Enterprise productivity analysis and evaluation method based on electrical load data analysis

Abstract

The invention relates to the technical field of enterprise productivity analysis, and particularly discloses an enterprise productivity analysis and evaluation method based on electrical load data analysis, and the method comprises the steps: S1, collecting the daily electrical load data of all equipment of an enterprise in the current month through an infrared sensor, and storing the data; the daily electrical load data of all equipment of an enterprise in the current month are respectively collected, and the electrical load data are preprocessed, so that the accuracy of the data can be improved, and the number of working days in the current month, rated production time and other data can be obtained; then, by calculating the mean value of the correlation coefficients of the power utilization load curves between all the workdays in the current month, the power utilization regularity of the enterprise can be judged, when it is judged that the power utilization regularity of the enterprise is high, the productivity of the enterprise in the current month is calculated and analyzed, in this way, the data obtaining mode is simple, and the efficiency is high. And the data is more accurate, so that the accuracy of an enterprise productivity analysis and evaluation result can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise capacity analysis, and in particular to an enterprise capacity analysis and evaluation method based on power load data analysis. Background Art

[0002] Enterprise capacity analysis is the process of in-depth understanding and evaluation of an enterprise's production capacity. It generally refers to the analysis of a company or group's total production capacity over a certain period of time. It is of great significance to the enterprise's strategic planning, production operations, and resource allocation.

[0003] Traditional capacity analysis methods usually include the working hour method and the product method. The working hour method evaluates production capacity by calculating the amount of work that can be completed per unit time. This method is suitable for companies with relatively stable production processes and fewer product types. The product method evaluates production capacity based on the quantity and quality of products. This method is suitable for companies with a large number of product types and more complex production processes.

[0004] Although the common capacity analysis methods in the existing technology can realize the evaluation of enterprise capacity, in actual application, obtaining accurate and comprehensive data is the key to evaluating enterprise capacity. However, due to information asymmetry, data confidentiality and other reasons, it is often difficult to obtain complete and accurate data, which affects the accuracy of the evaluation results. Summary of the invention

[0005] The purpose of the present invention is to provide a method for analyzing and evaluating enterprise capacity based on power load data analysis to solve the following technical problems:

[0006] How to improve the accuracy of enterprise capacity analysis and evaluation results.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for analyzing and evaluating enterprise capacity based on power load data analysis, the method comprising:

[0009] S1: Collect the daily power load data of all the equipment of the enterprise in the current month through infrared sensors and store the data;

[0010] S2: Pre-process and clean the daily power load data of all equipment in the enterprise in that month to ensure the accuracy and reliability of the power load data;

[0011] S3: Compare the daily power load data of all equipment of the enterprise in the month after preprocessing with the threshold value for ensuring production power consumption, and determine the number of working days in the month based on the comparison results;

[0012] S4: Calculate the mean value of the correlation coefficient of the power load curve between all two working days of the month through the daily power load data of the enterprise equipment in the data processing module;

[0013] S5: By comparing the average of the correlation coefficients of the power load curves between all two working days of the month with the preset average of the correlation coefficients, the strength of the power consumption regularity of the enterprise is determined based on the comparison;

[0014] S6: When it is judged that the enterprise has a strong regularity in electricity consumption, the rated production time is obtained by combining the number of working days in the month, and the enterprise's production capacity for the month is calculated.

[0015] Furthermore, the pretreatment cleaning process in S2 includes:

[0016] By formula Calculate the power load index of all equipment on the zth day of the month after the preprocessing is completed ;

[0017] Where z is any day of the month, s is any time period, m is the total number of time periods in a day, a is any electrical equipment in the enterprise, and b is the total number of electrical equipment in the enterprise. is the power load of the sth electrical equipment in the tth time period on the zth day of the month, is the weight coefficient of the a-th electrical equipment, which is set based on empirical fitting. for The standard value of is the indoor temperature in the sth time period on the zth day of the month, is the preset indoor temperature. is the indoor humidity in the sth time period on the zth day of the month, is the preset indoor humidity. for The standard value of is the influence coefficient of sensor usage time, To adjust the coefficient comparison table function, according to The impact of the range of values ​​on the power load is obtained based on testing.

[0018] Furthermore, the comparison process in S3 includes:

[0019] After the pre-processing, the daily power load of all equipment in the enterprise is calculated. Respectively with the preset production power consumption threshold Make a comparison;

[0020] like Greater than , judge that the power load of all equipment of the enterprise on that day is high, that is, the day is regarded as a working day and marked;

[0021] like Less than , judging that the power load of all the equipment in the enterprise on that day is low, that is, the day is regarded as a non-working day and no mark is made.

[0022] Furthermore, the method for determining the number of working days in the month in S3 includes:

[0023] After the pre-processing is completed, the daily power load of all equipment in the enterprise in that month Greater than the preset threshold for guaranteed production power consumption The natural days of the month are regarded as working days, and the number of working days in the month can be counted, and the total number of working days in the month is defined as .

[0024] Furthermore, the calculation method in S4 includes:

[0025] Through continuous monitoring of real-time data of daily power load, a curve of power load changes on working days is established. ;

[0026] Collect data evenly at preset fixed time intervals data;

[0027] By formula Calculate the first Working days and Correlation coefficient of the power load change curve for each working day ;

[0028] And through the formula Calculate the mean of the correlation coefficients between all two working days ;

[0029] in, and are any different working days, and , i is any data collection, For all The average value of For all The average value of , u is any set of Working days and The correlation coefficient of the power load change curve of the working day is Working days and The total number of combinations of correlation coefficients of the power load change curve for each working day.

[0030] Furthermore, the judgment and comparison process in S5 includes:

[0031] By taking the mean of the correlation coefficients between all two weekdays The mean threshold of the correlation coefficient with the preset contrast;

[0032] like , it is judged that the mean value of the correlation coefficient between all working days of the enterprise is high, indicating that the enterprise has a strong regularity in electricity consumption and can conduct capacity analysis;

[0033] Otherwise, it is judged that the mean of the correlation coefficient between all the working days of the enterprise is low, which means that the regularity of the enterprise's electricity consumption is weak and the capacity analysis cannot be carried out.

[0034] Furthermore, the process of obtaining the rated production time in S6 includes:

[0035] After the pretreatment is completed, the daily power load of all equipment in the enterprise for that month Greater than the preset threshold for guaranteed production power consumption When the working time is 2 hours, it is regarded as the enterprise's production. The daily working hours can be counted, and the highest value of the working hours frequency in the month is regarded as the enterprise's rated production time.

[0036] Furthermore, the calculation process of the monthly production capacity in S6 includes:

[0037] By formula Calculate the capacity coefficient for the month ;

[0038] Where w is any working day of the month. is the total production time on the wth working day of the month, The rated production time for each working day.

[0039] Beneficial effects of the present invention:

[0040] (1) The present invention can improve the accuracy of data by separately collecting the daily power load data of all equipment of the enterprise in the current month and preprocessing the power load data, and can obtain data such as the number of working days and rated production time in the current month. Then, by calculating the mean of the correlation coefficient of the power load curve between all working days in the current month, it is possible to judge the strength of the regularity of the enterprise's power consumption. When it is judged that the regularity of the enterprise's power consumption is strong, the enterprise's production capacity for the current month is calculated and analyzed. This method not only has a simple method for obtaining data, but also more accurate data, thereby improving the accuracy of the enterprise's production capacity analysis and evaluation results.

[0041] (2) The present invention collects and sums the power load of all equipment through multiple groups of infrared sensors, which can improve the accuracy of power load data and avoid the situation where the total load of enterprise electricity consumption is directly collected and is affected by the power load of non-production equipment, resulting in inaccurate data. In addition, by combining environmental data, the collected power load data of all equipment can be refined to further improve the accuracy of the data, thereby improving the accuracy of subsequent enterprise capacity analysis results.

[0042] (3) The present invention calculates the daily power load of all equipment in the enterprise after pretreatment. Respectively with the preset production power consumption threshold Through this comparison method, the power load of all the equipment in the enterprise on that day can be judged. The level of power load represents whether the enterprise has production on that day, so as to further judge whether the enterprise is working on that day, and then realize the identification of all working days in the month, so as to facilitate the subsequent evaluation of the enterprise's production capacity in combination with the data, thereby improving the accuracy of the evaluation results.

[0043] (4) The present invention calculates the mean of the correlation coefficients between all two working days. The mean threshold of the correlation coefficient with the preset By comparing, through this comparison method, we can judge the regularity of the company's electricity consumption according to the mean value of the correlation coefficient between all working days of the company. Strong regularity of electricity consumption means that the company's daily production time and equipment usage are similar, so the collected electricity load data is more accurate and complete, and capacity analysis can be performed on it.

[0044] (5) The present invention obtains data such as the rated production time of each working day, the number of working days in each month, and the total production time of the wth working day based on the enterprise's electricity load, so as to provide accurate data for calculating the production capacity of the current month. Then, by analyzing the size of the production capacity of each month throughout the year, the enterprise's annual production capacity distribution can be analyzed and evaluated. In addition, since the accuracy and reliability of the enterprise's electricity load data are high, the accuracy of the enterprise's annual production capacity distribution analysis and evaluation results can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below in conjunction with the accompanying drawings.

[0046] Figure 1 It is a flow chart of a method for analyzing and evaluating enterprise capacity based on power load data analysis in the present invention; DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1 As shown, in one embodiment, the present application provides a method for analyzing and evaluating enterprise capacity based on power load data analysis, the method comprising:

[0049] S1: Collect the daily power load data of all the equipment of the enterprise in the current month through infrared sensors and store the data;

[0050] S2: Pre-process and clean the daily power load data of all equipment in the enterprise in that month to ensure the accuracy and reliability of the power load data;

[0051] S3: Compare the daily power load data of all equipment of the enterprise in the month after preprocessing with the threshold value for ensuring production power consumption, and determine the number of working days in the month based on the comparison results;

[0052] S4: Calculate the mean value of the correlation coefficient of the power load curve between all two working days of the month through the daily power load data of the enterprise equipment in the data processing module;

[0053] S5: By comparing the average of the correlation coefficients of the power load curves between all two working days of the month with the preset average of the correlation coefficients, the strength of the power consumption regularity of the enterprise is determined based on the comparison;

[0054] S6: When it is judged that the electricity consumption of the enterprise is highly regular, the rated production time is obtained by combining the number of working days in the month, and the production capacity of the enterprise in the month is calculated;

[0055] Through the above technical scheme, this embodiment provides an enterprise capacity analysis and evaluation method based on power load data analysis, firstly, the power load data of all equipment of the enterprise every day in the month is collected through infrared sensors, and the data is stored, and then the power load data of all equipment of the enterprise every day in the month is preprocessed and cleaned to ensure the accuracy and reliability of the power load data, and then the power load data of all equipment of the enterprise every day in the month after the preprocessing is compared with the threshold value for ensuring production power consumption, and the number of working days in the month is determined according to the comparison result, and then the power load data of all equipment of the enterprise every day in the month is processed by the data processing module, and the mean of the correlation coefficient of the power load curve between all working days in the month is calculated, and the mean of the correlation coefficient of the power load curve between all working days in the month is compared with the preset correlation coefficient mean, and the strength of the power consumption regularity of the enterprise is judged according to the comparison, and finally, when it is judged that the power consumption regularity of the enterprise is strong, the rated production time is obtained by combining the number of working days in the month, and the capacity of the enterprise in the month is calculated;

[0056] Through such settings, by separately collecting the daily power load data of all the equipment of the enterprise in that month and preprocessing the power load data, the accuracy of the data can be improved, and data such as the number of working days and rated production time in that month can be obtained. Then, by calculating the mean of the correlation coefficient of the power load curve between all two working days in that month, the strength of the enterprise's power consumption regularity can be judged. When it is judged that the enterprise's power consumption regularity is strong, the enterprise's production capacity for that month is calculated and analyzed. This method not only has a simple way of obtaining data, but also more accurate data, thereby improving the accuracy of the enterprise's production capacity analysis and evaluation results.

[0057] The pretreatment cleaning process in S2 includes:

[0058] By formula Calculate the power load index of all equipment on the zth day of the month after the preprocessing is completed ;

[0059] Where z is any day of the month, s is any time period, m is the total number of time periods in a day, a is any electrical equipment in the enterprise, and b is the total number of electrical equipment in the enterprise. is the power load of the sth electrical equipment in the tth time period on the zth day of the month, is the weight coefficient of the a-th electrical equipment, which is set based on empirical fitting. for The standard value can be selected and set according to the allowable error in the empirical data. is the indoor temperature in the sth time period on the zth day of the month, is the preset indoor temperature. is the indoor humidity in the sth time period on the zth day of the month, is the preset indoor humidity. for The standard value can be selected and set according to the allowable error in the empirical data. is the influence coefficient of sensor usage time, To adjust the coefficient comparison table function, according to The influence of the range of values ​​on the power load is obtained based on the test;

[0060] Through the above technical solution, this embodiment provides the power load index of all equipment on the zth day of the enterprise in the month after the preprocessing is completed. , can be obtained by formula It is obvious that based on the power load collected by the infrared sensor, the higher the indoor temperature in the sth time period on the zth day of the month, and the greater the difference between the humidity and the preset humidity, the higher the power load index of all equipment on the zth day of the enterprise after the preprocessing is completed. The smaller it is, the more likely it is that the infrared sensor is affected by the external environment on that day, causing the collected data to be too large. On the contrary, the lower the indoor temperature in the sth time period on the zth day of the month, and the smaller the difference between the humidity and the preset humidity, the higher the power load index of all equipment on the zth day of the enterprise after the preprocessing is completed. The larger it is, the more it means that the infrared sensor is affected by the external environment on that day, resulting in the collected data not being too large;

[0061] Through this setting, the power load of all equipment is collected and summed up through multiple groups of infrared sensors, which can improve the accuracy of the power load data and avoid the influence of the power load of non-production equipment when directly collecting the total load of the enterprise's electricity consumption, resulting in inaccurate data. In addition, by combining environmental data, the power load data of all collected equipment can be refined to further improve the accuracy of the data, thereby improving the accuracy of subsequent enterprise production capacity analysis results.

[0062] The comparison process in S3 includes:

[0063] After the pre-processing, the daily power load of all equipment in the enterprise is calculated. Respectively with the preset production power consumption threshold Make a comparison;

[0064] like Greater than , judge that the power load of all equipment of the enterprise on that day is high, that is, the day is regarded as a working day and marked;

[0065] like Less than , judging that the power load of all the equipment of the enterprise on that day is low, that is, the day is regarded as a non-working day and no mark is made;

[0066] Through the above technical solution, this embodiment calculates the daily power load of all equipment in the enterprise after preprocessing. Respectively with the preset production power consumption threshold Through this comparison method, the power load of all the equipment in the enterprise on that day can be judged. The level of power load represents whether the enterprise has production on that day, so as to further judge whether the enterprise is working on that day, and then realize the identification of all working days in the month, so as to facilitate the subsequent evaluation of the enterprise's production capacity in combination with the data, thereby improving the accuracy of the evaluation results.

[0067] The method for determining the number of working days in the month in S3 includes:

[0068] After the pre-processing is completed, the power load of all equipment in the enterprise for the month will be Greater than the preset threshold for guaranteed production power consumption The natural days of the month are regarded as working days, and the number of working days in the month can be counted, and the total number of working days in the month is defined as ;

[0069] Through the above technical solution, this embodiment provides a method for determining the number of working days in the month. By comparing the daily power load of all equipment of the enterprise in the month after the preprocessing is completed with the preset production power threshold, the level of the power load can be judged. The level of the power load represents whether the enterprise has production, so the power load of all equipment of the enterprise in the month after the preprocessing is completed can be compared with the preset production power threshold. Greater than the preset threshold for guaranteed production power consumption The natural day of the month is regarded as a working day, so as to obtain the number of working days of the enterprise in that month, which is convenient for the subsequent calculation of the enterprise's production capacity.

[0070] The calculation method in S4 includes:

[0071] Through continuous monitoring of real-time data of daily power load, a curve of power load changes on working days is established. ;

[0072] Collect data evenly at preset fixed time intervals data;

[0073] By formula Calculate the first Working days and Correlation coefficient of the power load change curve for each working day ;

[0074] And through the formula Calculate the mean of the correlation coefficients between all two working days ;

[0075] in, and are any different working days, and , i is any data collection, For all The average value of For all The average value of , u is any set of Working days and The correlation coefficient of the power load change curve of the working day is Working days and The total number of combinations of correlation coefficients of the power load change curves for each working day;

[0076] Through the above technical solution, this embodiment provides the Working days and Correlation coefficient of the power load change curve for each working day , can be obtained by formula By this calculation method, the correlation coefficient of the power load change curve between two working days in the month can be calculated. This data can reflect the regularity of the power load of the enterprise on any two working days in the month. Then, the formula Calculate the mean of the correlation coefficients between all two working days , which can reflect the similarity of the enterprise's electricity load between any two working days of the month, thereby providing accurate data for subsequent judgments on the strength of the enterprise's working day electricity consumption pattern, and further making accurate judgments on whether the enterprise can conduct capacity analysis in the future.

[0077] The judgment and comparison process in S5 includes:

[0078] By taking the mean of the correlation coefficients between all two weekdays The mean threshold of the correlation coefficient with the preset contrast;

[0079] like , it is judged that the mean value of the correlation coefficient between all working days of the enterprise is high, indicating that the enterprise has a strong regularity in electricity consumption and can conduct capacity analysis;

[0080] Otherwise, it is judged that the mean value of the correlation coefficient between all working days of the enterprise is low, indicating that the regularity of the enterprise's electricity consumption is weak and capacity analysis cannot be performed;

[0081] Through the above technical solution, this embodiment calculates the mean of the correlation coefficients between all two working days. The mean threshold of the correlation coefficient with the preset By comparing, through this comparison method, we can judge the regularity of the company's electricity consumption according to the mean value of the correlation coefficient between all working days of the company. Strong regularity of electricity consumption means that the company's daily production time and equipment usage are similar, so the collected electricity load data is more accurate and complete, and capacity analysis can be performed on it.

[0082] The process of obtaining the rated production time in S6 includes:

[0083] After the pretreatment is completed, the daily power load of all equipment in the enterprise for that month Greater than the preset threshold for guaranteed production power consumption When the working hours are equal, it is regarded as the enterprise's production, and the daily working hours can be counted. The highest value of the working hours frequency in the month is regarded as the enterprise's rated production time;

[0084] Through the above technical solution, this embodiment provides a process for obtaining the rated production time. After the preprocessing is completed, the daily power load of all equipment in the enterprise in the month is Greater than the preset threshold for guaranteed production power consumption When the working hours are the same as the working hours of the enterprise, it is regarded as the enterprise production. Since the enterprise may leave work early or work overtime at night during production, the highest frequency of working hours in that month is selected as the rated production time of the enterprise to ensure the reliability of the data.

[0085] The calculation process of the monthly production capacity in S6 includes:

[0086] By formula Calculate the capacity coefficient for the month ;

[0087] Where w is any working day of the month. is the total production time on the wth working day of the month, is the rated production time for each working day;

[0088] Through the above technical solution, this embodiment provides the capacity coefficient of the current month The calculation method can be obtained by the formula It is calculated that the capacity coefficient of the current month can intuitively reflect the capacity of the current month. Obviously, the longer the total production time of the w-th working day of the current month, the higher the capacity of the current month. Conversely, the shorter the total production time of the w-th working day of the current month, the lower the capacity of the current month. By obtaining the rated production time of each working day, the number of working days in each month, and the total production time of the w-th working day based on the company's electricity load, accurate data can be provided for calculating the capacity of the current month. After that, by analyzing the size of the capacity of each month throughout the year, the company's annual capacity distribution can be analyzed and evaluated. Moreover, since the accuracy and reliability of the company's electricity load data are high, the accuracy of the company's annual capacity distribution analysis and evaluation results can be guaranteed.

[0089] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for analyzing and evaluating enterprise capacity based on power load data analysis, characterized in that: The method comprises: S1: Collect the daily power load data of all the equipment of the enterprise in the current month through infrared sensors and store the data; S2: Pre-process and clean the daily power load data of all equipment in the enterprise in that month to ensure the accuracy and reliability of the power load data; S3: Compare the daily power load data of all equipment of the enterprise in the month after preprocessing with the threshold value for ensuring production power consumption, and determine the number of working days in the month based on the comparison results; S4: Calculate the mean value of the correlation coefficient of the power load curve between all two working days of the month through the daily power load data of the enterprise equipment in the data processing module; S5: By comparing the average of the correlation coefficients of the power load curves between all two working days of the month with the preset average of the correlation coefficients, the strength of the power consumption regularity of the enterprise is determined based on the comparison; S6: When it is judged that the enterprise has a strong regularity in electricity consumption, the rated production time is obtained by combining the number of working days in the month, and the enterprise's production capacity for the month is calculated.

2. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 1 is characterized in that: The pretreatment cleaning process in S2 includes: By formula After preprocessing, the power load index of all equipment in the enterprise on the zth day of the month ; Where z is any day of the month, s is any time period, m is the total number of time periods in a day, a is any electrical equipment in the enterprise, and b is the total number of electrical equipment in the enterprise. is the power load of the sth electrical equipment in the tth time period on the zth day of the month, is the weight coefficient of the a-th electrical equipment, which is set based on empirical fitting. for The standard value of is the indoor temperature in the sth time period on the zth day of the month, is the preset indoor temperature. is the indoor humidity in the sth time period on the zth day of the month, is the preset indoor humidity. for The standard value of is the influence coefficient of sensor usage time, To adjust the coefficient comparison table function, according to The impact of the range of values ​​on the power load is obtained based on tests.

3. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 2 is characterized in that: The comparison process in S3 includes: After the pre-processing, the daily power load of all equipment in the enterprise is calculated. Respectively with the preset production power consumption threshold Make a comparison; like Greater than , judge that the power load of all equipment of the enterprise on that day is high, that is, the day is regarded as a working day and marked; like Less than , judging that the power load of all the equipment in the enterprise on that day is low, that is, the day is regarded as a non-working day and no mark is made.

4. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 3 is characterized in that: The method for determining the number of working days in the month in S3 includes: After the pre-processing is completed, the daily power load of all equipment in the enterprise in that month Greater than the preset threshold for guaranteed production power consumption The natural days of the month are regarded as working days, and the number of working days in the month can be counted, and the total number of working days in the month is defined as .

5. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 4 is characterized in that: The calculation method in S4 includes: Through continuous monitoring of real-time data of daily power load, a curve of power load changes on working days is established. ; Collect data evenly at preset fixed time intervals data; By formula Calculate the first Working days and Correlation coefficient of the power load change curve for each working day ; And through the formula Calculate the mean of the correlation coefficients between all two working days ; in, and are any different working days, and , i is any data collection, For all The average value of For all The average value of , u is any set of Working days and The correlation coefficient of the power load change curve of the working day is Working days and The total number of combinations of correlation coefficients of the power load change curve for each working day.

6. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 5 is characterized in that: The judgment and comparison process in S5 includes: By taking the mean of the correlation coefficients between all two weekdays The mean threshold of the correlation coefficient with the preset contrast; like , it is judged that the mean value of the correlation coefficient between all working days of the enterprise is high, indicating that the enterprise has a strong regularity in electricity consumption and can conduct capacity analysis; Otherwise, it is judged that the mean of the correlation coefficient between all the working days of the enterprise is low, which means that the regularity of the enterprise's electricity consumption is weak and the capacity analysis cannot be carried out.

7. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 6 is characterized in that: The process of obtaining the rated production time in S6 includes: After the pretreatment is completed, the daily power load of all equipment in the enterprise for that month Greater than the preset threshold for guaranteed production power consumption When the working time is 2 hours, it is regarded as the enterprise's production. The daily working hours can be counted, and the highest value of the working hours frequency in the month is regarded as the enterprise's rated production time.

8. The enterprise capacity analysis and evaluation method based on power load data analysis according to claim 7 is characterized in that: The calculation process of the monthly production capacity in S6 includes: By formula Calculate the capacity coefficient for the month ; Where w is any working day of the month. is the total production time on the wth working day of the month, The rated production time for each working day.

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