Temperature-based natural gas user analysis method and system

Abstract

The invention relates to a temperature-based natural gas user analysis method. The temperature-based natural gas user analysis method comprises the following steps: acquiring historical consumption data of natural gas of a user and corresponding temperature data, and establishing a temperature-consumption relation curve; performing correlation regression analysis on the temperature-consumption relation curve to obtain a temperature-consumption correlation coefficient; the user is classified based on the temperature-consumption correlation coefficient. According to the method, the data points are established for the natural gas daily consumption data and the temperature data of the users, the correlation regression analysis is carried out, the temperature-consumption correlation coefficient corresponding to the preset time period of each historical year is obtained, demand side classification is carried out according to the temperature-consumption correlation coefficient, refined user classification is realized, and the user experience is improved. The problem of inaccurate user classification in related technologies can be solved.

Description

Technical Field

[0001] The invention belongs to the technical field of natural gas demand prediction, and in particular relates to a method for analyzing natural gas users based on temperature. Background Art

[0002] According to forecasts from several energy research institutions, China’s natural gas consumption will exceed 7000×10 8 m 3 (billion cubic meters), accounting for about 17% of the total primary energy consumption, and has become one of the main energy sources. Due to the wide variety of users in the natural gas market, from the actual scenario, the regional differences and gas usage differences of the group company's direct natural gas customers are very large. At present, only the industry attributes are used to preliminarily classify natural gas customers, such as city gas, industrial fuel, chemical industry, fertilizer, LNG, etc., and no further subdivision is made according to the gas usage characteristics.

[0003] The existing technology involves energy user classification schemes, which mainly rely on two forms or methods to achieve user classification, both of which mainly involve clustering algorithms.

[0004] One is to establish an evaluation index system for energy customers by determining the factors that affect the value of comprehensive energy customers, and use algorithms to cluster customers based on the evaluation index system.

[0005] The other method is to collect the customer data of natural gas customers; cluster the customer data using cluster analysis, and divide the natural gas customers into multiple groups of different customers. This includes: feature classification of the customer data, where the feature classification categories include at least basic customer characteristics, recent gas usage patterns, seasonal preferences, planned gas usage, historical trend changes, reduction implementation, financial credit indicators, and customer status. This involves multiple reference indicators for the same customer.

[0006] Currently, natural gas customers are mostly classified simply according to industry attributes, lacking a scientific user analysis and classification method. Although some use clustering algorithms, the solutions are relatively complex and require the extraction of multiple influencing factors and clustering processing. They also require the collection of more indicator parameters, which is a huge workload.

[0007] Therefore, it is necessary to propose a method that can quickly classify users so as to guide natural gas sales companies to formulate similar marketing and sales plans for the same type of customers in order to achieve more accurate marketing and sales. Summary of the invention

[0008] The purpose of the present invention is to provide a method for analyzing natural gas users based on temperature in order to solve the above problems.

[0009] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0010] A method for natural gas user analysis based on temperature, comprising the following steps:

[0011] Obtain the user's historical natural gas consumption data and corresponding temperature data and establish a temperature-consumption relationship curve;

[0012] Performing correlation regression analysis on the temperature-consumption relationship curve to obtain a temperature-consumption correlation coefficient;

[0013] The users are classified based on the temperature-consumption correlation coefficient.

[0014] As a further optimization solution of the present invention, the historical consumption data includes the daily consumption data of the user within a preset time period of multiple historical years.

[0015] As a further optimization solution of the present invention, the preset time period is at least two months.

[0016] As a further optimization scheme of the present invention, the specific process of obtaining the historical natural gas consumption data and the corresponding temperature data of the user and establishing the temperature-consumption relationship curve is as follows:

[0017] Obtain the daily consumption data and temperature data of corresponding dates within a preset time period of multiple historical years;

[0018] Establishing a coordinate system, taking the daily consumption data and the temperature data of the corresponding date as two parameters of a two-dimensional data set, the temperature data as the abscissa of the temperature-consumption data point, and the daily consumption data as the ordinate of the temperature-consumption data point;

[0019] The daily consumption data and the temperature data of the corresponding dates within a preset time period of multiple historical years are displayed in the coordinate system to obtain a temperature-consumption relationship curve.

[0020] As a further optimization scheme of the present invention, the correlation regression analysis includes Pearson correlation regression analysis.

[0021] As a further optimization scheme of the present invention, the temperature-consumption relationship curve is subjected to correlation regression analysis to obtain the temperature-consumption correlation coefficient in the following specific process:

[0022] The temperature data in the temperature-consumption relationship curve within the preset time period of each historical year of the user is taken as the independent variable, and the corresponding consumption data is taken as the dependent variable. The correlation coefficient between the temperature data and the consumption data is calculated using the correlation regression analysis method, and the correlation coefficient is used as the temperature-consumption correlation coefficient corresponding to the preset time period of each historical year.

[0023] As a further optimization scheme of the present invention, the specific process of classifying the users based on the temperature-consumption correlation coefficient is as follows:

[0024] Presetting a plurality of user categories and adding a numerical range to each of the user categories;

[0025] Determine the numerical range of the degree-consumption correlation coefficient of the user;

[0026] The user is classified into a user category corresponding to the numerical range.

[0027] As a further optimization solution of the present invention, the specific process of presetting multiple user categories and adding a numerical range to each of the user categories is as follows:

[0028] The user categories are divided into highly temperature-sensitive users, temperature-sensitive users, and temperature-insensitive users;

[0029] The preset numerical range for the highly temperature-sensitive user is (X1, X2), including X1 and X2; the preset numerical range for the temperature-sensitive user is (X2, X3); the preset numerical range for the temperature-insensitive user is (X3, X4), including X3 and X4.

[0030] A temperature-based natural gas user analysis system, comprising:

[0031] A data acquisition module is used to obtain the user's historical natural gas consumption data and corresponding temperature data;

[0032] A construction module for establishing a temperature-consumption relationship curve based on the user's historical natural gas consumption data and corresponding temperature data;

[0033] An analysis module, used for performing correlation regression analysis on the temperature-consumption relationship curve to obtain a temperature-consumption correlation coefficient;

[0034] A classification module is used to classify the user based on the temperature-consumption correlation coefficient.

[0035] An electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0036] Memory, used to store computer programs;

[0037] The processor is used to implement a method for analyzing natural gas users based on temperature when executing a program stored in a memory.

[0038] A computer-readable storage medium stores a computer program, which implements a method for analyzing natural gas users based on temperature when executed by a processor.

[0039] The beneficial effects of the present invention are:

[0040] The present invention establishes data points for the user's daily natural gas consumption data and temperature data and performs correlation regression analysis to obtain the temperature-consumption correlation coefficient corresponding to the preset time period of each historical year, and classifies the demand side according to the temperature-consumption correlation coefficient to achieve refined user classification. This provides a scientific user analysis and classification method, which is convenient for guiding natural gas sales companies to formulate similar sales planning plans for the same type of customers, and can solve the problem of inaccurate user classification in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of the present invention;

[0042] Figure 2 is a flow chart in an embodiment of the present invention;

[0043] Figure 3 is a structural block diagram of a temperature-based natural gas user analysis system of the present invention;

[0044] Figure 4 It is a structural block diagram of the device of the present invention. DETAILED DESCRIPTION

[0045] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0046] like Figure 1 As shown, a method for analyzing natural gas users based on temperature includes the following steps:

[0047] S1: Obtain the historical natural gas consumption data and corresponding temperature data of the user and establish a temperature-consumption relationship curve;

[0048] S2: performing correlation regression analysis on the temperature-consumption relationship curve to obtain a temperature-consumption correlation coefficient;

[0049] S3: Classifying the users based on the temperature-consumption correlation coefficient.

[0050] The historical consumption data includes the daily consumption data of the user within a preset time period of multiple historical years; the preset time period is at least two months.

[0051] The specific process of obtaining the user's historical natural gas consumption data and corresponding temperature data and establishing a temperature-consumption relationship curve is as follows:

[0052] Obtain the daily consumption data and temperature data of corresponding dates within a preset time period of multiple historical years;

[0053] Establishing a coordinate system, taking the daily consumption data and the temperature data of the corresponding date as two parameters of a two-dimensional data set, the temperature data as the abscissa of the temperature-consumption data point, and the daily consumption data as the ordinate of the temperature-consumption data point;

[0054] The daily consumption data and the temperature data of the corresponding dates within a preset time period of multiple historical years are displayed in the coordinate system to obtain a temperature-consumption relationship curve.

[0055] The correlation regression analysis includes Pearson correlation regression analysis.

[0056] The specific process of performing correlation regression analysis on the temperature-consumption relationship curve to obtain the temperature-consumption correlation coefficient is as follows:

[0057] The temperature data in the temperature-consumption relationship curve within the preset time period of each historical year of the user is taken as the independent variable, and the corresponding consumption data is taken as the dependent variable. The correlation coefficient between the temperature data and the consumption data is calculated using the correlation regression analysis method, and the correlation coefficient is used as the temperature-consumption correlation coefficient corresponding to the preset time period of each historical year.

[0058] The specific process of classifying the user based on the temperature-consumption correlation coefficient is as follows:

[0059] Presetting a plurality of user categories and adding a numerical range to each of the user categories;

[0060] Determine the numerical range of the degree-consumption correlation coefficient of the user;

[0061] The user is classified into a user category corresponding to the numerical range.

[0062] The specific process of presetting multiple user categories and adding a value range to each of the user categories is as follows:

[0063] The user categories are divided into highly temperature-sensitive users, temperature-sensitive users, and temperature-insensitive users;

[0064] The preset numerical range for the highly temperature-sensitive user is (X1, X2), including X1 and X2; the preset numerical range for the temperature-sensitive user is (X2, X3); the preset numerical range for the temperature-insensitive user is (X3, X4), including X3 and X4.

[0065] In this embodiment, the specific process of the method for analyzing natural gas users based on temperature is as follows:

[0066] Obtaining historical natural gas consumption data and corresponding temperature data of the user, wherein the historical consumption data includes multiple daily consumption data of the user within a preset period (at least two months) of multiple historical years;

[0067] According to the daily consumption data and the temperature data, establishing temperature-consumption data points corresponding to the temperature and consumption data and constructing a temperature-consumption relationship curve;

[0068] Performing a correlation regression analysis on the temperature-consumption relationship curve within a preset time period of each of the multiple historical years to obtain a temperature-consumption correlation coefficient corresponding to the preset time period of each historical year;

[0069] The users are classified according to the temperature-consumption correlation coefficient.

[0070] In a specific embodiment, the above method specifically includes:

[0071] Get the historical consumption data of a certain customer or all customers under your jurisdiction;

[0072] Get the weather conditions of the corresponding area;

[0073] Plot the relationship between temperature and historical consumption;

[0074] The Pearson method was used to calculate the correlation coefficient between temperature and historical consumption;

[0075] Determine the size of the correlation coefficient;

[0076] Compare the correlation coefficients between temperature and consumption for different companies;

[0077] For example, the correlation coefficient between the temperature and historical consumption of user A from January to December 2021 is used as an array Z1, and the correlation coefficient between the temperature and historical consumption of user B from January to December 2021 is used as an array Z2. Users are classified based on arrays Z1 and Z2.

[0078] The embodiment of the present disclosure also provides a method for analyzing the sensitivity of natural gas customers to air temperature based on temperature, and the specific steps include:

[0079] When a sales company needs to classify and manage its customers, it can do so by providing the historical consumption of a certain customer or a large number of customers. The sales company can obtain the weather conditions of the corresponding area on the corresponding date from the national public weather information network. General weather conditions include wind speed, weather conditions, temperature and air quality. The sales company needs to obtain temperature data from the weather conditions.

[0080] When the sales company obtains a customer's historical consumption and the temperature conditions on the corresponding date, it draws a relationship curve between temperature and consumption to roughly determine the correlation between temperature and consumption; then it takes the Pearson correlation coefficient for temperature and consumption and analyzes the value of the correlation coefficient. When the correlation coefficient is negative and close to -1, it indicates that the customer has a negative correlation with temperature and the degree of correlation is high. Therefore, the customer is judged to be a temperature-sensitive customer; when the absolute value of the correlation coefficient is close to 0, it indicates that the customer is not correlated with temperature. Therefore, the heating period and non-heating period of the customer can be distinguished, and the correlation coefficients can be calculated separately for judgment.

[0081] The embodiment of the present disclosure also provides an application scenario, the specific contents of which include:

[0082] According to the temperature dependence correlation analysis method, customers in the same area (such as residential users and factory users under Beijing Gas) are classified;

[0083] According to the analysis method of temperature-consumption correlation coefficient, users in different regions of the country are classified (for example, users of Beijing Gas, Tianjin Gas, gas supply agencies in the southern region, etc. are classified).

[0084] The embodiment of the present disclosure provides a method for user classification management, and the specific steps include:

[0085] When a sales company needs to classify and manage its customers, it can do so by providing the historical consumption of all customers or the historical consumption of a large number of customers. The sales company can obtain the weather conditions of the corresponding area on the corresponding date from the national public weather information network. General weather conditions include wind speed, weather conditions, temperature and air quality. The sales company needs to obtain temperature data from the weather conditions.

[0086] A correlation analysis is performed on each customer and their corresponding temperature. The Pearson correlation coefficient is used to measure the sensitivity of different customers to temperature. The correlation coefficients of each customer are summarized, and users are divided according to the size of the correlation coefficient.

[0087] By establishing data points for daily consumption data and temperature data and performing correlation regression analysis, the temperature-consumption correlation coefficient corresponding to the preset time period of each historical year is obtained. The demand side is classified according to the temperature-consumption correlation coefficient to achieve refined user classification. This provides a scientific means of user analysis and classification, which is convenient for guiding natural gas sales companies to formulate similar sales planning plans for the same type of customers and can solve the problem of inaccurate user classification in related technologies.

[0088] In the application scenario, when an analysis request is received from a designated object, the customer historical consumption data provided by the designated object is input into the classification scenario, and the weather data publicly obtained by the designated object from the country is used as input data; the Pearson correlation coefficient is used to analyze the correlation between the customer's historical consumption and the temperature based on the historical consumption and its corresponding temperature conditions; for customers in different regions provided by the designated object, their correlation coefficients are calculated respectively, and customers with close correlation coefficients are classified into one category, which represents the customer's sensitivity to temperature. The similarities of customers in different regions can be analyzed through simple temperature influences, which makes it easier for sales companies to formulate similar sales strategies and planning programs for one category of customers.

[0089] Reference Figure 2 As shown, this embodiment provides a method for analyzing natural gas users based on temperature, including:

[0090] When receiving the classification requirements from the sales company's customers, the sales company needs to provide the customer's historical consumption data in a time series format;

[0091] Obtain historical weather data for the customer's corresponding area from the national public weather information network, including wind speed and direction, high and low temperature air quality index, etc.;

[0092] Input the historical temperature and historical consumption data into the calculation program (regression program) accordingly and calculate the Pearson correlation coefficient of the two columns of data.

[0093] In practical applications, the Pearson correlation coefficient measures the linear correlation. If r = 0, we can only say that there is no linear correlation between x and y, but not that there is no correlation. The larger the absolute value of the correlation coefficient, the stronger the correlation: the closer the correlation coefficient is to 1 or -1, the stronger the correlation, and the closer the correlation coefficient is to 0, the weaker the correlation.

[0094] The above steps only calculate the temperature-consumption correlation coefficient of each user.

[0095] This embodiment provides a method for analyzing natural gas users based on temperature, including:

[0096] Summarize the correlation coefficient R1 between each user's temperature and natural gas consumption;

[0097] The R1 value is used to determine the temperature sensitivity of multiple customers and classify them.

[0098] The above steps are an example of classifying customers based on their temperature-consumption correlation coefficient.

[0099] In actual applications, multiple users have different sensitivities to temperature, especially different customers in different regions. For users with heating purposes, when in the heating season, natural gas consumption is negatively correlated with temperature, and the greater the proportion of heating gas used by this user, the greater the correlation coefficient. When the user is in the non-heating season, the correlation between the customer's consumption behavior and temperature decreases, and the temperature mainly affects natural gas consumption through temperature difference. The temperature difference decreases in the non-heating season, so the correlation between gas consumption and temperature decreases. For customers with natural gas power plant business, when the temperature is high in summer, due to air conditioning and refrigeration, the higher the temperature, the greater the natural gas consumption. At this time, the temperature is positively correlated with natural gas, and the greater the proportion of natural gas power generation business, the greater the correlation coefficient between natural gas and temperature.

[0100] Therefore, the correlation coefficient between natural gas consumption and temperature can be used to determine the extent to which this user is affected by temperature. For multiple users, when the correlation coefficient R1 between natural gas and temperature is similar, these users can be judged as one type of users. Moreover, when R1 is larger, the correlation between these users and temperature is significantly higher, and their sensitivity to temperature increases. Therefore, the size of the correlation coefficient can be used to determine whether these users are users with greater sensitivity to temperature.

[0101] like Figure 3 As shown, an embodiment of the present disclosure provides a temperature-based natural gas user analysis system, comprising:

[0102] The data acquisition module 11 is used to acquire the historical natural gas consumption data and corresponding temperature data of the user;

[0103] A construction module 12 is used to establish a temperature-consumption relationship curve based on the user's historical natural gas consumption data and corresponding temperature data;

[0104] An analysis module 13 is used to perform a correlation regression analysis on the temperature-consumption relationship curve to obtain a temperature-consumption correlation coefficient;

[0105] The classification module 14 is used to classify the users based on the temperature-consumption correlation coefficient.

[0106] The implementation process of the functions and effects of each module in the above system is specifically described in the implementation process of the corresponding steps in the above method, which will not be repeated here.

[0107] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The system embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0108] In the above-mentioned embodiment, any multiple of all modules can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least some functions of one or more of these modules can be combined with at least some functions of other modules and implemented in one module. At least one of all modules can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in a suitable combination of any of them. Alternatively, at least one of all modules can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0109] See also Figure 4 , an electronic device provided by an embodiment of the present disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0110] Memory 1130, used for storing computer programs;

[0111] The processor 1110 is used to implement the method of natural gas user analysis based on temperature as shown below when executing the program stored in the memory 1130.

[0112] The communication bus 1140 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0113] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0114] The memory 1130 may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0115] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0116] The embodiment of the present disclosure further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for analyzing natural gas users based on temperature as described above is implemented.

[0117] The computer-readable storage medium may be included in the device / apparatus described in the above embodiment; or it may exist independently without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method for analyzing natural gas users based on temperature according to the embodiment of the present disclosure is implemented.

[0118] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0119] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for natural gas user analysis based on temperature, characterized in that: The following steps are involved: Obtain the user's historical natural gas consumption data and corresponding temperature data and establish a temperature-consumption relationship curve; Performing correlation regression analysis on the temperature-consumption relationship curve to obtain a temperature-consumption correlation coefficient; The users are classified based on the temperature-consumption correlation coefficient.

2. The method for analyzing natural gas users based on temperature according to claim 1, characterized in that: The historical consumption data includes daily consumption data of the user within a preset time period of multiple historical years.

3. The method for analyzing natural gas users based on temperature according to claim 2, characterized in that: The preset time period is at least two months.

4. The method for analyzing natural gas users based on temperature according to claim 2, characterized in that: The specific process of obtaining the user's historical natural gas consumption data and corresponding temperature data and establishing a temperature-consumption relationship curve is as follows: Obtain the daily consumption data and temperature data of corresponding dates within a preset time period of multiple historical years; Establishing a coordinate system, taking the daily consumption data and the temperature data of the corresponding date as two parameters of a two-dimensional data set, the temperature data as the abscissa of the temperature-consumption data point, and the daily consumption data as the ordinate of the temperature-consumption data point; The daily consumption data and the temperature data of the corresponding dates within a preset time period of multiple historical years are displayed in the coordinate system to obtain a temperature-consumption relationship curve.

5. The method for analyzing natural gas users based on temperature according to claim 1, characterized in that: The correlation regression analysis includes Pearson correlation regression analysis.

6. The method for analyzing natural gas users based on temperature according to claim 2, characterized in that: The specific process of performing correlation regression analysis on the temperature-consumption relationship curve to obtain the temperature-consumption correlation coefficient is as follows: The temperature data in the temperature-consumption relationship curve within the preset time period of each historical year of the user is taken as the independent variable, and the corresponding consumption data is taken as the dependent variable. The correlation coefficient between the temperature data and the consumption data is calculated using the correlation regression analysis method, and the correlation coefficient is used as the temperature-consumption correlation coefficient corresponding to the preset time period of each historical year.

7. The method for analyzing natural gas users based on temperature according to claim 1, characterized in that: The specific process of classifying the user based on the temperature-consumption correlation coefficient is as follows: Presetting a plurality of user categories and adding a numerical range to each of the user categories; Determine the numerical range of the degree-consumption correlation coefficient of the user; The user is classified into a user category corresponding to the numerical range.

8. The method for analyzing natural gas users based on temperature according to claim 7, characterized in that: The specific process of presetting multiple user categories and adding a value range to each of the user categories is as follows: The user categories are divided into highly temperature-sensitive users, temperature-sensitive users, and temperature-insensitive users; The preset numerical range for the highly temperature-sensitive user is (X1, X2), including X1 and X2; the preset numerical range for the temperature-sensitive user is (X2, X3); the preset numerical range for the temperature-insensitive user is (X3, X4), including X3 and X4.

9. A temperature-based natural gas user analysis system, characterized in that: include: A data acquisition module is used to obtain the user's historical natural gas consumption data and corresponding temperature data; A construction module for establishing a temperature-consumption relationship curve based on the user's historical natural gas consumption data and corresponding temperature data; An analysis module, used for performing correlation regression analysis on the temperature-consumption relationship curve to obtain a temperature-consumption correlation coefficient; A classification module is used to classify the user based on the temperature-consumption correlation coefficient.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; The processor is used to implement the method for temperature-based natural gas user analysis described in any one of claims 1-8 when executing the program stored in the memory.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for temperature-based natural gas user analysis according to any one of claims 1 to 8 is implemented.

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