Enterprise profile intelligent determination method based on electric power data

Abstract

The invention relates to the technical field of power data application, and particularly provides a power data-based enterprise classification intelligent judgment method and device, and the method comprises the steps: determining an enterprise capacity ranking percentage and an enterprise annual power consumption ranking percentage based on the power data of an enterprise; performing enterprise classification based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage; wherein the type corresponding to the enterprise is a large-scale enterprise, a medium-scale enterprise or a small-scale enterprise. According to the technical scheme, a large amount of manpower does not need to be input, operation is easy, and enterprises can be accurately shaped.

Description

Technical Field

[0001] The present invention relates to the technical field of power data application, and in particular to a method and device for intelligently determining enterprise classification based on power data. Background Art

[0002] In order to protect and promote the healthy development of enterprises and ensure the efficient management and operation of enterprises, enterprise scale classification is particularly important. At present, the "Regulations on the Classification of Small and Medium Enterprises" is the classification standard for large, medium, small and micro enterprises, which mainly relies on three data items: operating income, employees, and total assets, and classifies enterprises through a table lookup method. However, such statistical data is difficult to accurately obtain or verify in practice, and the operating cost is extremely high. And this technical means does not involve the use of electricity data.

[0003] In this regard, researchers have proposed a technology for enterprise scale classification based on power data. The first is electricity registration and classification: when enterprise users register electricity with relevant departments, they fill in enterprise information, electricity address, enterprise type, enterprise scale and other information. This method requires a lot of manpower and time, and the on-site registration is complicated. There are many uncertain factors on site, which affect the effect of information registration. The second is external data association: the data management unit obtains enterprise information through enterprise data procurement, self-collection, and purchase by authoritative institutions. After acquisition, the enterprise scale is divided by matching and associating with the power system archive data. However, this method relies on an accurate enterprise classification list, and there is currently no complete list of various types of enterprises. Even if the external data association filling work of some enterprises is carried out, information is collected through information means such as procurement data and collection. The overall workload is smaller than that of electricity registration. However, due to factors such as incomplete procurement information and untrue feedback from procurement objects, the quality of external data association filling is not as good as that of electricity registration and classification. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present invention proposes a method and device for intelligently determining enterprise classification based on power data.

[0005] In a first aspect, a method for intelligently determining enterprise classification based on power data is provided, wherein the method for intelligently determining enterprise classification based on power data comprises:

[0006] Determine the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage based on the enterprise's electricity data;

[0007] Classify enterprises based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage;

[0008] The enterprise may be a large enterprise, a medium-sized enterprise or a small enterprise.

[0009] Preferably, the enterprise capacity ranking percentages are as follows:

[0010]

[0011] The annual electricity consumption ranking percentages of the enterprises are as follows:

[0012]

[0013] In the above formula, rcr_p is the percentage of enterprise capacity ranking, rcr is the ranking of the enterprise's operating capacity in the descending ranking of the corresponding operating capacity of each enterprise, c is the total number of enterprises, pqr_p is the percentage of the enterprise's annual electricity consumption ranking, and pqr is the ranking of the enterprise's annual electricity consumption in the descending ranking of the corresponding annual electricity consumption of each enterprise.

[0014] Preferably, the enterprise classification based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage includes:

[0015] Determining a first boundary value and a second boundary value for classifying large and medium-sized enterprises;

[0016] Determine the third boundary value and the fourth boundary value for classifying large enterprises;

[0017] Enterprises whose operating capacity exceeds the third boundary value and whose annual electricity consumption exceeds the fourth boundary value shall be regarded as large enterprises;

[0018] Enterprises whose operating capacity exceeds the first boundary value and is less than the third boundary value, and whose annual electricity consumption exceeds the second boundary value and is less than the fourth boundary value are considered medium-sized enterprises;

[0019] Enterprises whose operating capacity is less than the first boundary value and whose annual electricity consumption is less than the second boundary value are regarded as small enterprises.

[0020] Furthermore, the determining of the first boundary value and the second boundary value for dividing large and medium-sized enterprises includes:

[0021] Step a. Initialize the total proportion of large and medium-sized enterprises y;

[0022] Step b. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise;

[0023] Step c. Let the first threshold be equal to the second threshold be equal to y;

[0024] Step d. adding enterprises whose enterprise capacity ranking percentage exceeds the first threshold to the first set, and adding enterprises whose annual electricity consumption ranking percentage exceeds the second threshold to the second set;

[0025] Step e. Obtain the ratio k of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises, and determine whether the ratio satisfies: yk≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the first boundary value and the second boundary value, respectively; otherwise, increase the first threshold by a first preset step length, increase the second threshold by a second preset step length, and then return to step d;

[0026] Among them, err is the preset error value.

[0027] Furthermore, the determining of the third boundary value and the fourth boundary value for classifying large enterprises includes:

[0028] Step f. Initialize the proportion of large enterprises to x;

[0029] Step g. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise;

[0030] Step h. Let the third threshold be equal to the fourth threshold be equal to x;

[0031] Step i. Add enterprises whose enterprise capacity ranking percentage exceeds the third threshold to the third set, and add enterprises whose annual electricity consumption ranking percentage exceeds the fourth threshold to the fourth set;

[0032] Step j. Obtain the ratio w of the number of enterprises corresponding to the intersection of the third set and the fourth set to the total number of enterprises, and determine whether the ratio satisfies: xw≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the third boundary value and the fourth boundary value, respectively; otherwise, increase the third threshold by a third preset step length, increase the fourth threshold by a fourth preset step length, and then return to step i;

[0033] Among them, err is the preset error value.

[0034] Furthermore, the ratio of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises is as follows:

[0035] k=Enum1 / c

[0036] In the above formula, Enum1 is the number of enterprises corresponding to the intersection of the first set and the second set, and c is the total number of enterprises.

[0037] Furthermore, the first step length is greater than the second step length, and the third step length is greater than the fourth step length.

[0038] In a second aspect, a device for intelligently determining enterprise classification based on power data is provided, wherein the device for intelligently determining enterprise classification based on power data comprises:

[0039] An analysis module, for determining a capacity ranking percentage of an enterprise and an annual electricity consumption ranking percentage of an enterprise based on the electricity data of the enterprise;

[0040] A classification module, used for classifying enterprises based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage;

[0041] The enterprise may be a large enterprise, a medium-sized enterprise or a small enterprise.

[0042] In a third aspect, a computer device is provided, comprising: one or more processors;

[0043] The processor is used to store one or more programs;

[0044] When the one or more programs are executed by the one or more processors, the enterprise classification intelligent determination method based on power data is implemented.

[0045] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the enterprise classification intelligent determination method based on power data is implemented.

[0046] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0047] The present invention relates to the field of power data application technology, and specifically provides a method and device for intelligently determining enterprise classification based on power data, including: determining the enterprise capacity ranking percentage and the enterprise annual power consumption ranking percentage based on the enterprise's power data; classifying the enterprise based on the enterprise capacity ranking percentage and the enterprise annual power consumption ranking percentage; wherein the enterprise corresponds to a large enterprise, a medium-sized enterprise or a small enterprise. The technical solution provided by the present invention does not require a large amount of manpower, is simple to operate, and can accurately classify enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flowchart diagram of the main steps of the enterprise classification intelligent determination method based on power data in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of an intelligent enterprise classification determination method based on power data according to an embodiment of the present invention. Figure 1 As shown, the enterprise classification intelligent determination method based on power data in the embodiment of the present invention mainly includes the following steps:

[0053] Step S101: determining the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage based on the enterprise's power data;

[0054] Step S102: classifying enterprises based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage;

[0055] The enterprise may be a large enterprise, a medium-sized enterprise or a small enterprise.

[0056] In this embodiment, due to the different electricity consumption characteristics of different industries, it is necessary to perform classification calculations for each industry separately, and the initial values ​​of the proportions of different industries and different types of enterprises are obtained through economic censuses or actual statistics.

[0057] In this embodiment, the enterprise capacity ranking percentages are as follows:

[0058]

[0059] The annual electricity consumption ranking percentages of the enterprises are as follows:

[0060]

[0061] In the above formula, rcr_p is the percentage of enterprise capacity ranking, rcr is the ranking of the enterprise's operating capacity in the descending ranking of the corresponding operating capacity of each enterprise, c is the total number of enterprises, pqr_p is the percentage of the enterprise's annual electricity consumption ranking, and pqr is the ranking of the enterprise's annual electricity consumption in the descending ranking of the corresponding annual electricity consumption of each enterprise.

[0062] In this embodiment, the enterprise classification based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage includes:

[0063] Determining a first boundary value and a second boundary value for classifying large and medium-sized enterprises;

[0064] Determine the third boundary value and the fourth boundary value for classifying large enterprises;

[0065] Enterprises whose operating capacity exceeds the third boundary value and whose annual electricity consumption exceeds the fourth boundary value shall be regarded as large enterprises;

[0066] Enterprises whose operating capacity exceeds the first boundary value and is less than the third boundary value, and whose annual electricity consumption exceeds the second boundary value and is less than the fourth boundary value are considered medium-sized enterprises;

[0067] Enterprises whose operating capacity is less than the first boundary value and whose annual electricity consumption is less than the second boundary value are regarded as small enterprises.

[0068] In one embodiment, determining the first boundary value and the second boundary value for dividing large and medium-sized enterprises includes:

[0069] Step a. Initialize the total proportion of large and medium-sized enterprises y;

[0070] Step b. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise;

[0071] Step c. Let the first threshold be equal to the second threshold be equal to y;

[0072] Step d. adding enterprises whose enterprise capacity ranking percentage exceeds the first threshold to the first set, and adding enterprises whose annual electricity consumption ranking percentage exceeds the second threshold to the second set;

[0073] Step e. Obtain the ratio k of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises, and determine whether the ratio satisfies: yk≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the first boundary value and the second boundary value, respectively; otherwise, increase the first threshold by a first preset step length, increase the second threshold by a second preset step length, and then return to step d;

[0074] Among them, err is the preset error value.

[0075] In one embodiment, determining the third boundary value and the fourth boundary value for classifying large enterprises includes:

[0076] Step f. Initialize the proportion of large enterprises to x;

[0077] Step g. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise;

[0078] Step h. Let the third threshold be equal to the fourth threshold be equal to x;

[0079] Step i. Add enterprises whose enterprise capacity ranking percentage exceeds the third threshold to the third set, and add enterprises whose annual electricity consumption ranking percentage exceeds the fourth threshold to the fourth set;

[0080] Step j. Obtain the ratio w of the number of enterprises corresponding to the intersection of the third set and the fourth set to the total number of enterprises, and determine whether the ratio satisfies: xw≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the third boundary value and the fourth boundary value, respectively; otherwise, increase the third threshold by a third preset step length, increase the fourth threshold by a fourth preset step length, and then return to step i;

[0081] Among them, err is the preset error value.

[0082] In one embodiment, the ratio of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises is as follows:

[0083] k=Enum1 / c

[0084] In the above formula, Enum1 is the number of enterprises corresponding to the intersection of the first set and the second set, and c is the total number of enterprises.

[0085] In one embodiment, the first step length is greater than the second step length, and the third step length is greater than the fourth step length.

[0086] Example 2

[0087] Based on the same inventive concept, the present invention also provides an intelligent determination device for enterprise classification based on power data, and the intelligent determination device for enterprise classification based on power data includes:

[0088] An analysis module, for determining a capacity ranking percentage of an enterprise and an annual electricity consumption ranking percentage of an enterprise based on the electricity data of the enterprise;

[0089] A classification module, used for classifying enterprises based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage;

[0090] The enterprise may be a large enterprise, a medium-sized enterprise or a small enterprise.

[0091] Preferably, the enterprise capacity ranking percentages are as follows:

[0092]

[0093] The annual electricity consumption ranking percentages of the enterprises are as follows:

[0094]

[0095] In the above formula, rcr_p is the percentage of enterprise capacity ranking, rcr is the ranking of the enterprise's operating capacity in the descending ranking of the corresponding operating capacity of each enterprise, c is the total number of enterprises, pqr_p is the percentage of the enterprise's annual electricity consumption ranking, and pqr is the ranking of the enterprise's annual electricity consumption in the descending ranking of the corresponding annual electricity consumption of each enterprise.

[0096] Preferably, the enterprise classification based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage includes:

[0097] Determining a first boundary value and a second boundary value for classifying large and medium-sized enterprises;

[0098] Determine the third boundary value and the fourth boundary value for classifying large enterprises;

[0099] Enterprises whose operating capacity exceeds the third boundary value and whose annual electricity consumption exceeds the fourth boundary value shall be regarded as large enterprises;

[0100] Enterprises whose operating capacity exceeds the first boundary value and is less than the third boundary value, and whose annual electricity consumption exceeds the second boundary value and is less than the fourth boundary value are considered medium-sized enterprises;

[0101] Enterprises whose operating capacity is less than the first boundary value and whose annual electricity consumption is less than the second boundary value are regarded as small enterprises.

[0102] Furthermore, the determining of the first boundary value and the second boundary value for dividing large and medium-sized enterprises includes:

[0103] Step a. Initialize the total proportion of large and medium-sized enterprises y;

[0104] Step b. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise;

[0105] Step c. Let the first threshold be equal to the second threshold be equal to y;

[0106] Step d. adding enterprises whose enterprise capacity ranking percentage exceeds the first threshold to the first set, and adding enterprises whose annual electricity consumption ranking percentage exceeds the second threshold to the second set;

[0107] Step e. Obtain the ratio k of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises, and determine whether the ratio satisfies: yk≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the first boundary value and the second boundary value, respectively; otherwise, increase the first threshold by a first preset step length, increase the second threshold by a second preset step length, and then return to step d;

[0108] Among them, err is the preset error value.

[0109] Furthermore, the determining of the third boundary value and the fourth boundary value for classifying large enterprises includes:

[0110] Step f. Initialize the proportion of large enterprises to x;

[0111] Step g. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise;

[0112] Step h. Let the third threshold be equal to the fourth threshold be equal to x;

[0113] Step i. Add enterprises whose enterprise capacity ranking percentage exceeds the third threshold to the third set, and add enterprises whose annual electricity consumption ranking percentage exceeds the fourth threshold to the fourth set;

[0114] Step j. Obtain the ratio w of the number of enterprises corresponding to the intersection of the third set and the fourth set to the total number of enterprises, and determine whether the ratio satisfies: xw≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the third boundary value and the fourth boundary value, respectively; otherwise, increase the third threshold by a third preset step length, increase the fourth threshold by a fourth preset step length, and then return to step i;

[0115] Among them, err is the preset error value.

[0116] Furthermore, the ratio of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises is as follows:

[0117] k=Enum1 / c

[0118] In the above formula, Enum1 is the number of enterprises corresponding to the intersection of the first set and the second set, and c is the total number of enterprises.

[0119] Furthermore, the first step length is greater than the second step length, and the third step length is greater than the fourth step length.

[0120] Example 3

[0121] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of an enterprise classification intelligent determination method based on power data in the above embodiment.

[0122] Example 4

[0123] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of an enterprise classification intelligent determination method based on power data in the above embodiment.

[0124] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent enterprise classification determination method based on power data, characterized in that: The method comprises: Determine the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage based on the enterprise's electricity data; Classify enterprises based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage; The enterprise may be a large enterprise, a medium-sized enterprise or a small enterprise.

2. The method according to claim 1, characterized in that The enterprise capacity ranking percentages are as follows: The annual electricity consumption ranking percentages of the enterprises are as follows: In the above formula, rcr_p is the percentage of enterprise capacity ranking, rcr is the ranking of the enterprise's operating capacity in the descending ranking of the corresponding operating capacity of each enterprise, c is the total number of enterprises, pqr_p is the percentage of the enterprise's annual electricity consumption ranking, and pqr is the ranking of the enterprise's annual electricity consumption in the descending ranking of the corresponding annual electricity consumption of each enterprise.

3. The method according to claim 1, characterized in that The enterprise classification based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage includes: Determining a first boundary value and a second boundary value for classifying large and medium-sized enterprises; Determine the third boundary value and the fourth boundary value for classifying large enterprises; Enterprises whose operating capacity exceeds the third boundary value and whose annual electricity consumption exceeds the fourth boundary value shall be regarded as large enterprises; Enterprises whose operating capacity exceeds the first boundary value and is less than the third boundary value, and whose annual electricity consumption exceeds the second boundary value and is less than the fourth boundary value are considered medium-sized enterprises; Enterprises whose operating capacity is less than the first boundary value and whose annual electricity consumption is less than the second boundary value are regarded as small enterprises.

4. The method according to claim 3, characterized in that The determining of the first boundary value and the second boundary value for dividing large and medium-sized enterprises comprises: Step a. Initialize the total proportion of large and medium-sized enterprises y; Step b. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise; Step c. Let the first threshold be equal to the second threshold be equal to y; Step d. adding enterprises whose enterprise capacity ranking percentage exceeds the first threshold to the first set, and adding enterprises whose annual electricity consumption ranking percentage exceeds the second threshold to the second set; Step e. Obtain the ratio k of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises, and determine whether the ratio satisfies: yk≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the first boundary value and the second boundary value, respectively; otherwise, increase the first threshold by a first preset step length, increase the second threshold by a second preset step length, and then return to step d; Among them, err is the preset error value.

5. The method according to claim 4, characterized in that The determining of the third boundary value and the fourth boundary value for classifying large enterprises includes: Step f. Initialize the proportion of large enterprises to x; Step g. Calculate the capacity ranking percentage of each enterprise and the annual electricity consumption ranking percentage of each enterprise; Step h. Let the third threshold be equal to the fourth threshold be equal to x; Step i. Add enterprises whose enterprise capacity ranking percentage exceeds the third threshold to the third set, and add enterprises whose annual electricity consumption ranking percentage exceeds the fourth threshold to the fourth set; Step j. Obtain the ratio w of the number of enterprises corresponding to the intersection of the third set and the fourth set to the total number of enterprises, and determine whether the ratio satisfies: xw≤err. If so, use the minimum value of the enterprise operating capacity and the minimum value of the enterprise annual electricity consumption in the intersection as the third boundary value and the fourth boundary value, respectively; otherwise, increase the third threshold by a third preset step length, increase the fourth threshold by a fourth preset step length, and then return to step i; Among them, err is the preset error value.

6. The method according to claim 4, characterized in that The proportion of the number of enterprises corresponding to the intersection of the first set and the second set to the total number of enterprises is as follows: k=Enum1 / c In the above formula, Enum1 is the number of enterprises corresponding to the intersection of the first set and the second set, and c is the total number of enterprises.

7. The method according to claim 5, characterized in that The first step length is greater than the second step length, and the third step length is greater than the fourth step length.

8. An intelligent determination device for enterprise classification based on power data, characterized in that: The device comprises: An analysis module, for determining a capacity ranking percentage of an enterprise and an annual electricity consumption ranking percentage of an enterprise based on the electricity data of the enterprise; A classification module, used for classifying enterprises based on the enterprise capacity ranking percentage and the enterprise annual electricity consumption ranking percentage; The enterprise may be a large enterprise, a medium-sized enterprise or a small enterprise.

9. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the enterprise classification intelligent determination method based on power data as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, it implements the enterprise classification intelligent determination method based on power data as described in any one of claims 1 to 7.

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