An intelligent dispatching early warning detection method and system for power supply and distribution

Abstract

The present invention relates to the field of control technology. More specifically, the present invention relates to an intelligent scheduling early warning detection method and system for power supply and distribution. The method includes: clustering the daily power consumption of the collected power consumption units for a set number of days to obtain multiple clustering clusters, where each clustering cluster represents a power consumption sequence during a period with similar power consumption behavior within a day; determining all power consumption periods based on the power consumption sequences of each day; constructing a complete power consumption cycle; screening the power consumption cycle to obtain an effective cycle; calculating a first ratio and a second ratio within the effective cycle; taking the difference between the second mean value and the first mean value as the power consumption surplus; calculating the ratio of the current remaining power of the power consumption unit to the power consumption surplus. If this ratio is less than a preset surplus threshold, an early warning of insufficient power supply for the power consumption unit is issued. The purpose of the present invention is to ensure the stability of power supply by formulating effective power management strategies and optimizing the allocation of power resources.

Description

Technical Field

[0001] The present invention generally relates to the field of control technology. More specifically, the present invention relates to an intelligent scheduling early warning detection method and system for power supply and distribution. Background Art

[0002] Power supply and distribution scheduling is the core link of the operation of the power system. Its task is to ensure the safe, stable and economic operation of the power system by commanding, supervising and managing the operation of power production, which includes the coordination and control of multiple links such as power generation, transmission, transformation, distribution and power consumption.

[0003] The power demand of power consumption units (such as residential areas, commercial areas, administrative areas, etc.) fluctuates with factors such as time (such as different periods of a day), which poses great challenges to power supply and distribution scheduling. In order to optimize power supply and distribution, power companies need to take a series of measures. However, traditional power grid scheduling often relies on the experience of dispatchers and a set of fixed operating rules, resulting in limited flexibility and accuracy of scheduling decisions in the face of emergencies or non-standard operating situations. In addition, the power grid scheduling of dispatchers often tends to be after-the-fact scheduling and cannot achieve advance prediction and planning.

[0004] Therefore, in order to address these challenges, an intelligent scheduling early warning detection method and system are needed to achieve the optimal allocation of power resources, reduce the risk of power supply shortage, and early warn of possible power shortage problems. Summary of the Invention

[0005] To solve the technical problem of uneven distribution of power resources in the above-mentioned traditional power scheduling, the present invention provides solutions in the following aspects.

[0006] In a first aspect, an intelligent scheduling early warning detection method for power supply and distribution includes:

[0007] Collect the daily power consumption of power consumption units for a set number of days, cluster the daily power consumption to obtain a plurality of clustering clusters, and each clustering cluster represents a power consumption sequence of a period with similar power consumption behavior within a day;

[0008] Based on the power consumption sequence of each period of each day, determine all power consumption periods; select any power consumption period as the target period, use the power consumption period on the day after a preset number of days after the target period as the to-be-determined period, calculate the matching coefficient between the target period and the to-be-determined period and the periods before and after the to-be-determined period, select the power consumption period with the highest matching coefficient as the same-period power consumption period, record the number of days as the cycle length, use the power consumption period with the highest matching coefficient as the new target period, and iteratively calculate the matching coefficient until a power consumption period that coincides with any power consumption period within the same cycle is found, that is, construct a complete power consumption cycle;

[0009] Calculate the cycle index of the electricity consumption cycle. When the corresponding cycle index is less than the preset cycle threshold, expand the cycle length of the electricity consumption cycle according to a preset ratio and recalculate the cycle index until the cycle index is greater than the preset cycle threshold, and then stop expanding the cycle length. Take the electricity consumption cycle with the expanded cycle length as the effective cycle;

[0010] Calculate the average electricity consumption of all electricity consumption periods within the effective cycle as the first average value; calculate the average electricity consumption corresponding to each electricity consumption period within the effective cycle, and take the maximum value of the average electricity consumption corresponding to all electricity consumption periods as the second average value; take the difference between the second average value and the first average value as the electricity consumption surplus;

[0011] Calculate the ratio of the current remaining electricity of the electricity consumption unit to the electricity consumption surplus. If the ratio is less than the preset surplus threshold, issue a warning that the electricity supply of the electricity consumption unit is insufficient.

[0012] The present invention first clusters the daily electricity consumption to initially identify the electricity consumption behavior patterns of electricity consumption units, secondly captures the electricity consumption rules of electricity consumption units within a long time range, and then constructs an electricity consumption cycle. Further, it screens out cycles with stable and predictable electricity consumption rules to provide a reliable basis for subsequent electricity consumption warnings. Then, by calculating the electricity consumption surplus, it understands the electricity consumption fluctuations of electricity consumption units within the effective cycle and the potential growth space of electricity consumption demand. By calculating the ratio of the current remaining electricity to the electricity consumption surplus and comparing it with the preset surplus threshold, when the ratio is less than the threshold, a warning of insufficient electricity supply is issued, thereby discovering the potential electricity consumption risks of electricity consumption units in advance, providing timely decision support for power dispatching and power supply management, and avoiding problems such as power outages or unstable power supply caused by insufficient electricity consumption.

[0013] In order to ensure the stability and reliability of the power system, the present invention optimizes the use of power resources through prediction and planning, especially in the face of the uncertainty of renewable energy and the fluctuations of electricity consumption demand.

[0014] In one embodiment, the clustering adopts ordered sample clustering.

[0015] In one embodiment, the steps of determining all electricity consumption periods include:

[0016] First step: Starting from the first day of the set number of days, calculate the electricity consumption coefficients between the first day and the second day, the first day and the third day, and so on until the last day of the set number of days. When the electricity consumption coefficient between the first day and any day is greater than or equal to the preset coefficient threshold, all the daily electricity consumptions between the first day and the corresponding day are used as an electricity consumption period;

[0017] Second step: Starting from the next day after the end day of the electricity consumption period determined in the first step, repeat the first step to obtain a new electricity consumption period;

[0018] Step 3: After determining a new power consumption period each time, update the starting point to the day after the end date of the current power consumption period, and repeat Step 2 until all the specified days are classified into power consumption periods.

[0019] In one embodiment, ; where is the power consumption coefficient between the first day and the second day, is the total number of power consumption sequences in the periods included in the first day, is the total number of power consumption sequences in the periods included in the second day, is the start time of the th power consumption sequence in the first day, is the end time of the th power consumption sequence in the first day, is the start time of the th power consumption sequence in the second day, is the start time of the th power consumption sequence in the second day, is the average power consumption of the th power consumption sequence in the first day, is the average power consumption of the th power consumption sequence in the second day, where by limiting the maximum value, it is ensured that the periods selected for the first day and the second day match in time, thereby reducing the analysis error caused by time differences.

[0020] In one embodiment, the process of obtaining the matching coefficient includes:

[0021] Calculating the number of overlapping days between the target period and the pending period in time, and calculating the absolute value of the difference between the average power consumption of the target period and the average power consumption of the pending period;

[0022] Taking the product of the number of overlapping days between the target period and the pending period in time and the absolute value of the difference between the average power consumption of the target period and the average power consumption of the pending period as the matching coefficient.

[0023] In one embodiment, the cycle index satisfies the relational expression:

[0024] ; where is the cycle index, is the number of days in the th power consumption period in the cycle, is the average power consumption of the th power consumption period in the cycle, is the natural exponential function.

[0025] In one embodiment, the process of obtaining the margin threshold includes:

[0026] Calculate the average number of electricity - using days within the effective period;

[0027] Obtain the number of days since the current moment entered the effective period, and use the ratio of the difference between the average number of electricity - using days within the effective period and the number of days since the current moment entered the effective period to the average number of electricity - using days within the effective period as the margin threshold.

[0028] In a second aspect, an intelligent power supply and distribution scheduling early - warning detection system includes: a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above - mentioned intelligent power supply and distribution scheduling early - warning detection method is implemented.

[0029] The beneficial effects of the present invention are:

[0030] By clustering the daily electricity consumption of the electricity - using unit for a set number of days, the electricity - using sequences during similar - behavior periods can be grouped into one category, thereby identifying different electricity - using behavior patterns. Based on the clustering results, the electricity - using periods are divided. By selecting the target period and the to - be - determined period, calculating the matching coefficient, and iterating this process, an electricity - using cycle can be constructed, that is, the periodic law of electricity - using behavior is identified. Analyzing the electricity - using cycle helps predict future electricity demand, provides a reliable basis for power scheduling, and optimizes the allocation and use of power resources.

[0031] Furthermore, by further evaluating the periodicity and screening out the effective periods with stable periodicity, calculating the electricity margin based on the effective periods, power companies can more effectively allocate power resources to meet the electricity demand at different times and improve resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] By reading the following detailed description with reference to the accompanying drawings, the above - mentioned and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understandable. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, where:

[0033] Figure 1 is the flowchart of steps S1 - S6 in an intelligent power supply and distribution scheduling early - warning detection method according to an embodiment of the present invention.

[0034] Figure 2 is the structural block diagram of an intelligent valve control system based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0036] The application scenario of the present invention is: according to the electricity consumption habits of electricity-consuming units, intelligent power supply and distribution scheduling is completed.

[0037] An embodiment of the present invention discloses an intelligent scheduling early warning detection method for power supply and distribution, referring to Figure 1 , including steps S1 - S6, specifically as follows:

[0038] S1: Collect the daily electricity consumption of the electricity-consuming unit for a set number of days, perform clustering on the daily electricity consumption, and obtain multiple clustering clusters. Each clustering cluster represents the electricity consumption sequence of a period with similar electricity consumption behavior within a day.

[0039] First, collect the daily electricity consumption data of the electricity-consuming unit. These data can be the daily electricity consumption (in kilowatt-hours) of the electricity-consuming unit in the past month, half-year, or year in the power department's collection system. In the implementation of the present invention, the daily electricity consumption of the electricity-consuming unit in the past month is selected for collection. Among them, the electricity-consuming unit can be a residential area, an administrative area, or a commercial area.

[0040] Then, preprocess the collected data, including filling in missing data, removing abnormal data, etc.

[0041] Finally, take the electricity consumption data of each day in the past month as a sample point, and use ordered sample clustering to perform clustering analysis on the daily electricity consumption of each day in the past month. Group the different periods of a day according to the similarity of electricity consumption behavior, and then obtain multiple clustering clusters corresponding to each day. It should be noted that each clustering cluster is actually a subsequence, representing the electricity consumption data within a specific period. This specific period can be different periods of a day (such as morning peak, evening peak, etc.).

[0042] It should be noted that using ordered sample clustering can effectively distinguish the periods of daily electricity consumption changes. This means that within the same clustering cluster, the electricity consumption does not change much, showing similar electricity consumption behavior. For example, a clustering cluster may contain the morning peak period of a day, and the electricity consumption during this period may show a similar increasing or decreasing trend. Between different clustering clusters, the electricity consumption changes significantly, and there are obvious differences in electricity consumption behavior. For example, the electricity consumption changes during the morning peak and the evening peak may belong to different clustering clusters because their electricity consumption patterns and peak time points in a day are different.

[0043] S2: Determine all power consumption periods based on the sequence of power consumption data for each day of the time period.

[0044] Based on the daily power consumption sequence for each day obtained from S1 above, compare the similarity between the power consumption patterns calculated at different times of the power consumption sequence. Through comparison, it can be determined whether the power consumption behaviors in different time periods are similar.

[0045] In one embodiment, specifically, in the first step, starting from the first day of the set number of days, calculate the power consumption coefficients between the first day and the second day, the first day and the third day, until the first day and the last day of the set number of days in sequence. When the power consumption coefficient between the first day and any day is greater than or equal to the preset coefficient threshold (in the example of the present invention, the coefficient threshold is set to 0.5. If the power consumption data fluctuates greatly, the coefficient threshold can be appropriately adjusted to distinguish different power consumption periods), that is, all the daily power consumption between the first day and this day is taken as one power consumption period.

[0046] In the second step, starting from the next day after the end day of the power consumption period determined in the first step, repeat the first step (i.e., calculate the power consumption coefficients between the new starting point and each subsequent day) to obtain a new power consumption period.

[0047] In the third step, after each new power consumption period is determined, update the starting point as the next day after the end day of the current power consumption period, and repeat step two until all the set number of days are classified into power consumption periods.

[0048] By continuously updating the starting point and calculating the power consumption coefficient, the continuous power consumption data is divided into multiple power consumption periods. The determination of each power consumption period is based on whether the power consumption coefficient reaches or exceeds the preset threshold. This method can help identify changes in power consumption patterns, thereby better managing and optimizing power consumption resources.

[0049] In another embodiment, when determining whether a power consumption period is completed, by setting a flexible number of days, abnormal power consumption situations within a short period can be ignored. For example, if the power consumption on a certain day is abnormal due to special events (such as holidays, extreme weather, etc.), but this abnormality is not continuous, then it will not affect the division of power consumption periods.

[0050] Exemplarily, set the flexible number of days to 3 days. For example, when the power consumption coefficients between the first day and the second day, the first day and the third day, and the first day and the fourth day are all greater than the preset coefficient threshold, then all the daily power consumption between the first day and the fourth day is taken as one power consumption period.

[0051] The introduction of flexible days is to make the division of electricity usage periods more in line with the actual electricity consumption situation, reduce the impact caused by short-term fluctuations, and at the same time maintain the flexibility and practicality of the division method. This method helps to more accurately identify the long-term changes in electricity consumption patterns and provides more practical data support for power management and energy planning.

[0052] Among them, the calculation formula for the electricity consumption coefficient of the first day and the second day is exemplarily given:

[0053] ;

[0054] In the formula, is the electricity consumption coefficient of the first day and the second day, is the total number of electricity consumption sequences in the periods included in the first day, is the total number of electricity consumption sequences in the periods included in the second day, is the start time of the th electricity consumption sequence in the first day, is the end time of the th electricity consumption sequence in the first day, is the start time of the th electricity consumption sequence in the second day, is the end time of the th electricity consumption sequence in the second day, is the average electricity consumption of the th electricity consumption sequence in the first day, is the average electricity consumption of the th electricity consumption sequence in the second day, is the maximum value.

[0055] The polynomial of the above electricity consumption coefficient can be decomposed into three main parts:

[0056] The first part, proportional difference: ;

[0057] The second part, sum of absolute differences: ; Among them, reflects the similarity degree of the lengths of the electricity consumption sequences in two periods, reflects the similarity degree of the actual electricity consumption within the electricity consumption sequences in two periods.

[0058] The third part, maximum time difference: ;

[0059] It should be noted that the part of the maximum time difference does not directly participate in the calculation of the electricity consumption coefficient, but it is an important condition for screening and determining which pair of time period data is used for calculation. The role of this maximum time difference is to ensure that the starting points of the corresponding accumulation units are close, so as to avoid the problem of deviation in the starting time of the electricity consumption sequences in similar time periods within two days. Specifically, by restricting the maximum value, it can be ensured that the time periods selected on the first day and the second day match as much as possible in time, thereby reducing the analysis error caused by time differences.

[0060] S3: Construct an electricity consumption cycle based on the electricity consumption time periods.

[0061] The power consumption of user points is not constant, but fluctuates with different factors (such as seasons, production demands, etc.). The change of this electricity consumption cycle is very important for power supply and grid management. Plan power production and distribution according to the periodic changes to ensure that the demand can be met during the peak electricity consumption period, and avoid resource waste during the low electricity consumption period.

[0062] In order to analyze the electricity consumption periodicity, it is first necessary to determine the cycle judgment method.

[0063] Specifically, select any electricity consumption time period as the target time period, use the electricity consumption time period on the preset number of days after the target time period as the to-be-determined time period, calculate the matching coefficients of the target time period with the to-be-determined time period and the time periods before and after the to-be-determined time period, select the electricity consumption time period with the highest matching coefficient as the same-cycle electricity consumption time period, record the number of days of the interval as the cycle length, use the electricity consumption time period with the highest matching coefficient as the new target time period, and iteratively calculate the matching coefficient until an electricity consumption time period that coincides with any electricity consumption time period within the same cycle is found, that is, construct a complete electricity consumption cycle.

[0064] Exemplarily, set the above-mentioned number of days of the interval to 5 days, use the electricity consumption time period corresponding to 5 days after the target time period as the to-be-determined time period, and calculate the matching coefficients of the target time period with the to-be-determined time period and the time periods before and after the to-be-determined time period;

[0065] Among them, an exemplary calculation process of the matching coefficient between the target time period and the to-be-determined time period is given:

[0066] Calculate the number of overlapping days between the target time period and the to-be-determined time period in time, and calculate the absolute value of the difference between the average electricity consumption of the target time period and the average electricity consumption of the to-be-determined time period;

[0067] Take the product of the number of overlapping days between the target time period and the to-be-determined time period in time and the absolute value of the difference between the average electricity consumption of the target time period and the average electricity consumption of the to-be-determined time period as the matching coefficient.

[0068] That is, the matching coefficient between the target time period and the to-be-determined time period satisfies the relational expression as:

[0069] ;

[0070] Wherein, is the matching coefficient between the target time period and the to-be-determined time period, is the number of time period power consumption sequences within the target time period, is the number of time period power consumption sequences within the to-be-determined time period, is the number of overlapping days in time between the target time period and the to-be-determined time period, is the absolute value of the difference between the average power consumption of the target time period and the average power consumption of the to-be-determined time period.

[0071] According to the calculation formula of the matching coefficient between the target time period and the to-be-determined time period, the matching coefficients of the time periods before and after the target time period and the to-be-determined time period can be calculated in the same way.

[0072] Further, mark the target time period as and mark the power consumption time period corresponding to the largest matching coefficient as , that is is considered to be the next power consumption time period in the same cycle as .

[0073] Further, set as the new current power consumption time period, repeat the calculation of the corresponding matching coefficient and find the next power consumption time period in the same cycle , and continue the iterative calculation until a power consumption time period coincides with a previous power consumption time period (i.e., returns to the starting point) or reaches the end of the data, thereby constructing a complete power consumption cycle.

[0074] Wherein, the set number of interval days (such as the set 5 days above) is used as the length of the power consumption cycle.

[0075] By repeating the above steps, all power consumption time periods are traversed, thereby identifying multiple power consumption cycles, and each power consumption cycle is composed of a series of power consumption time periods with the largest matching coefficients.

[0076] S4: Calculate the cycle index of the power consumption cycle. When the corresponding cycle index is less than the preset cycle threshold, expand the cycle length of the power consumption cycle according to the preset ratio and recalculate the cycle index until the cycle index is greater than the preset cycle threshold, and then stop expanding the cycle length. The power consumption cycle with the expanded cycle length is used as the effective cycle.

[0077] Based on all the power consumption cycles obtained in the above S3, a basic periodic pattern is provided. However, in some cases, there may be some outliers or noises within the cycle, and thus the true law of power consumption cannot be accurately reflected.

[0078] Specifically, calculate the cycle index of the power consumption cycle, that is, the relational expression is satisfied as:

[0079] ;

[0080] In the formula, is the cycle index, is the number of days in the th electricity consumption period within the cycle, is the average power consumption in the th electricity consumption period within the cycle, is the natural exponential function.

[0081] Among them, as the and standard deviations increase, the value decreases.

[0082] Furthermore, set the cycle threshold to 0.55. If the cycle index is less than 0.55 when the initially set equal interval days is 5 days, the equal interval days can be increased to 6 days, and then the cycle index is recalculated. If the cycle index still does not meet 0.55, continue to increase the equal interval days, such as increasing to 7 days, and calculate the cycle index again. This process continues until the cycle index exceeds 0.55, and the equal interval days at this time are the required effective cycle.

[0083] S5: Calculate the average power consumption of all electricity consumption periods within the effective cycle as the first average value; calculate the average power consumption corresponding to each electricity consumption period within the effective cycle, and take the maximum value of the average power consumption corresponding to all electricity consumption periods as the second average value; take the difference between the second average value and the first average value as the power consumption surplus.

[0084] Specifically, calculate the average power consumption of all electricity consumption periods within the effective cycle as the first average value; calculate the average power consumption corresponding to each electricity consumption period within the effective cycle, and take the maximum value of the average power consumption corresponding to all electricity consumption periods as the second average value; take the difference between the second average value and the first average value as the power consumption surplus.

[0085] By calculating the additional power that needs to be accumulated in the energy storage device (such as a storage battery) of the electricity consumption unit within the effective cycle, it can be clearly known how much additional power the electricity consumption unit needs to accumulate within a certain effective cycle to meet its power consumption requirements. This helps to prevent power outages or insufficient power supply caused by insufficient power.

[0086] S6: Calculate the ratio of the current remaining power of the electricity consumption unit to the power consumption surplus. If this ratio is less than the preset surplus threshold, issue a warning that the power supply of the electricity consumption unit is insufficient.

[0087] Specifically, when calculating the remaining power consumption and evaluating whether the remaining power is sufficient to support future power consumption needs, we need to consider the number of days that have elapsed within this effective period from a certain starting point (such as a specific date, time point, or a specific time period) to the current moment. Because as the effective period progresses, the remaining power of the power consumption unit will gradually decrease, and the future power required will be predicted based on the power consumption pattern and the average power consumption. If the remaining power is not sufficient to support the power consumption needs for a certain period in the future, then measures need to be taken in advance to accumulate power or adjust the power consumption plan.

[0088] Furthermore, the ratio of the difference between the average number of power consumption days within the effective period and the number of days since entering this effective period to the average number of power consumption days within the effective period is used as the margin threshold;

[0089] Obtain the number of days elapsed from the start time of the effective period to the current moment, and use the ratio of the difference between the average number of power consumption days within the effective period and the number of days elapsed from the start time of the effective period to the current moment to the average number of power consumption days within the effective period as the margin threshold.

[0090] Furthermore, calculate the ratio of the current remaining power of the power consumption unit (i.e., the power remaining in the user's single energy storage device at the current moment) to the power consumption margin. When the ratio of the current remaining power of the power consumption unit to the power consumption margin is less than the margin threshold, it indicates that the remaining power of the power consumption unit may not be sufficient to support its power consumption needs for a certain period in the future. At this time, the system or the power distribution unit can issue an early warning in advance to remind the power consumption unit to pay attention to the management of power consumption, or take timely measures (such as increasing the capacity of the storage battery, adjusting the power consumption plan, etc.) to avoid the situation of insufficient power supply.

[0091] Through the precise management and early warning of power consumption, the power distribution unit can allocate power resources more reasonably, ensuring the stability and reliability of power supply. At the same time, this also helps to reduce power waste and improve the efficiency of power use.

[0092] The embodiment of the present invention also discloses a power supply and distribution intelligent scheduling early warning detection system, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the power supply and distribution intelligent scheduling early warning detection method according to the present invention is implemented.

[0093] The system also includes a communication bus and a communication interface and other components well-known to those skilled in the art. Their settings and functions are known in the art, so they will not be described in detail here.

[0094] In the present invention, the aforementioned memory may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store the required information and can be accessed by an application program, module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0095] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three, or more, etc., unless otherwise specifically and clearly defined.

[0096] Although this specification has shown and described multiple embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and concept of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in the practice of the present invention.

Claims

1. A method for early warning detection of intelligent dispatching of electric power supply and distribution, characterized in that: include: The daily electricity consumption of the electricity consumption unit for a set number of days is collected, and the daily electricity consumption is clustered to obtain multiple clusters, each of which represents a time period electricity consumption sequence with similar electricity consumption behavior within a day; Based on the power consumption sequence of each day, determine all power consumption periods; Select any electricity consumption period as the target period, and take the electricity consumption period with a preset interval of days after the target period as the pending period, calculate the matching coefficient between the target period and the pending period and the periods before and after the pending period, select the electricity consumption period with the highest matching coefficient as the electricity consumption period in the same cycle, record the interval of days as the cycle length, take the electricity consumption period with the highest matching coefficient as the new target period, iteratively calculate the matching coefficient until a electricity consumption period that coincides with any electricity consumption period in the same cycle is found, that is, a complete electricity consumption cycle is constructed; Calculating the cycle index of the power usage cycle, when the corresponding cycle index is less than a preset cycle threshold, expanding the cycle length of the power usage cycle according to a preset ratio and recalculating the cycle index, until the cycle index is greater than the preset cycle threshold, then stopping expanding the cycle length, and taking the power usage cycle with the expanded cycle length as the effective cycle; Calculate the average power consumption of all power consumption periods within the effective period as the first average; calculate the average power consumption corresponding to each power consumption period within the effective period, and take the maximum value of the average power consumption corresponding to all power consumption periods as the second average; take the difference between the second average and the first average as the power margin; The ratio of the current remaining power of the power consuming unit to the power remaining capacity is calculated, and if the ratio is less than a preset remaining capacity threshold, an early warning of insufficient power supply of the power consuming unit is issued.

2. According to claim 1, a method for detecting intelligent dispatching of electric power supply and distribution, characterized in that: The clustering adopts ordered sample clustering.

3. The method for detecting intelligent dispatching of electric power supply and distribution according to claim 2 is characterized in that: The step of determining all electricity usage time periods comprises: The first step is to calculate the power consumption coefficients of the first day and the second day, the first day and the third day, and finally the first day and the last day of the set days, starting from the first day of the set days. When the power consumption coefficient of the first day and any day is greater than or equal to the preset coefficient threshold, all daily power consumption between the first day and the corresponding day is regarded as a power consumption period; Step 2: Starting from the next day after the end day of the electricity usage period determined in Step 1, repeat Step 1 to obtain a new electricity usage period; Step 3: Each time a new power usage period is determined, the starting point is updated as the next day of the end day of the current power usage period, and step 2 is repeated until all the set days are included in the power usage period.

4. The method for detecting intelligent dispatching of electric power supply and distribution according to claim 3 is characterized in that: The electricity consumption coefficients on the first and second days satisfy the following relationship: ; In the formula, is the electricity consumption coefficient of the first and second days, is the total number of electricity consumption sequences in the period included in the first day, is the total number of electricity consumption sequences in the period of the second day, For the first day The starting time of the electricity consumption sequence in each period, For the first day The end time of the electricity consumption sequence in each period, For the second day The starting time of the electricity consumption sequence in each period, For the second day The end time of the electricity consumption sequence in each period, For the first day The average power consumption of the power consumption sequence in each period, For the second day The average power consumption of the power consumption sequence in each period, where The maximum value of is used to ensure that the selected time periods of the first and second days match in time, thereby reducing the analysis error caused by time differences.

5. The method for detecting intelligent dispatching of electric power supply and distribution according to claim 4 is characterized in that: The process of obtaining the matching coefficient includes: Calculate the number of days that the target period overlaps with the pending period, and calculate the absolute value of the difference between the average power consumption of the target period and the average power consumption of the pending period; The product of the number of days of overlap between the target period and the pending period and the absolute value of the difference between the average power consumption of the target period and the average power consumption of the pending period is taken as the matching coefficient.

6. A method for detecting intelligent dispatching of electric power supply and distribution according to claim 5, characterized in that: The periodic index satisfies the relationship: ; In the formula, is the period index, For the cycle The number of days with electricity consumption period, For the cycle The average power consumption during each power consumption period, is a natural exponential function.

7. A method for detecting intelligent dispatching of electric power supply and distribution according to claim 6, characterized in that: The process of obtaining the remaining threshold value includes: Calculate the average number of days of electricity consumption within the effective period; The number of days entering the valid cycle at the current moment is obtained, and the ratio of the difference between the average number of power consumption days in the valid cycle and the number of days entering the valid cycle at the current moment to the average number of power consumption days in the valid cycle is used as the surplus threshold.

8. An intelligent dispatching early warning detection system for power supply and distribution, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the electric power supply and distribution intelligent dispatching early warning detection method according to any one of claims 1-7 is implemented.

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