Method and system for evaluating production and operation conditions of enterprise by using night light data

Abstract

The invention provides a method and system for evaluating enterprise production and operation conditions by using night light data, and relates to the field of spatial information technology application. The method is based on a remote sensing satellite image, combines a geographic spatial information processing technology, and utilizes the advantages of high timeliness, objectivity, accuracy and the like of a night light satellite image to evaluate enterprise production and operation conditions. According to the method, the weekly and quarterly night light indexes of a target enterprise are constructed, the night light indexes combine satellite big data with economic indexes, and the influence of cloud, background noise, abnormal values and seasonal factors on night production activities of the enterprise is fully considered, so that the production and operation activities of the enterprise can be evaluated more accurately and comprehensively, and the enterprise production and operation efficiency is improved. And enterprise managers, investors and the like are helped to predict enterprise production and operation conditions in a period of time in the future.

Description

Technical Field

[0001] This invention relates to the field of spatial information technology applications, specifically a method and system for assessing the production and operation status of enterprises using nighttime light data. Background Technology

[0002] With the development of technology, satellite remote sensing has become an important means of acquiring information about the Earth's surface. Among these, nighttime light satellite data, due to its unique advantages, has been widely applied in numerous fields. Nighttime light satellite data can reflect the intensity of light on the ground, thus indirectly reflecting human economic activities. For example, by analyzing nighttime light satellite data, it is possible to monitor urbanization processes, estimate population and GDP, and even predict the spread of diseases.

[0003] However, despite the wide application of nighttime light satellite data in many fields, there is still no mature method or system for using this data to evaluate a company's production and operation activities. Currently, companies typically assess their production and operation activities through internal financial reports, sales data, and other information. However, this information often has a certain lag and cannot reflect the company's production and operation status in real time. In addition, this information may also be affected by various internal factors, such as financial fraud and data tampering, thus affecting the accuracy of the assessment results.

[0004] Therefore, in order to solve the above-mentioned technical problems, this application proposes a method and system for evaluating the production and operation status of enterprises using nighttime light data. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for evaluating enterprise production and operation activities using nighttime light data. The aim is to evaluate enterprise production and operation activities in real time and accurately, so that enterprise decision-makers, investors and other stakeholders can make more scientific and rational decisions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method and system for evaluating the production and operation status of enterprises using nighttime light data, comprising;

[0007] The data collection module is used to periodically collect nighttime light satellite images, cloud cover images, and target enterprise boundary vector information;

[0008] The data preprocessing module is used to remove background noise, outliers, and pixels obscured by clouds from nighttime light satellite images.

[0009] The feature extraction module is used to cut the preprocessed satellite image according to the regional boundary contour of the target enterprise to obtain the nighttime light features of the corresponding area of ​​the target enterprise;

[0010] The evaluation model and output module use the company’s weekly and quarterly nighttime light indicators to evaluate the target company’s production and operation activities and output the evaluation results in the form of charts or reports.

[0011] The memory and processor are used to store software programs and various modules, and the processor is used to implement signal processing between modules.

[0012] A method for assessing the production and operation status of enterprises using nighttime light data, applied to the aforementioned system for assessing the production and operation status of enterprises using nighttime light data, includes the following steps in the specific assessment method:

[0013] A: By connecting the data collection module to interfaces with satellite data providers and the enterprise's internal data platform, nighttime light satellite images, satellite cloud imagery, and target enterprise boundary vector data are collected regularly.

[0014] B: The data processing module preprocesses the original nighttime light satellite image, including removing background noise and correcting abnormal pixels, and synthesizes the corrected nighttime light satellite image and satellite cloud image, and uses cloud masking to remove pixels obscured by clouds.

[0015] C: Based on the boundary contour vector data of the target enterprise, batch crop the synthesized and corrected nighttime light satellite images to extract the nighttime light features corresponding to the target enterprise;

[0016] D: Calculate the quarterly nighttime light index using the nighttime light characteristics of the target enterprise, and analyze and evaluate the production and operation status of the target enterprise through the evaluation model and output module;

[0017] It should be noted that the specific operation method for preprocessing satellite images using the data collection module in step B includes the following steps:

[0018] B1: Acquire satellite images through the data collection module and extract pixel values ​​in the nighttime visible light band from them;

[0019] B2: For some low-radiation and negative value pixels, background noise is removed based on the principle that the value is 0 in areas without light.

[0020] B3: Local disturbances caused by isolated extremely bright pixels are corrected using methods such as median filtering / fixed threshold segmentation;

[0021] B4: Using cloud-covered images as a mask, pixels obscured by clouds are removed to obtain a synthetic corrected nighttime light satellite image under cloudless conditions;

[0022] It is worth noting that step C, which involves extracting the nighttime lighting features of the enterprise, includes the following steps:

[0023] C1: Obtain the sum of the number of pixels covering the boundary of the target enterprise area and the radiation value of the target enterprise pixels, and define it as the sum of the nighttime light brightness;

[0024] C2: Calculate the quotient of the total pixel radiance of the target enterprise divided by the total number of pixels, and define it as the average nighttime light intensity:

[0025] C3: Obtain the number of pixels and radiation value within a 5-kilometer radius of the target enterprise (excluding the area where the target enterprise is located), and calculate the ratio of the total pixel radiation value to the total number of pixels, defining it as the ambient nighttime light intensity.

[0026] The specific method for evaluating the production and operation status of enterprises through the evaluation model and output module is as follows:

[0027] D1: Calculate the average nighttime light intensity of the target company and the mean of the nighttime light intensity of the surrounding area each week, and take the logarithm of each to obtain the nighttime light index of the target company for each week. Based on the mean of the average nighttime light intensity of the target company in a single month, obtain the nighttime light intensity for the corresponding month.

[0028] D2: Subtract the monthly nighttime light intensity of the same period last year from the monthly nighttime light intensity of the target company in the current year, and divide by the monthly nighttime light intensity of the same period last year to obtain the year-on-year change in the monthly nighttime light intensity of the target company. Then calculate the average of the year-on-year change in monthly nighttime light intensity within a single quarter to obtain the nighttime light index of the target company for each quarter.

[0029] D3: Analyze the causal relationship between nighttime lighting intensity and the target company's production and operation activities based on the company's nighttime lighting index data during a specific time period and the target company's operating conditions during that time period, and associate the company's nighttime lighting intensity with the company's production and operation.

[0030] D4: By examining the correlation between a company's nighttime lighting brightness and its production and operations, a fixed-effects model is used to predict the target company's sales revenue growth rate, production cost growth rate, and operating profit growth rate for the next quarter, and the prediction results are obtained. At the same time, the Fama-MacBeth model is used to predict the target company's monthly stock return for the next quarter, and the prediction results are obtained.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] This invention utilizes remote sensing satellites to detect and receive nighttime light radiation and reflections from the ground, identifying the economic and social activities and spatial distribution characteristics of the target enterprise's location. Leveraging the high timeliness, objectivity, and accuracy of this data, a quarterly nighttime light index for the target enterprise is constructed. This index combines satellite big data with economic indicators, fully considering the impact of outliers, background noise, and cloud cover. This allows for a more accurate and comprehensive analysis and assessment of the target enterprise's production and operation, helping enterprise managers and investors enhance their sensitivity to market changes and providing strong support for business strategies and investment decisions. Attached Figure Description

[0033] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0034] Figure 1 This is a flowchart of a system for evaluating the production and operation status of an enterprise using nighttime light data, according to the present invention.

[0035] Figure 2 This is a structural block diagram of a system for evaluating the production and operation status of an enterprise using nighttime light data, according to the present invention.

[0036] Figure 3 A schematic diagram of the regional boundary of a target enterprise in a system for evaluating the production and operation status of an enterprise using nighttime light data, provided by the present invention;

[0037] Figure 4 This invention provides a system for evaluating the operational status of enterprises using nighttime light data, including a vector map of the target enterprise's regional boundaries.

[0038] Figure 5 A schematic diagram of satellite image pixels covering a target enterprise in a system for assessing enterprise operations using nighttime light data provided by the present invention;

[0039] Figure 6 This invention provides a schematic diagram illustrating the dynamic changes in the average nighttime light brightness of a target enterprise over three weeks before and after the implementation of closed-loop management measures in a system for assessing enterprise operations using nighttime light data. Detailed Implementation

[0040] Example

[0041] like Figures 1-6 As shown, the present invention provides a method and system for evaluating the production and operation status of enterprises using nighttime light data, including:

[0042] The data collection module is used to periodically collect nighttime light satellite images, cloud cover images, and target enterprise boundary vector information;

[0043] The data preprocessing module is used to remove background noise, outliers, and pixels obscured by clouds from nighttime light satellite images.

[0044] The feature extraction module is used to cut the preprocessed satellite image according to the regional boundary contour of the target enterprise to obtain the nighttime light features of the corresponding area of ​​the target enterprise;

[0045] The evaluation model and output module use the company’s weekly and quarterly nighttime light indicators to evaluate the target company’s production and operation activities and output the evaluation results in the form of charts or reports.

[0046] The memory and processor are used to store software programs and various modules. The processor is used to implement signal processing between modules. The memory includes, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), and Electrically Erasable Programmable Read-Only Memory (EEPROM). The processor is an integrated circuit chip, which can be a Central Processing Unit (CPU), a Network Processor (NP), etc. Specific models are not described here.

[0047] It should be noted that the present invention also provides a method for assessing the production and operation status of enterprises using nighttime light data, applied to the aforementioned system for assessing the production and operation status of enterprises using nighttime light data. The specific assessment method includes the following steps:

[0048] A: By connecting the data collection module to the interfaces of satellite data providers and the enterprise's internal data platform, nighttime light satellite images, satellite cloud image data, and target enterprise boundary contour vector data are collected regularly.

[0049] The nighttime light satellite images and cloud cover images can be obtained from platforms such as the China Center for Resources Satellite Data and Application, the International Research Center for Big Data for Sustainable Development, and NASA's Earth Observation Group (EOG). In one embodiment, VIIRS nighttime light images containing the target company from the NPP satellite can be obtained from the EOG website, along with the pixel values ​​in the Day / Night Band. The target company's outline vector data can be obtained from an internal geographic information system. This geographic information system includes the regional boundary outline vector data of the target company's parent company and all its subsidiaries. This system can directly perform location searches and regional boundary queries based on company name and address from common map open platforms.

[0050] It should be noted that the Day / Night Band is a unique band of NPP satellites. Different types of satellites acquire different types of bands, which will not be elaborated here.

[0051] B: The original nighttime light satellite image is preprocessed through the data processing module, including background noise removal and abnormal pixel correction. The corrected nighttime light satellite image is synthesized with the satellite cloud image. Pixels obscured by clouds are removed using cloud masking. Background noise includes, but is not limited to, some pixel values ​​are negative or have low radiation values ​​due to local disturbances caused by transient light sources such as fires, lightning, fishing boat lights, and weak light reflected from rivers and lakes. Abnormal pixels are characterized by disordered fluctuations in pixel radiation values ​​in images of adjacent areas or adjacent times.

[0052] C: Using the ENVI remote sensing processing platform, combined with the regional boundary contour vector information of the target company's parent company and all its subsidiaries, the exported synthetic corrected nighttime light image is cropped and segmented to obtain the nighttime light images of the corresponding areas of the target company's parent company and all its subsidiaries.

[0053] D: Calculate the quarterly nighttime light index using the nighttime light characteristics of the target enterprise, and analyze and evaluate the production and operation status of the target enterprise through the evaluation model and output module;

[0054] It should be noted that the specific operation method for preprocessing satellite images using the data acquisition module in step B includes the following steps:

[0055] B1: Acquire satellite images through the data collection module and extract pixel values ​​in the nighttime visible light band from them;

[0056] B2: For some low-radiation and negative value pixels, background noise is removed based on the principle that the value is 0 in areas without light.

[0057] B3: Local disturbances caused by isolated extremely bright pixels are corrected using methods such as median filtering / fixed threshold segmentation;

[0058] B4: Using cloud-covered images as a mask, pixels obscured by clouds are removed to obtain a synthetic corrected nighttime light satellite image under cloudless conditions;

[0059] In one implementation scheme, to maximize the effectiveness of data preprocessing, sampling points can be selected from large bodies of water such as lakes and reservoirs within the administrative region where the target enterprise is located. The corresponding geographical locations are then located in the corresponding nighttime light satellite images, and the pixel values ​​at those locations are recorded. The average pixel value is calculated and used as the minimum light threshold. Pixels with values ​​less than this threshold are assigned a value of 0, while all pixels with values ​​greater than this threshold are retained, thereby achieving the goal of extracting effective nighttime lights and removing background noise.

[0060] To remove isolated, extremely bright pixels from nighttime light data, fixed threshold segmentation or median filtering can be used to dynamically eliminate abnormal pixels. For example, the maximum radiance value of pixels in the central urban area within the administrative region where the target enterprise is located can be calculated and used as a threshold for all nighttime light pixels in the area. Pixels with radiance values ​​exceeding this threshold are assigned a lower threshold. Otherwise, the original radiation value shall be used;

[0061] A cloud mask is generated using satellite cloud images. Grids with clouds are assigned a value of 1, and grids without clouds are assigned a value of 0. The cloud mask is then combined with the corrected nighttime light satellite image, and the light values ​​on the grids with a mask value of 0 are removed.

[0062] Step C, which involves extracting the nighttime lighting features of the enterprise, includes the following specific steps:

[0063] C1: Obtain the number of pixels covering the boundary of the target enterprise's area (defined as the number of pixels overlapping with the area where the target enterprise is located) and the sum of the radiation values ​​of the target enterprise's pixels (defined as the sum of nighttime light brightness).

[0064] C2: Calculate the quotient of the total pixel radiance of the target enterprise divided by the total number of pixels, and define it as the average nighttime light intensity:

[0065] C3: Obtain the number of pixels and radiation value within a 5-kilometer radius of the target enterprise (excluding the area where the target enterprise is located), and calculate the ratio of the total pixel radiation value to the total number of pixels, defining it as the ambient nighttime light intensity.

[0066] The specific method for evaluating the production and operation status of enterprises through the evaluation model and output module is as follows:

[0067] D1: Calculate the average nighttime light intensity of the target company and the mean of the nighttime light intensity of the surrounding area each week, and take the logarithm of each to obtain the nighttime light index of the target company for each week. Based on the mean of the average nighttime light intensity of the target company in a single month, obtain the nighttime light intensity for the corresponding month.

[0068] D2: Subtract the monthly nighttime light intensity of the same period last year from the monthly nighttime light intensity of the target company in the current year, and divide by the monthly nighttime light intensity of the same period last year to obtain the year-on-year change in the monthly nighttime light intensity of the target company. Then calculate the average of the year-on-year change in monthly nighttime light intensity within a single quarter to obtain the nighttime light index of the target company for each quarter.

[0069] D3: Based on the nighttime lighting data of enterprises during a specific time period and the operating conditions of the target enterprises, analyze the causal relationship between nighttime lighting brightness and enterprise production and operation activities to establish the correlation between the two. The specific time period includes the government control period during the epidemic, which is defined as the period of implementation of closed management. During this period, compare the enterprise nighttime lighting indicators with those under normal conditions. Given the fact that enterprises suspended operations during the closed management period, observe whether there was a significant decrease in nighttime lighting brightness after the implementation of closed management, thereby exploring the correlation between enterprise nighttime lighting data and production and operation data. Detailed research steps include:

[0070] Step (1): Obtain the closed-loop management measures implemented by various cities across the country in early 2020 to control the spread of the epidemic. These measures restricted population movement and caused businesses to shut down. Next, the branch offices of the target companies were divided into processing and control groups based on their registered business addresses. The cities where the companies in the processing group were located implemented closed-loop management measures, while the cities where the companies in the control group were located did not.

[0071] Step (2): Examine the changes in nighttime light brightness in each branch office using the following difference-in-differences model:

[0072] (1)

[0073] (2)

[0074] Formula (1) is used to examine whether the closed-loop management measures result in lower nighttime light brightness in the treatment group company compared to the control group company, while formula (2) is used to examine the dynamic changes in this relationship. In the above formulas... For branch offices Weekly nighttime light brightness, These are dummy variables representing the treatment group. It is a dummy variable representing the effect of policy implementation. It is the first time before the implementation of the characterization policy Zhou's dummy variables ( =1, 2, 3), It is a dummy variable representing the week in which the policy is implemented. It is the result of the implementation of the characterization policy. Zhou's dummy variables ( =1, 2, 3), These are the control variables for branch offices, including the surrounding nighttime light intensity and the number of daily nighttime light data points per week. For branch offices Individual fixed effects are used to control for the influence of all time-invariant factors at the branch level. This is a weekly point-in-time fixed effect, used to control for the impact of common factors faced by all branches at each weekly point in time. The residuals are shown in Table 1, which analyzes the impact of special circumstances (during government control measures during the pandemic) on the weekly nighttime light brightness of the target enterprise's branches using a difference-in-differences model, and shows the changes in nighttime light brightness of affected enterprises relative to unaffected enterprises in the three weeks before and after the implementation of closed management measures.

[0075] Table 1. Changes in weekly nighttime lighting indicators of branch offices during closed management period

[0076]

[0077] D4: By examining the correlation between the company's nighttime lighting brightness and its production and operations, a fixed-effects model is used to predict the target company's sales revenue growth rate, production cost growth rate, and operating profit growth rate for the next quarter, and the prediction results are obtained. At the same time, the Fama-MacBeth model is used to predict the target company's monthly stock return for the next quarter, and the prediction results are obtained.

[0078] It should be noted that when using a fixed-effects model to predict the target company's sales revenue growth rate, production cost growth rate, and operating profit growth rate for the next quarter, the target company's... A series of control variables for the current quarter, including the target company. The cumulative stock return over the 12 months to 1 month prior to the end of the quarter ( ), Enterprise size ( Book value to market value ratio ( ), leverage ratio ( ), net asset growth rate ( ), profit margin ( ) and number of branches ( Then calculate the target company using the following formula. Changes in fundamentals for the next quarter:

[0079] (3)

[0080] in It is one of the following: sales growth rate, cost growth rate, or profit growth rate for the next quarter. For target companies Current season's nighttime light index For target companies A series of control variables for the current quarter, To control the number of variables, For target companies Individual fixed effects are used to control for the impact of all time-invariant factors at the firm level. This is a quarterly fixed-point effect, used to control for the impact of common factors faced by all firms at each quarterly point in time. The residuals are shown in Table 2, where the relationship between the target company's quarterly nighttime lighting index and the sales growth rate, cost growth rate, and profit growth rate in the following quarter, obtained through fixed effects model analysis, is as follows:

[0081] Table 2. Relationship between Enterprise Quarterly Nighttime Lighting Indicators and Changes in Enterprise Fundamentals

[0082]

[0083] The method for predicting stock returns is as follows: [Identify the target company / entity ... A series of stock return predictors for the current quarter, including market risk coefficients ( ), market capitalization factor ( ), momentum factor ( ), inversion factor ( Profitability Factor ), investment factors ( Heterogeneous volatility factor ( ) and liquidity factor ( ).

[0084] Then, calculate the target company's monthly stock returns for the next quarter using the following formula ( ):

[0085] (4)

[0086] in For target companies Current season's nighttime light index For target companies A series of stock return predictors for the current quarter, The number of predictors. For the residual term, For the next quarter Table 3 shows the relationship between the target company's quarterly thermal radiation index and the monthly stock returns of the following quarter, obtained through the Fama-MacBeth model analysis described above.

[0087] Table 3. Corporate Quarterly Nighttime Lighting Indicators and Corporate Stock Returns

[0088]

[0089] In one different embodiment, for a target company with multiple subsidiaries, the daily nighttime light characteristics of all its subsidiaries are obtained based on NPP-VIIRS nighttime light satellite imagery.

[0090] In order to address the problem that high-frequency nighttime light data fluctuates greatly and makes it difficult to capture the stable production and operation activities of target enterprises, it is necessary to calculate the average value of nighttime light brightness on a weekly or monthly basis, so as to more accurately and timely assess the production and operation status of enterprises.

[0091] In the calculation, the average daily nighttime light intensity of the subsidiary companies is first calculated. and the brightness of surrounding nighttime lights Next, the average of the date data is calculated based on the week number of the date to obtain the average nighttime light intensity for each subsidiary. and the brightness of surrounding nighttime lights ;

[0092] Then, when calculating the target company's quarterly nighttime lighting index, the monthly nighttime lighting data is first grouped into monthly periods according to the month in which the date falls. The average of all available daily data within that month is then calculated to obtain the average monthly nighttime lighting brightness for each subsidiary. Next, the monthly nighttime lighting brightness of each subsidiary is summed to obtain the target company's monthly nighttime lighting brightness, and the year-on-year change in the target company's monthly nighttime lighting brightness is calculated using the following formula: ,in The number of subsidiaries;

[0093] Finally, according to the formula The quarterly nighttime light index of the target company was calculated, among which The data represents the year-on-year change in the monthly nighttime light intensity of the target company, and these months belong to the same quarter.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A system for assessing the production and operation status of enterprises using nighttime light data, characterized in that, Also includes; The data collection module is used to periodically collect nighttime light satellite images, cloud cover images, and target enterprise boundary vector information; The data preprocessing module is used to remove background noise, outliers, and pixels obscured by clouds from nighttime light satellite images. The feature extraction module is used to cut the preprocessed satellite image according to the regional boundary contour of the target enterprise to obtain the nighttime light features of the corresponding area of ​​the target enterprise; The evaluation model and output module use the company’s weekly and quarterly nighttime light indicators to evaluate the target company’s production and operation activities and output the evaluation results in the form of charts or reports. The memory and processor are used to store software programs and various modules, and the processor is used to implement signal processing between modules.

2. A method for assessing the production and operation status of enterprises using nighttime light data, applied to the aforementioned system for assessing the production and operation status of enterprises using nighttime light data, characterized in that: The specific evaluation method includes the following steps: A: By connecting the data collection module to the interfaces of satellite data providers and the enterprise's internal data platform, nighttime light satellite images, satellite cloud images, and target enterprise boundary vector data are collected regularly. B: The data processing module preprocesses the original nighttime light satellite image, including removing background noise and correcting abnormal pixels, and synthesizes the corrected nighttime light satellite image and satellite cloud image, and uses cloud masking to remove pixels obscured by clouds. C: Based on the boundary contour vector data of the target enterprise, batch crop the synthesized and corrected nighttime light satellite images to extract the nighttime light features corresponding to the target enterprise; D: Calculate the quarterly nighttime light index using the target company's nighttime light characteristics, and analyze and evaluate the target company's production and operation status through the evaluation model and output module.

3. The method for evaluating enterprise production and operation status using nighttime light data according to claim 2, characterized in that, Step B involves the following steps for preprocessing satellite images using the data collection module: B1: Acquire satellite images through the data collection module and extract pixel values ​​in the nighttime visible light band from them; B2: For some low-radiation and negative value pixels, background noise is removed based on the principle that the value is 0 in areas without light. B3: Local disturbances caused by isolated extremely bright pixels are corrected using methods such as median filtering / fixed threshold segmentation; B4: Using cloud-covered images as a mask, pixels obscured by clouds are removed to obtain a synthetic corrected nighttime light satellite image under cloudless conditions.

4. The method for evaluating the production and operation status of an enterprise using nighttime light data according to claim 3, characterized in that, Step C, which involves extracting the nighttime lighting features of the enterprise, includes the following steps: C1: Obtain the sum of the number of pixels covering the boundary of the target enterprise area and the radiation value of the target enterprise pixels, and define it as the sum of the nighttime light brightness; C2: Calculate the quotient of the total pixel radiance of the target enterprise divided by the total number of pixels, and define it as the average nighttime light intensity: C3: Obtain the number of pixels and radiation value within a 5-kilometer radius of the target enterprise (excluding the area where the target enterprise is located), and calculate the ratio of the total pixel radiation value to the total number of pixels, defining it as the ambient nighttime light intensity.

5. The method for evaluating the production and operation status of an enterprise using nighttime light data according to claim 4, characterized in that, Step D, which involves analyzing and evaluating the target company's production and operation status using an evaluation model and output module, consists of the following steps: D1: Calculate the average nighttime light intensity of the target company and the mean of the nighttime light intensity of the surrounding area each week, and take the logarithm of each to obtain the nighttime light index of the target company for each week. Based on the mean of the average nighttime light intensity of the target company in a single month, obtain the nighttime light intensity for the corresponding month. D2: Subtract the monthly nighttime light intensity of the same period last year from the monthly nighttime light intensity of the target company in the current year, and divide by the monthly nighttime light intensity of the same period last year to obtain the year-on-year change in the monthly nighttime light intensity of the target company. Then calculate the average of the year-on-year change in monthly nighttime light intensity within a single quarter to obtain the nighttime light index of the target company for each quarter. D3: Analyze the causal relationship between nighttime lighting intensity and the target company's production and operation activities based on the company's nighttime lighting index data during a specific time period and the target company's operating conditions during that time period, and associate the company's nighttime lighting intensity with the company's production and operation. D4: By examining the correlation between a company's nighttime lighting brightness and its production and operations, a fixed-effects model is used to predict the target company's sales revenue growth rate, production cost growth rate, and operating profit growth rate for the next quarter, and the prediction results are obtained. At the same time, the Fama-MacBeth model is used to predict the target company's monthly stock return for the next quarter, and the prediction results are obtained.

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