A system and method for analyzing productivity based on power data

Abstract

The present invention discloses a system and method for analyzing productivity based on power data, and relates to the technical field of power data analysis. The present invention collects data such as monthly power consumption, output value, energy consumption and number of employees of each sub-industry, and constructs a time series data set after data cleaning and sorting. The analysis model is used to mine the correlation between power and enterprise data, calculate the power efficiency of high-end equipment manufacturing and traditional equipment manufacturing, and comprehensively generate the power rate ratio. The power efficiency, energy consumption ratio and labor efficiency of each sub-industry are calculated, and the comprehensive index of each sub-industry is comprehensively generated. In the data evaluation stage, the efficiency and energy utilization of the sub-industry are deeply analyzed through three evaluation units, and the power rate ratio of high-end and traditional equipment manufacturing industries is evaluated. Finally, the future power demand and productivity development of the sub-industry are predicted through the prediction module, and the report is compiled and specific suggestions and improvement measures are proposed.

Description

Technical Field

[0001] The present invention relates to the technical field of power data analysis, and in particular to a system and method for analyzing productivity based on power data. Background Art

[0002] Power data analysis requires a wide range of data sources, making data acquisition complex. These data sources vary in data format, update frequency, and accuracy, increasing the complexity and difficulty of data collection. Data collected from multiple channels often contains duplication, errors, and invalidity. Different data sources may also have different values ​​for the same data, requiring processing. Productivity analysis typically involves long-term power data, making the collection and integration of this complex data challenging. Comprehensively assessing productivity requires calculating indicators across multiple dimensions, which involve complex mathematical formulas and algorithms, placing high demands on the analyst's professional capabilities and technical proficiency. Analyzing historical data to predict future trends is a massive undertaking. Productivity analysis not only involves power data but also draws on knowledge from multiple disciplines, such as information technology, communications technology, and artificial intelligence, requiring interdisciplinary expertise to tackle complex analytical tasks. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a system and method for analyzing productivity based on power data. By collecting data such as monthly electricity consumption, output value, energy consumption and number of employees of each sub-industry, formulas are established to clean and organize the data, and a time series data set is constructed to facilitate the management of data over a long period of time. The analysis model is then used to deeply mine and calculate the electricity efficiency of the high-end equipment manufacturing industry and the traditional equipment manufacturing industry, and a comprehensive electricity consumption rate ratio is generated. The electricity efficiency, energy consumption ratio and labor efficiency of each sub-industry are calculated, and a comprehensive index for each sub-industry is comprehensively generated. The efficiency and energy utilization of the sub-industries are comprehensively evaluated and analyzed through three evaluation units, and the electricity consumption rate ratio of the high-end and traditional equipment manufacturing industries is evaluated. The future electricity demand and productivity development trend of each sub-industry are predicted using a trained prediction module.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0005] A system for analyzing productivity based on power data, comprising:

[0006] Sub-industry data collection module: extract monthly electricity consumption data, monthly output value, monthly consumption of various energy sources and number of employees for five years according to sub-industry classification;

[0007] Data processing module: Cleans and organizes the collected data, removes duplicate, erroneous, and invalid data. If the values ​​of the same data x obtained from different websites are different, the maximum and minimum values ​​are removed and the average value is taken. The processed data is used to construct a time series sub-industry dataset;

[0008] Data analysis module: used to establish a time series analysis model, and calculate the power consumption per unit output value of the j-th sub-industry based on the time series sub-industry data set, that is, the power efficiency of the j-th sub-industry Hyydxl j ; Classify the sub-industries into high-end equipment manufacturing and traditional equipment manufacturing, calculate the electricity efficiency of high-end equipment manufacturing Gdxl and the electricity efficiency of traditional equipment manufacturing Ctxl; calculate the consumption ratio of the j-th sub-industry to the y-th energy Nybl jy , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy , labor efficiency of the jth sub-industry Rgxl j ; The electricity efficiency of the j-type sub-industry is Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Associated to generate the j-th sub-industry comprehensive index Zhzs j :

[0009] Evaluation module: If the comprehensive index of sub-industry category j is Zhzs j ≥ comprehensive threshold Q1, indicating that the j-th sub-industry belongs to the green, advanced and efficient industry; if the comprehensive index of the j-th sub-industry Zhzs j < comprehensive threshold Q1, indicating that the j-th sub-industry does not belong to the green, advanced and efficient industry, sends the first warning instruction, and further through the j-th sub-industry electricity efficiency Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy , labor efficiency of the jth sub-industry Rgxl j Continue to conduct in-depth assessments of issues in the jth sub-industry, compare and analyze them with corresponding thresholds, trigger corresponding early warning instructions, and generate corresponding strategies; evaluate the ratio of electricity efficiency of high-end equipment manufacturing to that of traditional equipment manufacturing: the electricity rate ratio, and analyze the differences in technology level and energy utilization efficiency between high-end equipment manufacturing and traditional equipment manufacturing;

[0010] Forecasting module: Use machine learning and deep learning algorithms to predict and evaluate the future electricity demand and productivity development of each sub-industry. Before conducting the forecast and evaluation, the collected and organized historical data and current trend data of each sub-industry are input into the forecasting module for training.

[0011] Preferably, the productivity includes: new generation information technology, biotechnology, new energy, new materials, high-end equipment, new energy vehicles, green environmental protection, civil aviation, shipbuilding and marine engineering equipment, artificial intelligence, quantum information, mobile communications, the Internet of Things and blockchain.

[0012] Preferably, if the values ​​of the same data x are different, they are standardized by the following formula:

[0013]

[0014] Where i = 1, 2, 3, ..., n, i represents the sequence number of the same data captured on the i-th website, n represents the total number of the same data captured on n different websites, x i Represents the same data value captured on the i-th website, x max represents the maximum value of the same data crawled on n different websites, x min Represents the minimum value of the same data crawled from n different websites.

[0015] Preferably, the data analysis module is used to establish a time series analysis model, including a first calculation unit, which is used to calculate and obtain the electricity efficiency Hyydxl of the j-th sub-industry based on the time series sub-industry data set. j , electricity consumption efficiency Gdxl of high-end equipment manufacturing industry and electricity consumption efficiency Ctxl of traditional equipment manufacturing industry;

[0016] The electricity efficiency of the j-th sub-industry is Hyydxl j Calculate using the following formula:

[0017]

[0018] Where j represents the sub-industry number, k = 1, 2, 3, ..., m, k represents the month number of the data, m represents the total number of months for which data for the past m months were obtained, Q(j,k) represents the monthly electricity consumption of the j-th sub-industry in the k-th month, and P(j,k) represents the monthly output value of the j-th sub-industry in the k-th month.

[0019] The electricity consumption efficiency Gdxl of the high-end equipment manufacturing industry is calculated using the following formula:

[0020]

[0021] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, g = 1, 2, 3, ..., s, g represents the serial number of the high-end equipment manufacturing industry, s represents the total number of high-end equipment manufacturing industries, Q(g, k) represents the monthly electricity consumption of the g-th high-end equipment manufacturing industry in the k-th month, Q(g, k) represents the monthly output value of the g-th high-end equipment manufacturing industry in the k-th month, represents the total monthly electricity consumption of all s high-end equipment manufacturing industries in m months. It represents the monthly output value of all s high-end equipment manufacturing industries in a total of m months;

[0022] The electricity consumption efficiency Ctxl of the traditional equipment manufacturing industry is calculated using the following formula:

[0023]

[0024] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, c = 1, 2, 3, ..., s, c represents the serial number of the traditional equipment manufacturing industry, q represents the total number of traditional equipment manufacturing industries, Q(c, k) represents the monthly electricity consumption of the cth traditional equipment manufacturing industry in the kth month, and Q(c, k) represents the monthly output value of the cth traditional equipment manufacturing industry in the kth month. represents the total monthly electricity consumption of all q traditional equipment manufacturing industries in m months. It represents the monthly output value of all q traditional equipment manufacturing industries in a total of m months.

[0025] Preferably, the data analysis module further includes a second calculation unit, which is used to calculate and obtain: the consumption ratio Nybl of the yth type of energy in the jth sub-industry based on the time series sub-industry data set jy And the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy Because the measurement units of various energy sources are different, the consumption of various energy sources is first converted into the consumption of standard coal according to the conversion coefficients of energy types issued by the energy management department, and then the calculation is carried out;

[0026] The consumption ratio of the yth type of energy in the jth sub-industry is Nybl jy Calculate using the following formula:

[0027]

[0028] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, j represents the serial number of the sub-industry, y = 1, 2, 3, ..., t, y represents the serial number of different types of energy, t represents the total number of energy types, N(j, k, y) represents the consumption of the yth type of energy in the kth month of the jth sub-industry, It represents the consumption of the yth type of energy in the jth sub-industry within m months. It represents the consumption of all t types of energy in the j-th sub-industry in m months;

[0029] The consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy Calculate using the following formula:

[0030]

[0031] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, j represents the serial number of the sub-industry, y = 1, 2, 3, ..., t, y represents the serial number of different types of energy, t represents the total number of energy types, f = 1, 2, 3, ..., u, f represents the serial number of different types of green energy, u represents the total number of green energy types, N(j, k, f) represents the f-th type of green energy consumption obtained in the k-th month of the j-th sub-industry, and N(j, k, y) represents the y-th type of energy consumption obtained in the k-th month of the j-th sub-industry. It represents the consumption of all u types of green energy in the j-th sub-industry in m months. It represents the consumption of all t types of energy in the j-th sub-industry within m months.

[0032] Preferably, the data analysis module further includes a third calculation unit, which is used to calculate and obtain the labor efficiency Rgxl of the j-th sub-industry based on the time series sub-industry data set. j , the labor efficiency of the j-th sub-industry Rgxl j Calculate using the following formula:

[0033]

[0034] In the formula, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, Q(j,k) represents the monthly output value of the jth sub-industry in the kth month, It represents the total output value of the j-th sub-industry for m months, and Rs(j,k) represents the number of employees in the j-th sub-industry in the k-th month.

[0035] Preferably, the data analysis module further includes a first correlation unit and a second correlation unit, wherein the first correlation unit is used to convert the electricity efficiency Hyydxl of the j-th sub-industry into j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j After dimensionless processing, the following correlation formula is used to generate the comprehensive index Zhzs of the j-th sub-industry: j :

[0036] Zhzs j =γ1*Hyydxl j +γ2*Lsbl jfy +γ3*Rgxl j +A1

[0037] Where γ1, γ2 and γ3 represent the electricity efficiency of the j-th sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j The weight value is adjusted by the user, 0<γ1<1, 0<γ2<1, 0<γ3<1, γ1+γ2+γ3=1, A1 represents the first correction constant value;

[0038] The second correlation unit generates the ratio of the electricity consumption efficiency of the high-end equipment manufacturing industry to the electricity consumption efficiency of the traditional equipment manufacturing industry through the following correlation formula: electricity consumption rate ratio Ydlb:

[0039]

[0040] Where Gdxl is the electricity consumption efficiency of high-end equipment manufacturing industry, and Ctxl is the electricity consumption efficiency of traditional equipment manufacturing industry.

[0041] Preferably, the evaluation module includes a first evaluation unit, a second evaluation unit and a third evaluation unit;

[0042] The first evaluation unit is used to compare and evaluate the ratio of the power consumption efficiency of the high-end equipment manufacturing industry to the power consumption efficiency of the traditional equipment manufacturing industry: the power consumption rate ratio Ydlb and the power consumption rate ratio threshold Q1, and obtain a first evaluation result, including:

[0043] If the electricity consumption ratio Ydlb>Q1, it means that the electricity consumption efficiency control of the high-end equipment manufacturing industry is far ahead of that of the traditional equipment manufacturing industry. It is worth learning from the advanced achievements of the high-end equipment manufacturing industry to upgrade and transform the traditional equipment manufacturing industry.

[0044] If 1 < electricity consumption ratio Ydlb ≤ Q1, it means that the electricity efficiency control of the high-end equipment manufacturing industry is ahead of that of the traditional equipment manufacturing industry. The electricity efficiency control is not obvious. It is necessary to analyze the reasons, ensure R&D investment, and further upgrade and transform the high-end equipment manufacturing industry.

[0045] If the electricity consumption ratio Ydlb≤1, it means that the electricity consumption efficiency of the high-end equipment manufacturing industry is lower than that of the traditional equipment manufacturing industry. On-site investigation is needed to find out the reasons, replace advanced equipment, and further transform and upgrade the high-end equipment manufacturing industry.

[0046] The second evaluation unit is used to calculate the j-th sub-industry comprehensive index Zhzs j Compare and evaluate with the comprehensive threshold Q2 to obtain a second evaluation result, including:

[0047] If the comprehensive index of sub-industry of category j is Zhzs j ≥ comprehensive threshold Q2, indicating that the j-th sub-industry belongs to the green, advanced and efficient industry; if the j-th sub-industry comprehensive index Zhzs j <Comprehensive threshold Q2 indicates that the j-th sub-industry does not belong to the green, advanced and efficient industry, and sends the first warning instruction to remind the j-th sub-industry that it needs to be transformed and upgraded. At the same time, the third evaluation unit is triggered through the j-th sub-industry electricity efficiency Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Continue to conduct in-depth assessments of sub-industry issues in category j and obtain the third assessment results, including:

[0048] If the electricity efficiency of the j-th sub-industry is Hyydxl j ≥ the power efficiency threshold Q3, indicating that the j-th sub-industry has high power efficiency and belongs to the advanced industry. If the power efficiency of the j-th sub-industry is Hyydxl j The power efficiency threshold Q3 indicates that the j-th sub-industry is inefficient in power consumption and is classified as a backward industry. A second warning instruction is issued and a strategy is generated: the power facilities of the j-th sub-industry are upgraded to ensure efficient power consumption.

[0049] If the consumption ratio of green energy of type f in the j-th sub-industry is Lsbl jfy ≥ green energy threshold Q4, indicating that the j-th sub-industry belongs to the green environmental protection industry. If the consumption ratio of the f-th type of green energy in the j-th sub-industry is Lsbl jfy Green energy threshold Q4 indicates that the j-th sub-industry relies on traditional fossil energy and does not belong to the green environmental protection industry. The third warning instruction is issued and a strategy is generated: upgrade and transform the j-th sub-industry to rely on green energy for production;

[0050] If the labor efficiency of the j-th sub-industry is Rgxlj ≥ the labor efficiency threshold Q5, indicating that the automation level of the j-th sub-industry is excellent and it belongs to the high-efficiency industry. If the labor efficiency of the j-th sub-industry Rgxl j <The labor efficiency threshold Q5 indicates that the automation level of the j-th sub-industry is unqualified and it is an inefficient industry. The fourth early warning instruction is issued and a strategy is generated: the automation level of the j-th sub-industry is upgraded and transformed to achieve reduction in staff and increase in efficiency.

[0051] A method for analyzing productivity based on power data, comprising the following steps:

[0052] Step 1: Industry data collection: Use data crawler technology to collect data from official websites and commercial data analysis websites, and extract monthly electricity consumption data, monthly output value, monthly consumption of various energy sources, and number of employees for each month over the past five years according to sub-industry classification;

[0053] Step 2: Data processing: Clean and organize the collected data, remove duplicate, erroneous, and invalid data to ensure data accuracy and completeness, and use the processed data to construct a time series sub-industry dataset;

[0054] Step 3: Data analysis: Use the pre-established time series analysis model to conduct in-depth analysis of power data and enterprise data to explore the correlation and trend information; calculate the electricity efficiency of the j-th sub-industry based on the time series sub-industry data set Hyydxl j , obtain the electricity consumption efficiency Gdxl of high-end equipment manufacturing industry, the electricity consumption efficiency Ctxl of traditional equipment manufacturing industry, and the consumption ratio Nybl of the j-type sub-industry for the y-type energy jy , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j ; The electricity efficiency of the j-type sub-industry is Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Associated to generate the j-th sub-industry comprehensive index Zhzs j , the electricity consumption efficiency Gdxl of high-end equipment manufacturing industry and the electricity consumption efficiency Ctxl of traditional equipment manufacturing industry are correlated to generate the electricity consumption rate ratio Ydlb;

[0055] Step 4: Data evaluation: The first evaluation unit evaluates the ratio of the electricity efficiency of the high-end equipment manufacturing industry to the electricity efficiency of the traditional equipment manufacturing industry: the electricity rate ratio Ydlb;

[0056] The second evaluation unit evaluates the comprehensive index of the j-th sub-industry Zhzs jThe third evaluation unit is evaluated through the electricity efficiency of the j-type sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Continue to conduct in-depth assessments of the j-type sub-industry issues and obtain corresponding assessment results and corresponding strategies;

[0057] Step 5: Trend Forecasting: Based on historical data and current trends, machine learning and deep learning algorithms are used to forecast and assess future electricity demand and productivity development in each sub-sector.

[0058] Step 6. Organize the analysis and evaluation results into a report, put forward specific suggestions and improvement measures, and predict the future development trends of each sub-industry.

[0059] The present invention has the following beneficial effects:

[0060] (1) Step 1 of the present invention collects data such as monthly electricity consumption, output value, energy consumption and number of employees of each sub-industry in a fast and efficient manner, obtains multi-source data, and conducts comparative analysis to obtain more accurate and rich data resources.

[0061] (2) Formulas are established to clean and organize the collected data, remove duplicate, erroneous, and invalid data, and for the same data x obtained from various websites, if the values ​​are different, the maximum and minimum values ​​are removed and the average value is taken to enhance the data accuracy. The processed data is used to construct a time series sub-industry data set to facilitate the management of data with a long span.

[0062] (3) Utilizing analytical models, we deeply explore and calculate the electricity efficiency of both high-end and traditional equipment manufacturing industries. We calculate indicators across multiple dimensions for each sub-industry: electricity efficiency, energy consumption ratio, and labor efficiency, and generate a comprehensive index for each sub-industry. Through three evaluation units, we comprehensively evaluate and analyze the efficiency and energy utilization of sub-industries from multiple perspectives. We also compare the electricity efficiency of high-end and traditional equipment manufacturing industries, and comprehensively assess productivity.

[0063] (4) Use the trained prediction module to predict the future electricity demand and productivity development trend of each sub-industry, save manpower, make predictions quickly and accurately, and guide the healthy development of each sub-industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a schematic diagram of a system block diagram and flow chart of productivity analysis based on power data according to the present invention;

[0065] Figure 2 This is a schematic diagram of the steps of a method for analyzing productivity based on power data according to the present invention;

[0066] Figure 3 This is a schematic diagram of a device for analyzing productivity based on power data according to the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0068] Example 1

[0069] See also Figure 1 Specifically, a system for analyzing productivity based on power data includes:

[0070] Sub-industry data collection module: Extract monthly electricity consumption data, monthly output value, monthly consumption of various energy sources and number of employees for five years according to sub-industry classification; data extraction can be obtained from relevant competent authorities, or by using data crawler technology to collect data through official websites and commercial data analysis websites, traversing web pages and grabbing data on the pages.

[0071] Data processing module: Cleans and organizes the collected data, removes duplicate, erroneous, and invalid data. If the values ​​of the same data x obtained from different websites are different, the maximum and minimum values ​​are removed and the average value is taken. The processed data is used to construct a time series sub-industry dataset;

[0072] Data analysis module: used to establish a time series analysis model, and calculate the power consumption per unit output value of the j-th sub-industry based on the time series sub-industry data set, that is, the power efficiency of the j-th sub-industry Hyydxl j ; Classify the sub-industries into high-end equipment manufacturing and traditional equipment manufacturing, calculate the electricity efficiency of high-end equipment manufacturing Gdxl and the electricity efficiency of traditional equipment manufacturing Ctxl; calculate the consumption ratio of the j-th sub-industry to the y-th energy Nybl jy , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy , labor efficiency of the jth sub-industry Rgxl j ; The electricity efficiency of the j-type sub-industry is Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Associated to generate the j-th sub-industry comprehensive index Zhzs j :

[0073] Evaluation module: If the comprehensive index of the j-th sub-industry is Zhzs j ≥ comprehensive threshold Q1, indicating that the j-th sub-industry belongs to the green, advanced and efficient industry; if the comprehensive index of the j-th sub-industry Zhzs j < comprehensive threshold Q1, indicating that the j-th sub-industry does not belong to the green, advanced and efficient industry, sends the first warning instruction, and further through the j-th sub-industry electricity efficiency Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy , labor efficiency of the jth sub-industry Rgxl j Continue to conduct in-depth assessments of issues in the jth sub-industry, compare and analyze them with corresponding thresholds, trigger corresponding early warning instructions, and generate corresponding strategies; evaluate the ratio of electricity efficiency of high-end equipment manufacturing to that of traditional equipment manufacturing: the electricity rate ratio, and analyze the differences in technology level and energy utilization efficiency between high-end equipment manufacturing and traditional equipment manufacturing;

[0074] Forecasting module: Use machine learning and deep learning algorithms to predict and evaluate the future electricity demand and productivity development of each sub-industry. Before conducting the forecast and evaluation, the collected and organized historical data and current trend data of each sub-industry are input into the forecasting module for training.

[0075] In this embodiment, the system collects monthly data on electricity consumption, output value, energy consumption, and employee headcount for each sub-industry quickly and efficiently, acquiring multi-source data for comparative analysis, resulting in a more accurate and richer data resource. Formulas are established to clean and organize the collected data, removing duplicate, erroneous, and invalid data. For data from different sources with varying values, the system removes the maximum and minimum values ​​and takes the average value to enhance data accuracy. This processed data is then used to construct a time series sub-industry dataset, facilitating the management of data spanning long time periods. Analytical models are used to deeply mine and calculate the electricity efficiency of both high-end and traditional equipment manufacturing industries. Indicators across multiple dimensions for each sub-industry are calculated, including electricity efficiency, energy consumption ratio, and labor efficiency, and a comprehensive index for each sub-industry is generated. Three evaluation units are used to comprehensively analyze sub-industry efficiency and energy utilization from multiple perspectives, comparing the electricity efficiency ratios of high-end equipment manufacturing with those of traditional equipment manufacturing to provide a comprehensive assessment of productivity. A trained prediction module is used to predict future electricity demand and productivity trends for each sub-industry, saving manpower and providing rapid and accurate forecasts that can guide the healthy development of each sub-industry. Through comprehensive data collection, precise data processing, in-depth data analysis and scientific evaluation and prediction, this system provides strong technical support and decision-making basis for improving the production efficiency and energy utilization efficiency of various sub-industries.

[0076] Example 2

[0077] See also Figure 1 Specifically, the productivity includes: new generation information technology, biotechnology, new energy, new materials, high-end equipment, new energy vehicles, green environmental protection, civil aviation, shipbuilding and marine engineering equipment, artificial intelligence, quantum information, mobile communications, the Internet of Things and blockchain.

[0078] In this embodiment, productivity is driven by revolutionary technological breakthroughs, innovative allocation of production factors, and deep industrial transformation and upgrading, specifically including: new generation information technology, biotechnology, new energy, new materials, high-end equipment, new energy vehicles, green environmental protection, civil aviation, shipbuilding and marine engineering equipment, artificial intelligence, quantum information, mobile communications, the Internet of Things, and blockchain.

[0079] Example 3

[0080] See also Figure 1 Specifically, if the values ​​of the same data x are different, they are standardized using the following formula:

[0081]

[0082] Where i = 1, 2, 3, ..., n, i represents the sequence number of the same data captured on the i-th website, n represents the total number of the same data captured on n different websites, x i Represents the same data value captured on the i-th website, x max represents the maximum value of the same data crawled on n different websites, x min Represents the minimum value of the same data crawled from n different websites.

[0083] In this embodiment, when the numerical values ​​of the same data from different sources are different, the maximum and minimum values ​​are removed and the average value is taken to enhance the data accuracy and lay a good data foundation for constructing the time series sub-industry dataset.

[0084] Example 4

[0085] See also Figure 1 Specifically, the data analysis module is used to establish a time series analysis model, including a first calculation unit, which is used to calculate and obtain the electricity efficiency Hyydxl of the j-th sub-industry based on the time series sub-industry data set. j , electricity consumption efficiency Gdxl of high-end equipment manufacturing industry and electricity consumption efficiency Ctxl of traditional equipment manufacturing industry;

[0086] The electricity efficiency of the j-th sub-industry is Hyydxl j Calculate using the following formula:

[0087]

[0088] Where j represents the sub-industry number, k = 1, 2, 3, ..., m, k represents the month number of the data, m represents the total number of months for which data for the past m months were obtained, Q(j,k) represents the monthly electricity consumption of the j-th sub-industry in the k-th month, and P(j,k) represents the monthly output value of the j-th sub-industry in the k-th month.

[0089] The electricity consumption efficiency Gdxl of the high-end equipment manufacturing industry is calculated using the following formula:

[0090]

[0091] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, g = 1, 2, 3, ..., s, g represents the serial number of the high-end equipment manufacturing industry, s represents the total number of high-end equipment manufacturing industries, Q(g, k) represents the monthly electricity consumption of the g-th high-end equipment manufacturing industry in the k-th month, Q(g, k) represents the monthly output value of the g-th high-end equipment manufacturing industry in the k-th month, represents the total monthly electricity consumption of all s high-end equipment manufacturing industries in m months. It represents the monthly output value of all s high-end equipment manufacturing industries in a total of m months;

[0092] The electricity consumption efficiency Ctxl of the traditional equipment manufacturing industry is calculated using the following formula:

[0093]

[0094] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, c = 1, 2, 3, ..., s, c represents the serial number of the traditional equipment manufacturing industry, q represents the total number of traditional equipment manufacturing industries, Q(c, k) represents the monthly electricity consumption of the cth traditional equipment manufacturing industry in the kth month, and Q(c, k) represents the monthly output value of the cth traditional equipment manufacturing industry in the kth month. represents the total monthly electricity consumption of all q traditional equipment manufacturing industries in m months. It represents the monthly output value of all q traditional equipment manufacturing industries in a total of m months.

[0095] In this example, a time series analysis model was constructed to collect and process time series data from sub-industry datasets, specifically data related to industry electricity consumption and output value. This model was used to assess the electricity efficiency of different industries, particularly sub-industries, high-end equipment manufacturing, and traditional equipment manufacturing. This analysis is crucial for understanding energy efficiency, industry development trends, and guiding the healthy development of each sub-industry.

[0096] The first calculation unit is the core part of the data analysis module. It is responsible for calculating the electricity efficiency of different industries based on the provided time series sub-industry dataset. The details are as follows:

[0097] Electricity efficiency of the jth sub-sector: This metric measures the electricity efficiency of a specific sub-sector (indicated by the index j). The average electricity efficiency for the entire analysis period is calculated by taking the ratio of the sum of the sub-sector's electricity consumption over m months to its monthly output over m months.

[0098] High-end equipment manufacturing electricity efficiency (Gdxl): This metric measures the electricity efficiency of the entire high-end equipment manufacturing industry. The average electricity efficiency of the entire high-end equipment manufacturing industry is calculated by calculating the ratio of total electricity consumption over m months to total output value over m months.

[0099] Traditional equipment manufacturing electricity efficiency (Ctxl): This indicator measures the electricity efficiency of the traditional equipment manufacturing industry. The average electricity efficiency of the entire traditional equipment manufacturing industry is calculated by calculating the ratio of total electricity consumption over m months to total output value over m months.

[0100] These calculations provide decision makers with key energy efficiency indicators, helping them understand the energy performance of different industries, particularly the equipment manufacturing sector. By comparing electricity efficiency across different industries or sub-industries, potential optimization opportunities for energy utilization can be identified, thereby promoting energy conservation, emission reduction, and sustainable development.

[0101] Example 5

[0102] See also Figure 1 Specifically, the data analysis module further includes a second calculation unit, which is used to calculate and obtain: the consumption ratio Nybl of the yth type of energy in the jth sub-industry based on the time series sub-industry data set jy And the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy Because the measurement units of various energy sources are different, the consumption of various energy sources is first converted into the consumption of standard coal according to the conversion coefficients of energy types issued by the energy management department, and then the calculation is carried out;

[0103] The consumption ratio of the yth type of energy in the jth sub-industry is Nybl jy Calculate using the following formula:

[0104]

[0105] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, j represents the serial number of the sub-industry, y = 1, 2, 3, ..., t, y represents the serial number of different types of energy, t represents the total number of energy types, N(j, k, y) represents the consumption of the yth type of energy in the kth month of the jth sub-industry, It represents the consumption of the yth type of energy in the jth sub-industry within m months. It represents the consumption of all t types of energy in the j-th sub-industry in m months;

[0106] The consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy Calculate using the following formula:

[0107]

[0108] Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, j represents the serial number of the sub-industry, y = 1, 2, 3, ..., t, y represents the serial number of different types of energy, t represents the total number of energy types, f = 1, 2, 3, ..., u, f represents the serial number of different types of green energy, u represents the total number of green energy types, N(j, k, f) represents the f-th type of green energy consumption obtained in the k-th month of the j-th sub-industry, and N(j, k, y) represents the y-th type of energy consumption obtained in the k-th month of the j-th sub-industry. It represents the consumption of all u types of green energy in the j-th sub-industry in m months. It represents the consumption of all t types of energy in the j-th sub-industry within m months.

[0109] In this embodiment, the content further expands the functionality of the data analysis module by introducing a second calculation unit that focuses on calculating the proportion of various energy sources consumed by each sub-industry, as well as the proportion of green energy consumed. This analysis is important for understanding the industry's energy structure, optimizing energy use, and promoting the development of green energy.

[0110] The second calculation unit uses the time series sub-industry data set and obtains two key indicators through a series of calculation steps: the consumption ratio of the yth type of energy in the jth sub-industry (Nybl jy ) and the consumption ratio of the yth type of green energy in the jth sub-industry (Lsbl jfy Since different energy sources have different measurement units, in order to make a unified comparison and analysis, it is first necessary to convert the consumption of these energy sources into the consumption of standard coal. This is done based on the energy type conversion coefficients issued by the energy management department.

[0111] Nybljy, the proportion of energy consumption by type y in sub-sector j, reflects the distribution of energy consumption by sub-sector j over a period of time (m months). By calculating the contribution of each energy source to total energy consumption, we can determine the sub-sector's energy consumption ratio. This ratio helps understand the sub-sector's energy consumption structure and provides data support for energy management and optimization.

[0112] The consumption ratio of the fth type of green energy in the jth sub-industry Lsbljfy: This indicator further refines the calculation of the energy consumption ratio, with special attention paid to the consumption of green energy. By calculating the proportion of green energy in the total energy consumption, the performance of the sub-industry in green energy utilization can be evaluated, providing data support for promoting the development of green energy.

[0113] The results of the second calculation unit are of great reference value to energy management departments, industry associations, and enterprises themselves. By understanding the proportion of various energy sources consumed by each sub-industry, as well as the proportion of green energy consumed, more scientific and reasonable energy management strategies can be formulated to optimize the energy structure, improve energy efficiency, reduce environmental pollution, promote sustainable development, and contribute to the green development of the entire industry.

[0114] Example 6

[0115] See also Figure 1 Specifically, the data analysis module further includes a third calculation unit, which is used to calculate and obtain the labor efficiency Rgxl of the j-th sub-industry based on the time series sub-industry data set. j , the labor efficiency of the j-th sub-industry Rgxl j Calculate using the following formula:

[0116]

[0117] In the formula, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, Q(j,k) represents the monthly output value of the jth sub-industry in the kth month, It represents the total output value of the j-th sub-industry for m months, and Rs(j,k) represents the number of employees in the j-th sub-industry in the k-th month.

[0118] The data analysis module further includes a first correlation unit and a second correlation unit. The first correlation unit is used to convert the electricity efficiency of the j-th sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl jAfter dimensionless processing, the following correlation formula is used to generate the comprehensive index Zhzs of the j-th sub-industry: j :

[0119] Zhzs j =γ1*Hyydxl j +γ2*Lsbl jfy +γ3*Rgxl j +A1

[0120] Where γ1, γ2 and γ3 represent the electricity efficiency of the j-th sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j The weight value is adjusted by the user, 0<γ1<1, 0<γ2<1, 0<γ3<1, γ1+γ2+γ3=1, A1 represents the first correction constant value;

[0121] The second correlation unit generates the ratio of the electricity consumption efficiency of the high-end equipment manufacturing industry to the electricity consumption efficiency of the traditional equipment manufacturing industry through the following correlation formula: electricity consumption rate ratio Ydlb:

[0122]

[0123] Where Gdxl is the electricity consumption efficiency of high-end equipment manufacturing industry, and Ctxl is the electricity consumption efficiency of traditional equipment manufacturing industry.

[0124] In this embodiment, the data analysis module further includes a third calculation unit, a first correlation unit and a second correlation unit. The third calculation unit is used to calculate the labor efficiency (Rgxl) of a specific sub-industry (class j) based on the time series sub-industry data set. j By calculating the ratio of the total monthly output value of a specific sub-industry (class j) for m months to the total number of monthly employees in the specific sub-industry for m months, the labor efficiency (Rgxl) of the specific sub-industry (class j) is obtained. j ), which reflects the monthly average labor efficiency of the sub-industry during the investigation period.

[0125] The first associated unit performs dimensionless processing (i.e., standardization or normalization) on electricity efficiency (Hyydxlj), green energy consumption ratio (Lsbljfy) and labor efficiency (Rgxlj) so that they can be compared and summed on the same scale to obtain the j-th specific sub-industry comprehensive index Zhzs j This formula integrates efficiency indicators from three different dimensions (electricity efficiency, green energy consumption ratio, and labor efficiency) into a comprehensive index, thereby comprehensively evaluating the overall performance of a specific sub-industry.

[0126] The second associated unit calculates the ratio of the electricity consumption efficiency of the high-end equipment manufacturing industry to that of the traditional equipment manufacturing industry to obtain the electricity consumption rate ratio Ydlb, which is used to reflect the degree of difference between the electricity consumption efficiency of the high-end equipment manufacturing industry and that of the traditional equipment manufacturing industry.

[0127] Example 7

[0128] See also Figure 1 ,Specifically, the evaluation module includes a first evaluation unit, a second evaluation unit and a third evaluation unit;

[0129] The first evaluation unit is used to compare and evaluate the ratio of the power consumption efficiency of the high-end equipment manufacturing industry to the power consumption efficiency of the traditional equipment manufacturing industry: the power consumption rate ratio Ydlb and the power consumption rate ratio threshold Q1, and obtain a first evaluation result, including:

[0130] If the electricity consumption ratio Ydlb>Q1, it means that the electricity consumption efficiency control of the high-end equipment manufacturing industry is far ahead of that of the traditional equipment manufacturing industry. It is worth learning from the advanced achievements of the high-end equipment manufacturing industry to upgrade and transform the traditional equipment manufacturing industry.

[0131] If 1 < electricity consumption ratio Ydlb ≤ Q1, it means that the electricity efficiency control of the high-end equipment manufacturing industry is ahead of that of the traditional equipment manufacturing industry. The electricity efficiency control is not obvious. It is necessary to analyze the reasons, ensure R&D investment, and further upgrade and transform the high-end equipment manufacturing industry.

[0132] If the electricity consumption ratio Ydlb≤1, it means that the electricity consumption efficiency of the high-end equipment manufacturing industry is lower than that of the traditional equipment manufacturing industry. On-site investigation is needed to find out the reasons, replace advanced equipment, and further transform and upgrade the high-end equipment manufacturing industry.

[0133] The second evaluation unit is used to calculate the j-th sub-industry comprehensive index Zhzs j Compare and evaluate with the comprehensive threshold Q2 to obtain a second evaluation result, including:

[0134] If the comprehensive index of sub-industry of category j is Zhzs j ≥ comprehensive threshold Q2, indicating that the j-th sub-industry belongs to the green, advanced and efficient industry; if the j-th sub-industry comprehensive index Zhzs j <Comprehensive threshold Q2 indicates that the j-th sub-industry does not belong to the green, advanced and efficient industry, and sends the first warning instruction to remind the j-th sub-industry that it needs to be transformed and upgraded. At the same time, the third evaluation unit is triggered through the j-th sub-industry electricity efficiency Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Continue to conduct in-depth assessments of sub-industry issues in category j and obtain the third assessment results, including:

[0135] If the electricity efficiency of the j-th sub-industry is Hyydxl j ≥ the power efficiency threshold Q3, indicating that the j-th sub-industry has high power efficiency and belongs to the advanced industry. If the power efficiency of the j-th sub-industry is Hyydxl j The power efficiency threshold Q3 indicates that the j-th sub-industry is inefficient in power consumption and is classified as a backward industry. A second warning instruction is issued and a strategy is generated: the power facilities of the j-th sub-industry are upgraded to ensure efficient power consumption.

[0136] If the consumption ratio of green energy of type f in the j-th sub-industry is Lsbl jfy ≥ green energy threshold Q4, indicating that the j-th sub-industry belongs to the green environmental protection industry. If the consumption ratio of the f-th type of green energy in the j-th sub-industry is Lsbl jfy Green energy threshold Q4 indicates that the j-th sub-industry relies on traditional fossil energy and does not belong to the green environmental protection industry. The third warning instruction is issued and a strategy is generated: upgrade and transform the j-th sub-industry to rely on green energy for production;

[0137] If the labor efficiency of the j-th sub-industry is Rgxl j ≥ the labor efficiency threshold Q5, indicating that the automation level of the j-th sub-industry is excellent and it belongs to the high-efficiency industry. If the labor efficiency of the j-th sub-industry Rgxl j <The labor efficiency threshold Q5 indicates that the automation level of the j-th sub-industry is unqualified and it is an inefficient industry. The fourth early warning instruction is issued and a strategy is generated: the automation level of the j-th sub-industry is upgraded and transformed to achieve reduction in staff and increase in efficiency.

[0138] This example describes a modular system for industry evaluation and optimization, specifically divided into three evaluation units. It aims to use different indicators and thresholds to determine the energy efficiency, environmental protection, and automation levels of different industries (especially sub-industries), and propose corresponding transformation and upgrading strategies accordingly. The following is a detailed explanation:

[0139] The first evaluation unit evaluates the energy efficiency performance of the high-end equipment manufacturing industry by comparing the electricity consumption rates of the high-end equipment manufacturing industry and the traditional equipment manufacturing industry, and accordingly puts forward corresponding improvement suggestions.

[0140] High-end equipment manufacturing, due to its strong technological innovation capabilities, widespread application of information technology and automation, and efficient energy management and energy-saving technologies, typically demonstrates higher electricity efficiency. In contrast, traditional equipment manufacturing lags behind in technological innovation and energy-saving technology application, resulting in lower electricity efficiency and greater resource waste.

[0141] Specifically, this evaluation process involves the following key steps and conclusions:

[0142] Evaluation logic and conclusion:

[0143] When the electricity consumption ratio Ydlb>Q1: This means that the high-end equipment manufacturing industry is far superior to the traditional equipment manufacturing industry in terms of electricity consumption rate control, which shows that the high-end equipment manufacturing industry has achieved remarkable results in energy utilization and management. Its advanced results are worthy of reference by the traditional equipment manufacturing industry for upgrading and transformation to improve energy utilization efficiency.

[0144] When 1 < power consumption ratio Ydlb ≤ Q1, while the high-end equipment manufacturing industry still outperforms the traditional equipment manufacturing industry in terms of power consumption control, this advantage is not significant. This suggests that further analysis is needed. Perhaps the high-end equipment manufacturing industry still has room for improvement in certain areas, or its advantages have not yet been fully utilized. Therefore, it is recommended to ensure R&D investment and further upgrade and transform the high-end equipment manufacturing industry to expand its advantages in power consumption control.

[0145] When the electricity consumption ratio Ydlb ≤ 1, this is the most undesirable situation, indicating that the high-end equipment manufacturing industry's electricity consumption is even lower than that of the traditional equipment manufacturing industry. This generally indicates that there are problems with energy utilization and management in the high-end equipment manufacturing industry, and immediate action is needed. It is recommended to conduct on-site investigations to thoroughly investigate the causes, which may be caused by aging equipment or poor management. To address these issues, advanced equipment should be replaced and the high-end equipment manufacturing industry should be upgraded.

[0146] The second evaluation unit is used to preliminarily assess the comprehensive energy efficiency level of sub-industry category j. By comparing it with the comprehensive threshold Q2, it determines whether it belongs to the green, advanced, and efficient industry. The evaluation logic: If the comprehensive index of sub-industry category j is greater than or equal to Q2, it is considered a green, advanced, and efficient industry. If it is less than Q2, it is considered a non-green, advanced, and efficient industry and a first warning instruction is issued, indicating that the sub-industry needs to be upgraded. Simultaneously, the third evaluation unit is triggered for more in-depth analysis.

[0147] After the second assessment unit determined that the industry was not green, advanced and efficient, the third assessment unit conducted a further in-depth assessment from three dimensions: electricity efficiency, green energy consumption ratio and labor efficiency.

[0148] Electricity efficiency (Hyydxlj): If it is higher than the electricity efficiency threshold Q3, it is considered to be efficient and belongs to an advanced industry; otherwise, it is considered to be inefficient and the electricity facilities need to be upgraded.

[0149] Green energy consumption ratio (Lsbljfy): If it is higher than the green energy threshold Q4, it is considered a green and environmentally friendly industry; otherwise, it is considered to be dependent on traditional fossil energy and needs to be upgraded to use green energy.

[0150] Labor efficiency (Rgxlj): If it is higher than the labor efficiency threshold Q5, it is considered to have an excellent degree of automation and belongs to a high-efficiency industry; otherwise, it is considered to have an insufficient degree of automation and requires automation upgrades.

[0151] The entire evaluation system uses hierarchical evaluation units, from comprehensive to specific, from preliminary to in-depth, to comprehensively evaluate the energy efficiency, environmental protection and automation levels of different sub-industries, and proposes targeted transformation and upgrading strategies based on this. This will help promote the development of related industries in the direction of green, efficient and automated, and achieve sustainable development.

[0152] Example 8

[0153] See also Figure 2 Specifically, a method for analyzing productivity based on power data includes the following steps:

[0154] Step 1: Industry data collection: Use data crawler technology to collect data from official websites and commercial data analysis websites, and extract monthly electricity consumption data, monthly output value, monthly consumption of various energy sources, and number of employees for each month over the past five years according to sub-industry classification;

[0155] Step 2: Data processing: Clean and organize the collected data, remove duplicate, erroneous, and invalid data to ensure data accuracy and completeness, and use the processed data to construct a time series sub-industry dataset;

[0156] Step 3: Data analysis: Use the pre-established time series analysis model to conduct in-depth analysis of power data and enterprise data to explore the correlation and trend information; calculate the electricity efficiency of the j-th sub-industry based on the time series sub-industry data set Hyydxl j , obtain the electricity consumption efficiency Gdxl of high-end equipment manufacturing industry, the electricity consumption efficiency Ctxl of traditional equipment manufacturing industry, and the consumption ratio Nybl of the j-type sub-industry for the y-type energy jy , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j ; The electricity efficiency of the j-type sub-industry is Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Associated to generate the j-th sub-industry comprehensive index Zhzs j , the electricity consumption efficiency Gdxl of high-end equipment manufacturing industry and the electricity consumption efficiency Ctxl of traditional equipment manufacturing industry are correlated to generate the electricity consumption rate ratio Ydlb;

[0157] Step 4. Data evaluation: The first evaluation unit evaluates the ratio of electricity efficiency of high-end equipment manufacturing industry to that of traditional equipment manufacturing industry: electricity efficiency ratio Ydlb; the second evaluation unit evaluates the comprehensive index Zhzs of the j-th sub-industry j The third evaluation unit is evaluated through the electricity efficiency of the j-type sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Continue to conduct in-depth assessments of the j-type sub-industry issues and obtain corresponding assessment results and corresponding strategies;

[0158] Step 5: Trend Forecasting: Based on historical data and current trends, machine learning and deep learning algorithms are used to forecast and assess future electricity demand and productivity development in each sub-sector.

[0159] Step 6. Organize the analysis and evaluation results into a report, put forward specific suggestions and improvement measures, and predict the future development trends of each sub-industry.

[0160] In this example, a method for analyzing productivity based on power data is a systematic analytical framework that aims to evaluate and improve the productivity levels of different sub-sectors through power data analysis, especially in terms of energy utilization, technical efficiency, and sustainable development. The following is a detailed explanation of each step:

[0161] Step 1: Industry data collection

[0162] Data source: Data is collected from multiple authoritative and professional websites (such as the State Grid, industry associations, and commercial data analysis websites) using data crawler technology.

[0163] Data content: Includes monthly electricity consumption, monthly output value, monthly consumption of various energy sources, and number of employees for each of the past five years, categorized by sub-industry. This data provides the basis for subsequent in-depth analysis.

[0164] Step 2: Data processing

[0165] Data cleaning: Remove duplicate, erroneous, and invalid data to ensure data accuracy and reliability.

[0166] Data organization: The cleaned data is organized into time series sub-industry datasets to prepare for subsequent analysis.

[0167] Step 3: Data Analysis

[0168] Model application: Use time series analysis models to conduct in-depth analysis of power data and enterprise data to reveal the correlation and trends between the data.

[0169] Calculation of key indicators: Calculate key indicators such as electricity efficiency, green energy consumption ratio, labor efficiency, etc. of each sub-industry, and generate a comprehensive sub-industry index to comprehensively evaluate the productivity level and sustainability of the sub-industry.

[0170] Step 4: Data Evaluation

[0171] Multi-level assessment:

[0172] The first evaluation unit: Evaluate the ratio of electricity efficiency of high-end equipment manufacturing industry to that of traditional equipment manufacturing industry: electricity efficiency ratio, and analyze the differences between the two in terms of technical level and energy utilization efficiency.

[0173] The second evaluation unit: Evaluate the comprehensive index of sub-industries and make a preliminary judgment on the overall performance of sub-industries.

[0174] The third evaluation unit: Based on specific indicators such as electricity efficiency, green energy consumption ratio and labor efficiency, conduct a more in-depth evaluation of the sub-industry and propose corresponding improvement strategies.

[0175] Step 5: Trend Forecast

[0176] Forecasting Methodology: Using machine learning and deep learning algorithms, combined with historical data and current trends, we predict future electricity demand and productivity trends for each sub-sector.

[0177] Purpose of forecasting: To provide forward-looking decision-making support to enterprises and governments and help formulate long-term development plans.

[0178] Step 6: Report and Recommendations

[0179] Report compilation: The analysis and evaluation results are compiled into a report to clearly present the current status, problems, room for improvement and future development trends of each sub-industry.

[0180] Recommendations and measures: Based on the analysis results, specific recommendations and improvement measures are put forward to help enterprises improve productivity, optimize energy structure, improve energy efficiency, and promote sustainable development of the industry.

[0181] This approach provides strong support for improving the productivity level of various sub-industries through comprehensive and in-depth data analysis and evaluation, and helps promote the transformation, upgrading and sustainable development of the industry.

[0182] Example 9

[0183] See also Figure 3 Specifically, a device for analyzing productivity based on power data includes:

[0184] Processor: When executing instructions, performs the system and method;

[0185] Memory: used to store computer instructions. When the processor executes the instructions, the above-mentioned system and method are executed;

[0186] A computer-readable storage medium comprising a computer program or instructions, which, when executed on a computer, causes the computer to execute the above-mentioned system and method;

[0187] Specifically include:

[0188] Sub-industry data collection module: responsible for collecting monthly electricity consumption data, monthly output value, monthly consumption of various energy sources and number of employees by sub-industry classification;

[0189] Data processing module: responsible for data cleaning and organization, and using the processed data to construct a time series sub-industry dataset;

[0190] Data analysis module: performs computational analysis based on time series sub-industry data sets;

[0191] Evaluation Module: Evaluate the green, advanced and efficient level of each sub-industry and provide rectification strategies; compare and evaluate the ratio of electricity efficiency of high-end equipment manufacturing industry to that of traditional equipment manufacturing industry: electricity efficiency ratio;

[0192] Forecasting module: Based on historical data and current trends, it uses machine learning and deep learning algorithms to predict and evaluate the future power demand and productivity development of each sub-industry.

[0193] This embodiment describes a device for analyzing productivity based on power data. This device integrates multiple key components and aims to improve the productivity of various sub-industries through in-depth analysis of power data and assess their green, advanced, and efficient status. The following is a detailed explanation of each component:

[0194] Processor: The processor is the core component of the device, responsible for executing computer instructions stored in the memory. These instructions correspond to the above-mentioned systems and methods, that is, performing a series of operations such as power data analysis, processing, analysis, evaluation and prediction.

[0195] Memory: Memory stores computer instructions and data. When the processor needs to perform an operation, it reads the corresponding instructions from memory and executes them. These instructions correspond to the system and method for achieving productivity in power data analysis, including steps such as data acquisition, processing, analysis, evaluation, and prediction.

[0196] Computer-readable storage medium: This is a physical medium, such as a hard drive or flash memory, that stores computer programs or instructions. When these programs or instructions are executed on a computer (or this device), they direct the computer to perform the systems and methods described above. This ensures the functionality and reproducibility of the device.

[0197] Sub-industry Data Collection Module: This module collects relevant data by sub-industry, including monthly electricity consumption, monthly output value, monthly consumption of various energy sources, and number of employees. This data forms the basis for subsequent analysis and evaluation and is crucial for understanding the sub-industry's operational status and energy use.

[0198] Data Processing Module: This module is responsible for cleaning and organizing the collected raw data. This includes removing duplicate, erroneous, and invalid data to ensure data accuracy and completeness. The processed data is then used to construct a time series sub-industry dataset, providing the foundation for subsequent in-depth analysis.

[0199] Data Analysis Module: Based on time-series sub-industry datasets, the Data Analysis Module performs various calculations and analyses. This includes calculating key indicators such as sub-industry electricity efficiency, green energy consumption ratio, and labor efficiency, and generating assessment results such as sub-industry comprehensive indices. These analysis results provide an important basis for subsequent assessments and forecasts.

[0200] Evaluation Module: This module assesses the green, advanced, and efficient performance of each sub-industry and provides corresponding corrective strategies. It also compares the electricity efficiency of high-end equipment manufacturing with that of traditional equipment manufacturing, revealing differences in technological level and energy efficiency between the two sectors. These assessment results help companies understand their position within the industry and develop appropriate improvement plans.

[0201] Forecasting Module: Based on historical data and current trends, the Forecasting Module uses machine learning and deep learning algorithms to predict and assess future power demand and productivity development across various sub-sectors. This helps businesses and governments make forward-looking decisions to address future challenges and opportunities.

[0202] In summary, this power data-based productivity analysis device integrates multiple key components to achieve comprehensive data collection, processing, analysis, evaluation, and prediction. It provides powerful data support for businesses, helping to improve productivity, optimize energy structures, increase energy efficiency, and promote sustainable development in the industry.

[0203] In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the modules is only one type. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0204] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

[0205] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A system for analyzing productivity based on power data, characterized in that: include: Sub-industry data collection module: extract monthly electricity consumption data, monthly output value, monthly consumption of various energy sources and number of employees for five years according to sub-industry classification; Data processing module: cleans and organizes the collected data, removes duplicate, erroneous, and invalid data, and uses the processed data to construct a time series sub-industry dataset; Data analysis module: used to establish a time series analysis model, and calculate the power consumption per unit output value of the j-th sub-industry based on the time series sub-industry data set, that is, the power efficiency of the j-th sub-industry Hyydxl j ; Classify the sub-industries into high-end equipment manufacturing and traditional equipment manufacturing, calculate the electricity efficiency of high-end equipment manufacturing Gdxl and the electricity efficiency of traditional equipment manufacturing Ctxl; calculate the consumption ratio of the j-th sub-industry to the y-th energy Nybl jy , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy , labor efficiency of the jth sub-industry Rgxl j ; The electricity efficiency of the j-type sub-industry is Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Associated to generate the j-th sub-industry comprehensive index Zhzs j : The data analysis module further includes a first correlation unit and a second correlation unit. The first correlation unit is used to convert the electricity efficiency of the j-th sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j After dimensionless processing, the following correlation formula is used to generate the comprehensive index Zhzs of the j-th sub-industry: j : Yessss j =γ1*Hyydxl j +γ2*Lsbl jfy +γ3*Rgxl j +A1 Where γ1, γ2 and γ3 represent the electricity efficiency of the j-th sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j The weight value is adjusted by the user, 0<γ1<1, 0<γ2<1, 0<γ3<1, γ1+γ2+γ3=1, A1 represents the first correction constant value; The second correlation unit generates the ratio of the electricity consumption efficiency of the high-end equipment manufacturing industry to the electricity consumption efficiency of the traditional equipment manufacturing industry through the following correlation formula: electricity consumption rate ratio Ydlb: Where Gdxl is the electricity consumption efficiency of high-end equipment manufacturing industry, and Ctxl is the electricity consumption efficiency of traditional equipment manufacturing industry; Evaluation module: If the comprehensive index of sub-industry category j is Zhzs j ≥ comprehensive threshold Q1, indicating that the j-th sub-industry belongs to the green, advanced and efficient industry; if the comprehensive index of the j-th sub-industry Zhzs j < comprehensive threshold Q1, indicating that the j-th sub-industry does not belong to the green, advanced and efficient industry, sends the first warning instruction, and further through the j-th sub-industry electricity efficiency Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy , labor efficiency of the jth sub-industry Rgxl j Continue to conduct in-depth assessments of issues in the jth sub-industry, compare and analyze them with corresponding thresholds, trigger corresponding early warning instructions, and generate corresponding strategies; evaluate the ratio of electricity efficiency of high-end equipment manufacturing to that of traditional equipment manufacturing: the electricity rate ratio, and analyze the differences in technology level and energy utilization efficiency between high-end equipment manufacturing and traditional equipment manufacturing; Forecasting module: Use machine learning and deep learning algorithms to predict and evaluate the future electricity demand and productivity development of each sub-industry. Before conducting the forecast and evaluation, the collected and organized historical data and current trend data of each sub-industry are input into the forecasting module for training.

2. The system for analyzing productivity based on power data according to claim 1, characterized in that: The productivity mentioned includes: new generation information technology, biotechnology, new energy, new materials, high-end equipment, new energy vehicles, green environmental protection, civil aviation, ship and marine engineering equipment, artificial intelligence, quantum information, mobile communications, the Internet of Things and blockchain.

3. The system for analyzing productivity based on power data according to claim 1, characterized in that: For the same data x obtained from different channels, if the values ​​are different, the following formula is used for standardization: In the formula, i = 1, 2, 3, ..., n, i represents the sequence number of the same data obtained in the i-th channel, n represents the total number of the same data obtained in n different channels, x i Represents the same data value captured on the i-th website, x max Indicates the maximum value of the same data obtained from n different channels, x min Indicates the minimum value of the same data obtained from n different channels.

4. The system for analyzing productivity based on power data according to claim 1, characterized in that: The data analysis module is used to establish a time series analysis model, including a first calculation unit, which is used to calculate and obtain the electricity efficiency Hyydxl of the j-th sub-industry based on the time series sub-industry data set: j , electricity consumption efficiency Gdxl of high-end equipment manufacturing industry and electricity consumption efficiency Ctxl of traditional equipment manufacturing industry; The electricity efficiency of the j-th sub-industry is Hyydxl j Calculate using the following formula: Where j represents the sub-industry number, k = 1, 2, 3, ..., m, k represents the month number of the data, m represents the total number of months for which data for the past m months were obtained, Q(j,k) represents the monthly electricity consumption of the j-th sub-industry in the k-th month, and P(j,k) represents the monthly output value of the j-th sub-industry in the k-th month. The electricity consumption efficiency Gdxl of the high-end equipment manufacturing industry is calculated using the following formula: Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, g = 1, 2, 3, ..., s, g represents the serial number of the high-end equipment manufacturing industry, s represents the total number of high-end equipment manufacturing industries, Q(g, k) represents the monthly electricity consumption of the g-th high-end equipment manufacturing industry in the k-th month, Q(g, k) represents the monthly output value of the g-th high-end equipment manufacturing industry in the k-th month, represents the total monthly electricity consumption of all s high-end equipment manufacturing industries in m months. It represents the monthly output value of all s high-end equipment manufacturing industries in a total of m months; The electricity consumption efficiency Ctxl of the traditional equipment manufacturing industry is calculated using the following formula: Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, c = 1, 2, 3, ..., s, c represents the serial number of the traditional equipment manufacturing industry, q represents the total number of traditional equipment manufacturing industries, Q(c, k) represents the monthly electricity consumption of the cth traditional equipment manufacturing industry in the kth month, and Q(c, k) represents the monthly output value of the cth traditional equipment manufacturing industry in the kth month. represents the total monthly electricity consumption of all q traditional equipment manufacturing industries in m months. It represents the monthly output value of all q traditional equipment manufacturing industries in a total of m months.

5. The system for analyzing productivity based on power data according to claim 1, characterized in that: The data analysis module further includes a second calculation unit, which is used to calculate and obtain: the consumption ratio Nybl of the yth energy in the jth sub-industry based on the time series sub-industry data set jy And the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy Because the measurement units of various energy sources are different, the consumption of various energy sources is first converted into the consumption of standard coal according to the conversion coefficients of energy types issued by the energy management department, and then the calculation is carried out; The consumption ratio of the yth type of energy in the jth sub-industry is Nybl jy Calculate using the following formula: Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, j represents the serial number of the sub-industry, y = 1, 2, 3, ..., t, y represents the serial number of different types of energy, t represents the total number of energy types, N(j, k, y) represents the consumption of the yth type of energy in the kth month of the jth sub-industry, It represents the consumption of the yth type of energy in the jth sub-industry within m months. It represents the consumption of all t types of energy in the j-th sub-industry in m months; The consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy Calculate using the following formula: Where, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, j represents the serial number of the sub-industry, y = 1, 2, 3, ..., t, y represents the serial number of different types of energy, t represents the total number of energy types, f = 1, 2, 3, ..., u, f represents the serial number of different types of green energy, u represents the total number of green energy types, N(j, k, f) represents the f-th type of green energy consumption obtained in the k-th month of the j-th sub-industry, and N(j, k, y) represents the y-th type of energy consumption obtained in the k-th month of the j-th sub-industry. It represents the consumption of all u types of green energy in the j-th sub-industry in m months. It represents the consumption of all t types of energy in the j-th sub-industry within m months.

6. The system for analyzing productivity based on power data according to claim 1, characterized in that: The data analysis module also includes a third calculation unit, which is used to calculate the labor efficiency Rgxl of the j-th sub-industry based on the time series sub-industry data set. j , the labor efficiency of the j-th sub-industry Rgxl j Calculate using the following formula: In the formula, k = 1, 2, 3, ..., m, k represents the serial number of the month for which data was obtained, m represents the total number of months for which data for the past m months was obtained, Q(j,k) represents the monthly output value of the jth sub-industry in the kth month, It represents the total output value of the j-th sub-industry for m months, and Rs(j,k) represents the number of employees in the j-th sub-industry in the k-th month.

7. The system for analyzing productivity based on power data according to claim 1, characterized in that: The evaluation module includes a first evaluation unit, a second evaluation unit and a third evaluation unit; The first evaluation unit is used to compare and evaluate the ratio of the power consumption efficiency of the high-end equipment manufacturing industry to the power consumption efficiency of the traditional equipment manufacturing industry: the power consumption rate ratio Ydlb and the power consumption rate ratio threshold Q1, and obtain a first evaluation result, including: If the electricity consumption ratio Ydlb>Q1, it means that the electricity consumption efficiency control of the high-end equipment manufacturing industry is far ahead of that of the traditional equipment manufacturing industry. It is worth learning from the advanced achievements of the high-end equipment manufacturing industry to upgrade and transform the traditional equipment manufacturing industry. If 1 < electricity consumption ratio Ydlb ≤ Q1, it means that the electricity efficiency control of the high-end equipment manufacturing industry is ahead of that of the traditional equipment manufacturing industry. The electricity efficiency control is not obvious. It is necessary to analyze the reasons, ensure R&D investment, and further upgrade and transform the high-end equipment manufacturing industry. If the electricity consumption ratio Ydlb ≤ 1, it means that the electricity efficiency of the high-end equipment manufacturing industry is lower than that of the traditional equipment manufacturing industry. On-site investigation is needed to find out the cause, replace advanced equipment, and further transform and upgrade the high-end equipment manufacturing industry. The second evaluation unit is used to calculate the j-th sub-industry comprehensive index Zhzs j Compare and evaluate with the comprehensive threshold Q2 to obtain a second evaluation result, including: If the comprehensive index of sub-industry of category j is Zhzs j ≥ comprehensive threshold Q2, indicating that the j-th sub-industry belongs to the green, advanced and efficient industry; if the j-th sub-industry comprehensive index Zhzs j <Comprehensive threshold Q2 indicates that the j-th sub-industry does not belong to the green, advanced and efficient industry, and sends the first warning instruction to remind the j-th sub-industry that it needs to be transformed and upgraded. At the same time, the third evaluation unit is triggered through the j-th sub-industry electricity efficiency Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Continue to conduct in-depth assessments of sub-industry issues in category j and obtain the third assessment results, including: If the electricity efficiency of the j-th sub-industry is Hyydxl j ≥ the power efficiency threshold Q3, indicating that the j-th sub-industry has high power efficiency and belongs to the advanced industry. If the power efficiency of the j-th sub-industry is Hyydxl j The power efficiency threshold Q3 indicates that the j-th sub-industry is inefficient in power consumption and is classified as a backward industry. A second warning instruction is issued and a strategy is generated: the power facilities of the j-th sub-industry are upgraded to ensure efficient power consumption. If the consumption ratio of green energy of type f in the j-th sub-industry is Lsbl jfy ≥ green energy threshold Q4, indicating that the j-th sub-industry belongs to the green environmental protection industry. If the consumption ratio of the f-th type of green energy in the j-th sub-industry is Lsbl jfy Green energy threshold Q4 indicates that the j-th sub-industry relies on traditional fossil energy and does not belong to the green environmental protection industry. The third warning instruction is issued and a strategy is generated: upgrade and transform the j-th sub-industry to rely on green energy for production; If the labor efficiency of the j-th sub-industry is Rgxl j ≥ the labor efficiency threshold Q5, indicating that the automation level of the j-th sub-industry is excellent and it belongs to the high-efficiency industry. If the labor efficiency of the j-th sub-industry Rgxl j <The labor efficiency threshold Q5 indicates that the automation level of the j-th sub-industry is unqualified and it is an inefficient industry. The fourth early warning instruction is issued and a strategy is generated: the automation level of the j-th sub-industry is upgraded and transformed to achieve reduction in staff and increase in efficiency.

8. A method for analyzing productivity based on power data, applied to a system for analyzing productivity based on power data according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Industry data collection: Use data crawler technology to collect data from official websites and commercial data analysis websites, and extract monthly electricity consumption data, monthly output value, monthly consumption of various energy sources, and number of employees for each month over the past five years according to sub-industry classification; Step 2: Data processing: Clean and organize the collected data, remove duplicate, erroneous, and invalid data to ensure data accuracy and completeness, and use the processed data to construct a time series sub-industry dataset; Step 3: Data analysis: Use the pre-established time series analysis model to conduct in-depth analysis of power data and enterprise data to explore the correlation and trend information; calculate the electricity efficiency of the j-th sub-industry based on the time series sub-industry data set Hyydxl j , obtain the electricity consumption efficiency Gdxl of high-end equipment manufacturing industry, the electricity consumption efficiency Ctxl of traditional equipment manufacturing industry, and the consumption ratio Nybl of the j-type sub-industry for the y-type energy jy , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j ; The electricity efficiency of the j-type sub-industry is Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Associated to generate the j-th sub-industry comprehensive index Zhzs j , the electricity consumption efficiency Gdxl of high-end equipment manufacturing industry and the electricity consumption efficiency Ctxl of traditional equipment manufacturing industry are correlated to generate the electricity consumption rate ratio Ydlb; Step 4: Data evaluation: The first evaluation unit evaluates the ratio of the electricity efficiency of the high-end equipment manufacturing industry to the electricity efficiency of the traditional equipment manufacturing industry: the electricity rate ratio Ydlb; The second evaluation unit evaluates the comprehensive index of the j-th sub-industry Zhzs j The third evaluation unit is evaluated through the electricity efficiency of the j-type sub-industry Hyydxl j , the consumption ratio of the fth type of green energy in the jth sub-industry Lsbl jfy and the labor efficiency of the j-th sub-industry Rgxl j Continue to conduct in-depth assessments of the j-type sub-industry issues and obtain corresponding assessment results and corresponding strategies; Step 5: Trend Forecasting: Based on historical data and current trends, machine learning and deep learning algorithms are used to forecast and assess future electricity demand and productivity development in each sub-sector. Step 6. Organize the analysis and evaluation results into a report, put forward specific suggestions and improvement measures, and predict the future development trends of each sub-industry.

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