilization efficiency. The mutation degree can also be used as an early indicator of equipment failure or performance degradation, and can predict whether the equipment is faulty.

[0045] Anomaly detection is the process of identifying points in the degree of mutation that do not conform to expected patterns or deviate from normal behavior. In the context of resource consumption sequences, anomaly detection can help identify sequences that may indicate failures or inefficiencies. Exemplarily, the anomaly detection method can use statistical methods, such as Z scores, IQR scores, etc.; it can also use machine learning methods, such as isolation forests, local outlier factors (LOF), autoencoders, etc., which are not limited by the present invention.

[0046] The stable decline describes the degree to which a variable or indicator decreases steadily over time. It includes the following meanings: Trend stability: refers to an indicator showing a stable downward trend over a period of time, rather than fluctuating or unstable changes; Rate of decrease: refers to the speed or amplitude of the decline being relatively stable, without a sharp rise or fall; Predictability: A stable downward trend usually means that future changes are also predictable. In resource management, a stable decline may mean a reduction in resource consumption, which helps to achieve sustainable resource utilization.

[0047] Furthermore, obtaining the mutation degree of the resource consumption sequence in the resource consumption sequence set includes: Obtaining the consumption trend and actual consumption trend change slope of the resource consumption sequence in the relevant resource consumption sequence set; According to the consumption trend and the actual consumption trend change slope, the actual consumption trend change slope is normalized to obtain a mutation degree of a resource consumption sequence in the resource consumption sequence set; The standardization includes: ; in, represents the mutation degree of the resource consumption sequence in the resource consumption sequence set, The proportional coefficient representing the degree of mutation, which can be a constant or a variable, is used to adjust the sensitivity of the degree of mutation. represents the actual consumption trend change slope of the resource consumption sequence in the resource consumption sequence set, Indicates the standard consumption trend change slope of the resource consumption sequence in the resource consumption sequence set.

[0048] It should be noted that the mutation degree is a concept that describes the degree or magnitude of a sharp change in a system, process or variable at a certain point in time or within a period of time, and is usually significantly different from the normal behavior or historical trend of the system. The mutation degree can be used to identify mutations in resource consumption and inefficient links in the production process, so that measures can be taken to optimize operations and improve resource utilization efficiency. The mutation degree can also be used as an early indicator of equipment failure or performance degradation, and can predict whether the equipment is faulty.

[0049] Obtain the consumption trend and actual consumption trend change slope of the resource consumption sequence in the relevant resource consumption sequence set. Clean and preprocess the resource consumption sequences in the collected relevant resource consumption sequence set, analyze the resource consumption trend using linear regression or time series analysis methods, and calculate the actual consumption trend change slope by observing the consumption trend.

[0050] The slope of the actual consumption trend change of the resource consumption sequence in the resource consumption sequence set can be calculated by the following formula: ; in, Indicates time The resource consumption, is the total number of data points in the resource consumption sequence.

[0051] The slope of the standard consumption trend change of the resource consumption sequence in the resource consumption sequence set can be calculated by the following formula: ; in, Indicates The actual consumption trend change slope of a resource consumption sequence, is the total number of sequences in the resource consumption sequence set. The actual consumption trend change slope of the resource consumption sequence can help monitor the efficiency of resource consumption in real time. A positive slope indicates that resource consumption is increasing over time, while a negative slope indicates that resource consumption is decreasing. The size of the slope can reflect the speed and trend of resource consumption changes.

[0052] In step S16, the resources of the chemical plant are optimally allocated using a genetic algorithm according to the stable decrease degree data.

[0053] According to the stable decrease degree data, the stable decrease threshold value is compared with the determined stable decrease threshold value to determine the resource consumption units that may fail corresponding to all resource consumption sequences that may fail; When the total production consumption of the resource consuming units that may fail and other resource consuming units that are unlikely to fail is the lowest, the genetic algorithm is used to optimize the allocation of resources in the chemical plant.

[0054] It should be noted that the stable decline threshold can be determined by analyzing the historical resource consumption sequence data, determining the consumption range under normal operating conditions, and setting a threshold. When the resource consumption is lower than this value, it is considered to be steadily declining. Statistical methods, such as standard deviation, coefficient of variation, etc., can also be used to determine the reasonable range of consumption changes, so that the stable decline threshold can be set as the average consumption level minus several times the standard deviation. Setting a stable decline threshold can optimize resource allocation, improve resource utilization efficiency, and reduce production costs by identifying the downward trend of resource consumption.

[0055] Genetic algorithm is a heuristic search algorithm that imitates the process of natural selection and is used to solve optimization and search problems. In the optimal allocation of resources in chemical plants, genetic algorithms can be used to find the optimal allocation plan for resource consumption to improve efficiency and reduce costs.

[0056] Specifically, the implementation process of optimizing resource allocation in a chemical plant using a genetic algorithm includes: Firstly, the optimization goal is defined as the minimum total production consumption of resource-consuming units that may fail and other resource-consuming units that are unlikely to fail, and the key parameters of the genetic algorithm, such as the population size, are set; a binary coding scheme is designed to represent the resource allocation scheme, in which each binary bit represents whether a resource-consuming unit is assigned to a specific task or production process, and then an initial population is randomly generated, in which each individual represents a possible resource allocation scheme; a fitness function is defined based on the minimum total production consumption of the resource-consuming units to evaluate the performance of each resource allocation scheme; based on the results evaluated by the fitness function, the individual with the highest fitness is selected from the final population, decoded into a resource allocation scheme, and implemented in actual production, thereby realizing the optimal allocation of resources in the chemical plant.

[0057] Through the optimal allocation of resources, we can ensure that resources are used efficiently in every step of the production process, reduce waste, and improve overall production efficiency; ensuring that the resource needs of key production links are met can prevent production accidents caused by resource shortages, ensure the safety of workers and the continuity of production; reasonable optimal allocation of resources can avoid equipment overload, thereby extending the service life of equipment and reducing maintenance costs and replacement frequency.

[0058] The following describes the working process of the present invention using a common scenario as an example. Figure 1 Schematic diagram of the working scenario of the method.

[0059] In a modern chemical plant, various resource consumption sequences in the production process of the chemical plant are obtained; based on the various resource consumption sequences, the stationarity results of the various resource consumption sequences are determined through autocorrelation analysis; based on the stationarity results of the various resource consumption sequences, the fluctuation degree of the various resource consumption sequences is calculated to obtain the correlation degree values ​​between the various resource consumption sequences; based on the correlation degree values ​​between the various resource consumption sequences, by comparing with the difference between the maximum correlation degrees, a set of related resource consumption sequences between the resource consumption sequences is determined; based on the set of related resource consumption sequences, various resource consumption sequences that may fail are obtained and the stable decline degree data of the various resource consumption sequences that may fail are calculated; based on the stable decline degree data, a genetic algorithm is used to optimize the allocation of resources of the chemical plant.

[0060] In summary, the present invention discloses an Internet of Things control method for a digital chemical plant, the method comprising obtaining each resource consumption sequence in the production process of the chemical plant, determining the stationarity results of each resource consumption sequence through autocorrelation analysis, thereby calculating the degree of fluctuation of each resource consumption sequence, obtaining the correlation degree value between each resource consumption sequence, and determining the set of related resource consumption sequences between resource consumption sequences by comparing with the difference between the maximum correlation degrees, and then obtaining each resource consumption sequence that may fail and calculating the stable decline degree data of each resource consumption sequence that may fail, and finally using a genetic algorithm to optimize the resource allocation of the chemical plant. This method has the following effects: it can timely determine the core factors of problems in the Internet of Things control of a chemical plant, and achieve optimal allocation of resources.

[0061] Reference Figure 2 The second embodiment of the present invention provides an Internet of Things control system for a digital factory, including: The data acquisition module is used to obtain the resource consumption sequence of each chemical plant during the production process; A stationarity determination module, used to determine the stationarity result of each resource consumption sequence through autocorrelation analysis according to each resource consumption sequence; A fluctuation degree calculation module, used to calculate the fluctuation degree of each resource consumption sequence according to the stationarity results of each resource consumption sequence, and obtain the correlation degree values ​​between each resource consumption sequence; A resource consumption sequence set determination module, configured to determine a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation degree values ​​between the resource consumption sequences with the difference between the maximum correlation degrees; A stable descent degree calculation module, used to obtain each resource consumption sequence that may fail according to the relevant resource consumption sequence set and calculate the stable descent degree data of each resource consumption sequence that may fail; The optimization allocation module is used to optimize the resource allocation of the chemical plant by using a genetic algorithm according to the stable decline degree data.

[0062] It should be noted that the Internet of Things control system for a digital factory provided in an embodiment of the present invention is used to execute all the process steps of the Internet of Things control method for a digital factory in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0063] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data processing terminal program. When the processor executes the computer program, the steps in the above-mentioned embodiments of the IoT control method for the digital factory are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the fluctuation degree calculation module.

[0064] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0065] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0066] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0067] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0068] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0069] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0070] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An Internet of Things control method for a digital factory, characterized in that: Executed by a computer, including: Obtain the resource consumption sequence of each chemical plant during the production process; According to each resource consumption sequence, determining the stationarity result of each resource consumption sequence through autocorrelation analysis; According to the stationarity results of the resource consumption sequences, the fluctuation degree of the resource consumption sequences is calculated to obtain the correlation degree values ​​between the resource consumption sequences; Determine a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation values ​​between the resource consumption sequences and the difference between the maximum correlation values; According to the set of related resource consumption sequences, obtaining each resource consumption sequence that may fail and calculating stable decrease degree data of each resource consumption sequence that may fail; According to the stable decline degree data, the resource allocation of the chemical plant is optimized by using a genetic algorithm.

2. The method for controlling the Internet of Things of a digital factory according to claim 1, characterized in that: The step of determining the stationarity results of each resource consumption sequence through autocorrelation analysis according to each resource consumption sequence includes: Obtaining the initial mean value of each resource consumption sequence; Comparing the initial mean value with the final mean values ​​of the resource consumption sequences at all different time points in each resource consumption sequence, to obtain difference values ​​of each resource consumption sequence; According to the difference values ​​of the various resource consumption sequences, the inverse of the sum of all the difference values ​​is taken as the stationarity result of the various resource consumption sequences.

3. The method for controlling the Internet of Things of a digital factory according to claim 1, characterized in that: The step of calculating the fluctuation degree of each resource consumption sequence according to the stationarity result of each resource consumption sequence to obtain the correlation degree value between each resource consumption sequence includes: According to the stationarity results of each resource consumption sequence, the fluctuation degree of each resource consumption sequence is calculated by the following formula: ； in, Indicates the degree of fluctuation of resource consumption sequence, represents the mean of the resource consumption series, Represents the time index of each data in the resource consumption sequence. Indicates that the resource consumption sequence is The consumption of time, The index identifier of the resource consumption sequence. Indicates that the resource consumption sequence is and The relevance of the data at the moment The larger the value, the higher the resource consumption sequence. and The greater the correlation of the data at each moment, σ represents the standard deviation; dividing a plurality of resource consumption sequences with similar fluctuation degrees of the resource consumption sequences into a new resource consumption sequence group; Aggregation is performed based on correlation coefficient values ​​between any two resource consumption sequences in the new resource consumption sequence group to obtain correlation degree values ​​between the resource consumption sequences.

4. The method for controlling the Internet of Things of a digital factory according to claim 1, characterized in that: The determining a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation degree values ​​between the resource consumption sequences with the difference between the maximum correlation degree values ​​includes: Obtaining a maximum correlation value among the correlations between the resource consumption sequences; Subtracting the correlation degree values ​​between the resource consumption sequences from the maximum correlation degree value to obtain a correlation degree difference value; According to the correlation degree difference, a set of related resource consumption sequences between the resource consumption sequences is determined by the following method: When the correlation degree difference is greater than β, all resource consumption sequences are determined as the same resource consumption sequence set; When the correlation degree difference is less than or equal to β, it is determined that the resource consumption sequence does not belong to any resource consumption sequence set; Among them, β is the stable correlation value completed in advance.

5. The method for controlling the Internet of Things of a digital factory according to claim 1, characterized in that: The step of acquiring each resource consumption sequence that may fail and calculating stable decrease degree data of each resource consumption sequence that may fail according to the set of related resource consumption sequences includes: Obtaining a mutation degree of a resource consumption sequence in the resource consumption sequence set; According to the mutation degree, anomaly detection is performed to generate resource consumption sequences of the various possible failures; Predicting based on the resource consumption sequences that may fail, to obtain predicted consumption parameters; According to the predicted consumption parameters, the stable decrease degree data of each of the resource consumption sequences that may fail is calculated by the following formula: ； in, is the stable descent degree data, It is The number of data points in a resource consumption sequence, Indicates A resource consumption sequence at time point and The difference between .

6. The method for controlling the Internet of Things of a digital factory according to claim 1, characterized in that: The method of optimizing resource allocation of the chemical plant by using a genetic algorithm according to the stable decline degree data includes: According to the stable decrease degree data, the stable decrease threshold value is compared to determine the resource consumption units that may fail corresponding to all resource consumption sequences that may fail; When the total production consumption of the resource consuming units that may fail and other resource consuming units that are unlikely to fail is the lowest, the genetic algorithm is used to optimize the allocation of resources in the chemical plant.

7. The method for controlling the Internet of Things of a digital factory according to claim 5, characterized in that: Obtaining the mutation degree of the resource consumption sequence in the resource consumption sequence set includes: Obtaining the consumption trend and actual consumption trend change slope of the resource consumption sequence in the relevant resource consumption sequence set; According to the consumption trend and the actual consumption trend change slope, the actual consumption trend change slope is normalized to obtain a mutation degree of a resource consumption sequence in the resource consumption sequence set; Wherein, the standardization includes: ； in, represents the mutation degree of the resource consumption sequence in the resource consumption sequence set, The proportional coefficient representing the degree of mutation is used to adjust the sensitivity of the degree of mutation. represents the actual consumption trend change slope of the resource consumption sequence in the resource consumption sequence set, Indicates the standard consumption trend change slope of the resource consumption sequence in the resource consumption sequence set.

8. An Internet of Things control system for a digital factory, characterized in that: include: The data acquisition module is used to obtain the resource consumption sequence of each chemical plant during the production process; A stationarity determination module, used to determine the stationarity result of each resource consumption sequence through autocorrelation analysis according to each resource consumption sequence; A fluctuation degree calculation module, used to calculate the fluctuation degree of each resource consumption sequence according to the stationarity results of each resource consumption sequence, and obtain the correlation degree values ​​between each resource consumption sequence; A resource consumption sequence set determination module, configured to determine a set of related resource consumption sequences between the resource consumption sequences by comparing the correlation degree values ​​between the resource consumption sequences with the difference between the maximum correlation degrees; A stable descent degree calculation module, used to obtain each resource consumption sequence that may fail according to the relevant resource consumption sequence set and calculate the stable descent degree data of each resource consumption sequence that may fail; The optimization allocation module is used to optimize the resource allocation of the chemical plant by using a genetic algorithm according to the stable decline degree data.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the Internet of Things control method for a digital factory as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the Internet of Things control method for a digital factory as described in any one of claims 1 to 7.

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