Intelligent adjustment method and system for intelligent safe energy-saving terminal based on Internet of Things

Abstract

The invention relates to the technical field of information perception of the Internet of Things, in particular to an intelligent adjustment method and system for an intelligent safe energy-saving terminal based on the Internet of Things, and the method comprises the steps: obtaining load data of a factory power grid at each collection moment in the current day and historical daily in a preset time period; analyzing load data change conditions of the current day and historical daily in a preset time period, and updating the load data of each moment of the current day; and predicting the load data at the future moment by using the updated load data at all moments acquired in the current day, and inputting the load data into the PID controller to control the voltage regulator to regulate the voltage. The invention aims to avoid the influence of the high-load equipment on the overall load data when the high-load equipment operates unstably, and obtain more accurate predicted load data, thereby achieving the precise adjustment of the voltage of the intelligent, safe and energy-saving terminal.

Description

Technical Field

[0001] The present application relates to the field of information perception technology of the Internet of Things, and specifically to an intelligent adjustment method and system for an intelligent, safe and energy-saving terminal based on the Internet of Things. Background Art

[0002] An intelligent, safe and energy-saving terminal is a device or system that integrates multiple technologies and has functions such as intelligent control, security and energy-saving management. As an important device of an intelligent, safe and energy-saving terminal, the voltage stabilizer is widely used in the field of industrial Internet of Things. A voltage stabilizer is an electronic device that converts unstable voltage output into stable voltage output. It can maintain the stability of the output voltage under high current load conditions. In the factory power grid, various devices may have different voltage requirements. As a terminal device, the voltage stabilizer can provide a variety of output voltage specifications to meet the needs of different devices. However, in some cases, if the input voltage fluctuates or changes suddenly, the voltage stabilizer may fail to make effective adjustments in time, resulting in unstable output voltage and affecting the normal operation of electrical equipment.

[0003] The traditional method generally uses the Internet of Things technology to obtain the sum of the load data of all operating equipment in the factory power grid at the same time to reflect the changing trend of the load data. However, when there are many types of equipment in the factory, different types of equipment have different degrees of influence on the changing trend of the load data during operation. When high-load equipment becomes unstable during operation, the obtained load data will be unreliable. Therefore, the voltage of the intelligent safety and energy-saving terminal cannot be accurately adjusted based on the predicted results of the load data change. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an intelligent adjustment method and system for an intelligent security and energy-saving terminal based on the Internet of Things. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides an intelligent adjustment method for an intelligent security and energy-saving terminal based on the Internet of Things, the method comprising the following steps:

[0006] Step 1: Obtain the load data of the factory power grid at each collection time of the current day and every day in the preset time period;

[0007] Step 2: Analyze the load data changes of the current day and the historical daily load data within the preset time period, and update the load data at each moment of the current day; specifically:

[0008] S1, according to the number of devices running at each moment of the current day, and the difference in the number of devices running at the previous moment and the difference in load data, obtain the dynamic change characteristics of the load at each moment of the current day;

[0009] S2, combining the difference in the dynamic change characteristics of the load at the same time on the current day and every day in history and the number of days between the current day and every day in history, determine the consistent characteristics of the load at each time on the current day and every day in history;

[0010] S3, use the rated power of the equipment to screen high-load equipment; analyze the peak and maximum distribution characteristics of the proportion of high-load equipment running at each moment in the target period at each moment, and combine the load consistency characteristics of the current day and the historical days at each moment and the difference in the number of high-load equipment running to determine the consistency of the working status of the current day and the historical days at each moment;

[0011] S4, using the consistency of the working status of the current day and the working status of each day in history at each moment to select several reference days at each moment of the current day from all the days in history; based on the consistency of the working status of all reference days at each moment with the working status of the current day at each moment, the number of high-load equipment running at each moment of the current day, and the time interval between each moment of the current day and the current moment, determine the factory working condition stability factor at each moment of the current day, which is used to update the load data at each moment of the current day;

[0012] Step 3: Use the updated load data collected at all times of the current day to predict the load data at future times, and input it into the PID controller to control the voltage regulator to adjust the voltage.

[0013] Preferably, the load data in step 1 is the sum of load data of all running devices at the corresponding collection time.

[0014] Preferably, the method for acquiring the dynamic change characteristics of the load at each moment of the current day is:

[0015] Calculate the proportion of the number of running devices at each moment of the current day to the total number of devices;

[0016] The proportion, the difference in the number of devices, and the difference in load data are integrated to obtain the dynamic change characteristics of the load at each moment of the current day.

[0017] Preferably, the method for determining the load consistency characteristics of the current day and the historical days at each time is:

[0018] The difference of the dynamic change characteristics of the load is forward fused with the interval days; the result of the forward fusion is used to determine the consistent characteristics of the load at each moment of the current day and each day in history;

[0019] Among them, the load consistency characteristics of the current day and the historical days at each moment are negatively correlated with the result of the forward fusion.

[0020] Preferably, the method for screening high-load devices is: obtaining the median of the rated powers of all devices in the factory power grid after arranging them from large to small; and recording the devices with rated powers greater than the median as high-load devices.

[0021] Preferably, the target period at each moment is composed of a preset number of moments before each moment.

[0022] Preferably, the method for determining the consistency between the working status of the current day and the working status of each day at each time in history is:

[0023] The proportion of high-load devices running at all times in the target period at each moment is sorted in chronological order to form a sequence of the proportion of high-load devices running in the target period at each moment;

[0024] Obtain the range, number of peaks, and the mean of the time intervals of all adjacent peaks in the sequence of the proportion of high-load devices running in the target period at each moment, so as to calculate the stability of the equipment operation at each moment;

[0025] Calculate the average value of the equipment operation stability at each moment of the current day and the equipment operation stability at the corresponding moment of each day in history;

[0026] Calculate the ratio of the difference between the load consistency characteristics of the current day and the historical days at each moment and the number of high-load devices in operation, and multiply the ratio result by the average value as the consistency of the working status of the current day and the historical days at each moment.

[0027] Preferably, the calculation method of the stability of the equipment operation at each moment is:

[0028] Taking the inverse of the product of the range and the peak number as the exponent of an exponential function with a natural constant as the base;

[0029] The product of the calculation result of the exponential function and the mean value is used as the stability of the equipment operation at each moment.

[0030] Preferably, the method of determining the plant operating stability factor at each moment of the current day based on the consistency of the working status of all reference days at each moment with the current day at each moment, the number of high-load equipment running at each moment of the current day, and the time interval between each moment of the current day and the current moment, for updating the load data at each moment of the current day, includes:

[0031] Calculate the cumulative sum of the consistency between the working status of all reference days and the current day at each moment;

[0032] Calculate the product value of the number of high-load devices running at each time of the current day and the time interval between each time of the current day and the current time;

[0033] The normalized value of the ratio of the accumulated sum value to the product value is used as the plant operating condition stability factor at each moment of the current day;

[0034] The product of the plant operating stability factor at each moment of the current day and the load data at the corresponding moment is used as the updated load data at the corresponding moment of the current day.

[0035] In the second aspect, an embodiment of the present application also provides an intelligent adjustment system for a smart, safe and energy-saving terminal based on the Internet of Things, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the system implements the steps of any one of the above-mentioned intelligent adjustment methods for smart, safe and energy-saving terminals based on the Internet of Things.

[0036] This application has at least the following beneficial effects:

[0037] This application obtains the dynamic change characteristics of the load at each moment of the current day based on the number of devices running at each moment of the current day, as well as the difference in the number of devices running at the previous moment and the difference in load data, which helps to identify the impact of dynamic changes on the load; combined with the difference in the dynamic change characteristics of the load at the same moment of the current day and every day in history, as well as the number of days between the current day and every day in history, the load consistency characteristics of the current day and every moment of the historical day are determined, which can be used to compare the pattern similarities between different days under a fixed production process in a short period of time in the factory; analyzes the peak and maximum distribution characteristics of the proportion of high-load devices running at each moment in the target period of each moment, and combines the load consistency characteristics of the current day and every day in history at each moment and the number of days between the current day and every day in history. The difference in the number of load devices is used to determine the consistency of the working status at each moment of the current day and each day in history, and further explore the similarity of the load status of high-load devices at the same moment on different days; based on the consistency of the working status of all reference days at each moment with the current day, the number of high-load devices running at each moment of the current day, and the time interval between each moment of the current day and the current moment, the factory operating stability factor at each moment of the current day is determined to update the load data at each moment of the current day, so as to obtain load data that can better reflect the overall load changes of the factory power grid, avoid the impact of high-load equipment on the overall load data when it is unstable, and obtain more accurate predicted load data, thereby realizing precise adjustment of the voltage of the intelligent, safe and energy-saving terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flow chart of the intelligent adjustment method for an intelligent, secure and energy-saving terminal based on the Internet of Things provided in this application;

[0040] Figure 2 A flow chart of the update process of the load data at each moment of the current day provided by this application;

[0041] Figure 3 A schematic diagram of the load data of the current day and the load data of the historical d-th day provided for one embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of the intelligent adjustment method and system of the intelligent security and energy-saving terminal based on the Internet of Things proposed in this application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0044] The specific scheme of the intelligent adjustment method and system for intelligent security and energy-saving terminals based on the Internet of Things provided by the present application is described in detail below with reference to the accompanying drawings.

[0045] An embodiment of the present application provides an intelligent adjustment method and system for an intelligent, secure and energy-saving terminal based on the Internet of Things.

[0046] Specifically, the following intelligent adjustment method for intelligent security and energy-saving terminals based on the Internet of Things is provided. Figure 1 , the method comprises the following steps:

[0047] Step 1: Obtain the load data of the factory power grid at each collection time of the current day and every day in the preset time period.

[0048] Through the Internet of Things technology, the load data time series of the factory power grid on the current day and the historical daily load data within the preset time period are obtained. Each element in the load data time series represents the sum of the load data of all running devices at the corresponding collection time, and it is stored in the industrial cloud platform to facilitate the subsequent analysis of the load data.

[0049] The preset time period is all days within the current week, and the frequency of time collection is once every minute.

[0050] Step 2: Analyze the load data changes of the current day and the historical daily load data within the preset time period, and update the load data at each moment of the current day.

[0051] A voltage stabilizer is a device that stabilizes the output voltage. It consists of a voltage regulating circuit, a control circuit, and a servo motor. When the input voltage or load changes, the control circuit samples, compares, and amplifies, and then drives the servo motor to rotate, causing the position of the voltage regulator's carbon brush to change. The output voltage is kept stable by automatically adjusting the coil turns ratio.

[0052] When using a voltage stabilizer to adjust the voltage, it is necessary to accurately adjust the voltage based on the predicted future load data. Based on this, this application determines the updated load data at each moment of the current day by analyzing the changes in load data for the current day and the historical daily load data within a preset time period.

[0053] In this application, the update process flow chart of the load data at each moment of the current day is as shown in the attached Figure 2 As shown, specifically:

[0054] S1, according to the number of running devices at each moment of the current day, and the difference in the number of running devices and load data at the previous moment, obtain the dynamic change characteristics of the load at each moment of the current day.

[0055] The changing trend of load data is a key factor in establishing an accurate prediction model. By predicting load data, we can understand the changes in future loads in advance and improve the accuracy of load data prediction at future moments. The number of devices running in the factory directly affects the size of the load data. Because each device consumes a certain amount of power when running, changes in the number of running devices will cause changes in the total load data.

[0056] In a factory power grid, the load data at each moment is affected by a variety of dynamic factors, such as equipment startup and shutdown, production plan adjustments, sudden failures, etc. Analyzing the changing trend of load data at each moment can help identify the impact of dynamic changes on the load.

[0057] Accordingly, the present application obtains the dynamic change characteristics of the load at each moment of the current day according to the number of devices running at each moment of the current day, as well as the difference in the number of devices running at the previous moment and the difference in load data.

[0058] Preferably, the method for acquiring the dynamic change characteristics of the load at each moment of the current day is: obtaining the number of devices running at each moment of the current day through Internet of Things technology, and calculating the proportion of the number of devices running at each moment of the current day in the number of all devices; fusing the proportion, the difference in the number of devices, and the difference in load data to obtain the dynamic change characteristics of the load at each moment of the current day.

[0059] It can be understood that fusion can be divided into forward fusion and reverse fusion. This embodiment adopts the forward fusion method. Forward fusion is a fusion method such as addition and multiplication between data. The specific forward fusion method is determined by the implementer according to the actual situation. The application does not impose any special restrictions.

[0060] In a specific implementation, taking the load dynamic change characteristic at the xth moment of the current day as an example, the load dynamic change characteristic B at the xth moment of the current day is calculated. x The expression is: Among them, B x Indicates the dynamic change characteristics of the load at the xth moment of the current day, N x Indicates the number of devices running at the xth moment, N x-1 It represents the number of devices running at the x-1th moment, and N represents the number of all devices in the factory. It is used to characterize the production scale of the factory at the xth moment. The larger the production scale, the more equipment is running in the factory at that moment. x,x-1 Represents the absolute value of the difference between the load data at the xth moment and the load data at the x-1th moment.

[0061] S2, combining the difference in the dynamic change characteristics of the load at the same time on the current day and every day in history and the number of days between the current day and every day in history, determines the consistent characteristics of the load at each time on the current day and every day in history.

[0062] In factories, the operation of many devices is arranged according to fixed working hours or production processes, which will cause the load data to show periodic changes. For example, the production process of a factory is generally carried out according to fixed shifts and task arrangements, and the start, stop and operation status of most equipment will have similar patterns in a day. In this case, the change pattern of daily load data is relatively similar.

[0063] In this embodiment, as shown in the attached Figure 3 As shown, Figure 3 This is a schematic diagram of the load data of the current day and the load data of the historical d-day. It can be seen that the load data of the current day and the load data of the historical d-day are similar most of the time, but the similarity suddenly decreases at the xth moment, indicating that the dynamic consistency of the factory power grid is poor at this moment.

[0064] Based on this, the present application combines the difference in the dynamic change characteristics of the load at the same time on the current day and every day in history and the number of days between the current day and every day in history to determine the consistent characteristics of the load at each time on the current day and every day in history.

[0065] Preferably, the method for determining the load consistency characteristics of the current day and each moment of each day in history is: forward fusion of the difference in the dynamic change characteristics of the load and the interval days; using the result of forward fusion to determine the load consistency characteristics of the current day and each moment of each day in history; wherein, the load consistency characteristics of the current day and each moment of each day in history are negatively correlated with the result of the forward fusion.

[0066] In a specific implementation, taking the load consistency feature of the current day and the historical d-th day at the x-th moment as an example, the load consistency feature X of the current day and the historical d-th day at the x-th moment is calculated. x,d The expression is: X x,d =exp(-D d ×|B x -B x,d |); where X x,d Indicates the load consistency characteristics of the current day and the historical day d at the xth moment, B x It represents the dynamic change characteristics of the load at the xth moment of the current day. exp is an exponential function with the natural constant e as the base. B x,d represents the dynamic change characteristics of the load at the xth moment in the dth day of history, |B x -B x,d |It means that the closer the change pattern of the load data at the xth moment of the current day is to the corresponding moment in the historical day, the higher the similarity. d It indicates the number of days between the current day and the dth day in history. The fewer the number of days, the more similar the workflow of the equipment in the factory at the xth moment of the current day is to the workflow of the equipment at the xth moment of the dth day in history.

[0067] In an actual factory environment, some devices may bear most of the load, while other devices may bear less load. When high-load devices account for a large proportion of the devices running at each moment, the increase or decrease of high-load devices may have a more significant impact on the trend of load data changes.

[0068] S3, use the rated power of the equipment to screen high-load equipment; analyze the peak and maximum distribution characteristics of the proportion of high-load equipment running at each moment in the target period at each moment, and combine the load consistency characteristics of the current day and the historical days at each moment and the difference in the number of high-load equipment running, to determine the consistency of the working status of the current day and the historical days at each moment.

[0069] The rated power of a device is an important indicator of its potential load capacity. Devices with higher rated power can usually bear greater loads within the same operating range.

[0070] Based on this, this embodiment uses the rated power of the equipment to screen high-load equipment. The rated power of all equipment in the factory power grid is arranged from large to small to obtain a rated power sequence. Equipment with a rated power greater than the median of the rated power sequence is recorded as a high-load equipment; otherwise, it is recorded as a low-load equipment.

[0071] This embodiment takes the xth moment as an example, and records the period consisting of a preset number of moments before the xth moment as the target period of the xth moment. In this embodiment, the preset number is 60. In the target period of the xth moment, calculate the proportion of high-load devices running at each moment Where N′ x,b N is the number of high-load devices running at the bth moment in the target period at the xth moment. x,b is the number of devices running at the bth moment in the target period at the xth moment. The proportions of high-load devices running at all moments in the target period at the xth moment are arranged in chronological order to form a sequence of the proportions of high-load devices running in the target period at the xth moment.

[0072] First, the stability of the equipment operation at each moment. Taking the stability of the equipment operation at the xth moment as an example, the stability of the equipment operation at the xth moment W is calculated. x The expression is: W x =exp(-|N″ x,max -N″ x,min |×M x )×T x ; Among them, W x represents the stability of the equipment operation at the xth moment, exp represents the exponential function with the natural constant e as the base, M x It indicates the peak number of the proportion of high-load devices running in the target period at the xth moment. The greater the number, the more frequent the start and stop of high-load devices at the xth moment, and the worse the stability of the device operation at the xth moment. xIndicates the mean of the time intervals of all adjacent peaks in the sequence of the proportion of high-load equipment in operation during the target period at the xth moment. The larger the mean, the more dispersed the impact of the start and stop of high-load equipment on the power grid. N″ x,max Indicates the maximum value of the proportion of high-load devices running in the target period at the xth moment, N″ x,min Indicates the minimum value in the sequence of the proportion of high-load devices running in the target period at the xth moment, |N″ x,max -N″ x,min |Used to characterize the range of the proportion of high-load devices running in the target period at the xth moment. The smaller the range, the more stable the number of high-load devices running in the target period at the xth moment.

[0073] Changes in high-load equipment will cause dynamic responses in the power grid. The start and stop of high-load equipment will have a greater impact on the power grid. In the target period at each moment, if the number of high-load motors in operation changes, the voltage, frequency and other parameters of the factory power grid will be dynamically adjusted accordingly. This dynamic change will affect the operating status and load data of other equipment in the factory, and will also affect the judgment of the similarity of load data at each moment in historical days.

[0074] Furthermore, the consistency degree of the working status of the current day and the historical day at each moment is calculated. Taking the consistency degree of the working status of the current day and the historical day d at the xth moment as an example, the consistency degree of the working status of the current day and the historical day d at the xth moment is X′ x,d The expression is: Among them, X′ x,d Indicates the consistency between the working status of the current day and the working status of the dth day in history at the xth moment, X x,d Indicates the load consistency characteristics of the current day and the historical day d at the xth moment, W x,d It represents the mean of the stability of the equipment operation at the xth moment of the current day and the stability of the equipment operation at the xth moment of the dth day in history. The larger the mean, the more reliable the load data corresponding to the xth moment. N′ x Indicates the number of high-load devices running at the xth moment of the current day, N′ x,d Indicates the number of high-load devices running at the xth moment in the dth day of history, |N′ x -N′ x,d The smaller | is, the closer the number of high-load devices at the xth moment is to the number of high-load devices at the xth moment on the dth day in history. x,d The higher the credibility.

[0075] S4, using the consistency of the working status of the current day and each moment of each day in history, several reference days for each moment of the current day are screened out from all the days in history; based on the consistency of the working status of all reference days at each moment with the current day at each moment, the number of high-load equipment running at each moment of the current day, and the time interval between each moment of the current day and the current moment, the factory operating stability factor at each moment of the current day is determined to update the load data at each moment of the current day.

[0076] Voltage stabilizers usually have a certain response time. When the input voltage of the factory power grid fluctuates, the voltage stabilizer needs a certain amount of time to effectively adjust. If the frequency of voltage fluctuations is too high, or the fluctuation amplitude is large, the voltage stabilizer may not be able to keep up completely, resulting in unstable voltage output, which will affect the load.

[0077] In factory workshops or data centers with large motors, transformers, or high-frequency communication equipment, the electromagnetic environment is complex. For example, when a large three-phase asynchronous motor is started, a strong electromagnetic field is generated. This electromagnetic field may interfere with the nearby voltage sensor used to measure the load, causing the voltage signal output by the sensor to fluctuate and become noisy, which in turn leads to deviations in the calculated load data.

[0078] For the consistency of the working status of the current day and every day in history at each moment, all reference days at each moment of the current day are screened out from all days in history. The screening method is: take the reference day at the xth moment of the current day as an example: record the historical days whose working status consistency at the xth moment is greater than or equal to the preset status consistency threshold as the reference day at the xth moment of the current day. In this embodiment, the preset status consistency threshold is set to 0.7, and the specific value can be set by the implementer.

[0079] Furthermore, the present application determines the factory operating condition stability factor at each moment of the current day based on the degree of consistency in the working status of all reference days at each moment with the current day, the number of high-load equipment running at each moment of the current day, and the time interval between each moment of the current day and the current moment.

[0080] Preferably, in this embodiment, the method for determining the factory operating condition stability factor at each moment of the current day is: calculate the cumulative sum of the consistency of the working conditions of all reference days and the current day at each moment at each moment; calculate the product of the number of high-load equipment running at each moment of the current day and the time interval between each moment of the current day and the current moment; use the normalized value of the ratio of the cumulative sum value to the product value as the factory operating condition stability factor at each moment of the current day; use the product of the factory operating condition stability factor at each moment of the current day and the load data at the corresponding moment as the updated load data at the corresponding moment of the current day.

[0081] In a specific implementation, taking the factory operating stability factor at the xth moment of the current day as an example, the factory operating stability factor K at the xth moment of the current day is calculated. x The expression is: Among them, K x represents the stability factor of the plant condition at the xth moment of the current day, norm represents the normalization function, C represents the number of reference days at the xth moment, and X′ x,c Indicates the consistency between the working status of the current day and the cth reference day at the xth moment, N′ x Indicates the number of high-load devices running at the xth moment of the current day. The larger the number, the more likely the factory power grid is in a high-load state. Under high-load conditions, the processing capacity of the data filtering system may be insufficient, resulting in deviations in the original data and reduced data credibility. x It indicates the time interval between the xth moment of the current day and the current moment. The shorter the interval, the more important the prediction of load data at future moments is and the higher the data credibility is.

[0082] The larger the factory operating stability factor at any moment of the current day, the greater the weight should be given to the load data at that moment to ensure that the historical load data better reflects the changing trend of the load data in the factory power grid.

[0083] The plant operating stability factor K at the xth moment of the current day x As the load data S at the corresponding time x The weight of the load data S′ after the update at the xth moment of the current day is obtained x , the expression is: S′ x =S x ×K x .

[0084] At this point, the updated load data for each moment of the current day can be obtained through step 2.

[0085] Step 3: Use the updated load data collected at all times of the current day to predict the load data at future times, and input it into the PID controller to control the voltage regulator to adjust the voltage.

[0086] The updated load data collected at all times of the current day are combined into an updated load data time series in chronological order, and the autoregressive moving average model ARIMA is used to obtain the predicted load data of the updated load data time series. The predicted load data and the expected stable load state are input into the PID controller, and a voltage regulator control instruction at the current moment is output to control the voltage of the voltage regulator. Among them, the operation process of the autoregressive moving average model ARIMA and the PID controller is a well-known technology, and this embodiment will not be repeated.

[0087] It should be understood that the PID controller makes calculations based on the deviation between the predicted load data and the expected steady load state. For example, if the predicted load data indicates that a high load condition is about to occur, which may cause the regulator output voltage to drop (assuming that the regulator is at the edge of its regulation capability), the PID controller will calculate a command to increase the output voltage so that the regulator can adjust the output voltage in advance to cope with the upcoming load change.

[0088] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent adjustment system for an intelligent, safe and energy-saving terminal based on the Internet of Things, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things are implemented in any one of the above methods.

[0089] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0090] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0091] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0092] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. An intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things, characterized in that: The method comprises the following steps: Step 1: Obtain the load data of the factory power grid at each collection time of the current day and every day in the preset time period; Step 2: Analyze the load data changes of the current day and the historical daily load data within the preset time period, and update the load data at each moment of the current day; specifically: S1, according to the number of devices running at each moment of the current day, and the difference in the number of devices running at the previous moment and the difference in load data, obtain the dynamic change characteristics of the load at each moment of the current day; S2, combining the difference in the dynamic change characteristics of the load at the same time on the current day and every day in history and the number of days between the current day and every day in history, determine the consistent characteristics of the load at each time on the current day and every day in history; S3, use the rated power of the equipment to screen high-load equipment; analyze the peak and maximum distribution characteristics of the proportion of high-load equipment running at each moment in the target period at each moment, and combine the load consistency characteristics of the current day and the historical days at each moment and the difference in the number of high-load equipment running to determine the consistency of the working status of the current day and the historical days at each moment; S4, using the consistency of the working status of the current day and the working status of each day in history at each moment to select several reference days at each moment of the current day from all the days in history; based on the consistency of the working status of all reference days at each moment with the working status of the current day at each moment, the number of high-load equipment running at each moment of the current day, and the time interval between each moment of the current day and the current moment, determine the factory working condition stability factor at each moment of the current day, which is used to update the load data at each moment of the current day; Step 3: Use the updated load data collected at all times of the current day to predict the load data at future times, and input it into the PID controller to control the voltage regulator to adjust the voltage.

2. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 1, characterized in that: The load data in step 1 is the sum of the load data of all running devices at the corresponding collection time.

3. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 1, characterized in that: The method for obtaining the dynamic load change characteristics at each moment of the current day is: Calculate the proportion of the number of running devices at each moment of the current day to the total number of devices; The proportion, the difference in the number of devices, and the difference in load data are integrated to obtain the dynamic change characteristics of the load at each moment of the current day.

4. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 1, characterized in that: The method for determining the load consistency characteristics of the current day and the historical days at each time is as follows: The difference of the dynamic change characteristics of the load is forward fused with the interval days; the result of the forward fusion is used to determine the consistent characteristics of the load at each moment of the current day and each day in history; Among them, the load consistency characteristics of the current day and the historical days at each moment are negatively correlated with the result of the forward fusion.

5. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 1, characterized in that: The method for screening high-load devices is: obtaining the median of the rated powers of all devices in the factory power grid after arranging them from large to small; and recording the devices with rated powers greater than the median as high-load devices.

6. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 1, characterized in that: The target period at each moment is composed of a preset number of moments before each moment.

7. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 6, characterized in that: The method for determining the consistency between the working status of the current day and the working status of each day at each time in history is as follows: The proportion of high-load devices running at all times in the target period at each moment is sorted in chronological order to form a sequence of the proportion of high-load devices running in the target period at each moment; Obtain the range, number of peaks, and the mean of the time intervals of all adjacent peaks in the sequence of the proportion of high-load devices running in the target period at each moment, so as to calculate the stability of the equipment operation at each moment; Calculate the average value of the equipment operation stability at each moment of the current day and the equipment operation stability at the corresponding moment of each day in history; Calculate the ratio of the difference between the load consistency characteristics of the current day and the historical days at each moment and the number of high-load devices in operation, and multiply the ratio result by the average value as the consistency of the working status of the current day and the historical days at each moment.

8. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 7, characterized in that: The calculation method of the stability of the equipment operation at each moment is: Taking the inverse of the product of the range and the peak number as the exponent of an exponential function with a natural constant as the base; The product of the calculation result of the exponential function and the mean value is used as the stability of the equipment operation at each moment.

9. The intelligent adjustment method for an intelligent, safe and energy-saving terminal based on the Internet of Things according to claim 1, characterized in that: The method determines the factory operating stability factor at each moment of the current day based on the consistency of the working status of all reference days at each moment with the current day at each moment, the number of high-load equipment running at each moment of the current day, and the time interval between each moment of the current day and the current moment, and is used to update the load data at each moment of the current day, including: Calculate the cumulative sum of the consistency between the working status of all reference days and the current day at each moment; Calculate the product value of the number of high-load devices running at each time of the current day and the time interval between each time of the current day and the current time; The normalized value of the ratio of the accumulated sum value to the product value is used as the plant operating condition stability factor at each moment of the current day; The product of the plant operating stability factor at each moment of the current day and the load data at the corresponding moment is used as the updated load data at the corresponding moment of the current day.

10. An intelligent, safe and energy-saving terminal intelligent adjustment system based on the Internet of Things, comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the intelligent adjustment method of the intelligent security and energy-saving terminal based on the Internet of Things as described in any one of claims 1 to 9 are implemented.

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