An e-commerce purchasing method for electric power companies

Abstract

The present invention discloses an e-commerce procurement method for an electric power company, relates to the field of e-commerce procurement, and solves the problem of low accuracy and reliability in traditional prediction methods. The method comprises: step S1, acquiring historical procurement data and historical inventory data of the electric power company; step S2, analyzing the historical procurement data and historical inventory data of the electric power company to obtain the fluctuation coefficient corresponding to the selected year; step S3, calculating the difference order and lag order of the monthly procurement commodity quantity corresponding to the selected year; step S4, predicting the predicted monthly procurement quantity corresponding to the current year through the monthly procurement commodity quantity corresponding to the selected year; step S5, calculating the prediction accuracy value corresponding to the predicted monthly procurement quantity, a user receives the predicted monthly procurement quantity through a user terminal, and determines the accuracy of the prediction through the prediction accuracy value. The present invention eliminates the influence of abnormal data in the historical data on the prediction result, and ensures the prediction accuracy and prediction reliability of the corresponding procurement quantity of the purchased commodity.

Description

Technical Field

[0001] The present invention belongs to the field of e-commerce procurement, and specifically relates to an e-commerce procurement method for an electric power company. Background Art

[0002] E-commerce procurement refers to the process of conducting procurement activities through e-commerce platforms. It is a way to digitize and online the traditional procurement process and use e-commerce technology and network platforms to conduct procurement activities.

[0003] In e-commerce procurement, buyers can browse the supplier's product catalog or online market through the e-commerce platform, search and screen the required goods or services, and communicate and negotiate with suppliers online. Once a procurement agreement is reached, buyers can generate an order on the platform and pay through the online payment system. E-commerce procurement has many advantages, so it is necessary to formulate a procurement plan in advance.

[0004] However, at present, when predicting the purchase quantity of purchased goods, it is usually impossible to remove outliers in historical data, which leads to errors in the prediction results. At the same time, after completing the prediction, traditional methods are also unable to determine the accuracy of the prediction results, resulting in reduced reliability of the prediction results.

[0005] To this end, we propose an e-commerce procurement method for power companies. Summary of the invention

[0006] The purpose of the present invention is to propose an e-commerce purchasing method for an electric power company to solve the problems raised in the above background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An e-commerce purchasing method for an electric power company, the method comprising:

[0009] Step S1, obtaining the historical purchasing data and historical inventory data of the power company;

[0010] Step S2, analyzing the historical purchasing data and historical inventory data of the power company to obtain the fluctuation coefficient corresponding to the selected year;

[0011] Step S3, calculating the difference order and lag order corresponding to the purchase quantity of goods in the corresponding month of the selected year;

[0012] Step S4, predicting the predicted monthly purchase volume of the current year by selecting the monthly purchase volume of commodities corresponding to the year;

[0013] Step S5, calculating the forecast accuracy value corresponding to the forecast monthly purchase volume.

[0014] Furthermore, the historical procurement data specifically refers to the monthly purchase volume of commodities by the power company in the year of collection;

[0015] The historical inventory data refers to the inventory of goods of the power company in the collection year, and the collection year is recorded from the year before the current year onwards.

[0016] Furthermore, the step S2 includes the following sub-steps:

[0017] Step S21, obtaining the historical purchase data corresponding to the power company, and obtaining the monthly purchase volume of goods by the power company in the collection year;

[0018] Step S22, obtaining the historical inventory data corresponding to the power company, obtaining the inventory quantity corresponding to the goods purchased by the power company in the collection year, and recording the inventory quantity at the end of the year as the annual inventory quantity of the corresponding year;

[0019] Step S23, adding up the monthly purchase quantities of goods in different months of the same year to obtain the annual purchase quantity of the corresponding year;

[0020] Compare the annual inventory and annual purchase volume corresponding to the same year. If the annual inventory is greater than or equal to the correction coefficient multiplied by the annual purchase volume, the historical purchase data of the corresponding year will be discarded. If the annual inventory is less than the correction coefficient multiplied by the annual purchase volume, no operation will be performed.

[0021] Step S24, record the year before the current year as the selected year, record the monthly purchase volume of the corresponding purchased goods in the selected year as the selected purchase data, record the five years before the selected year as the comparison year, record the monthly purchase volume of the corresponding purchased goods in the comparison year as the comparison purchase data, and proceed to the next step;

[0022] Step S25, identifying and removing abnormal values ​​corresponding to the monthly purchase quantity of goods in the selected purchase data.

[0023] Furthermore, the step S25 includes the following sub-steps:

[0024] Step S251, adding up the monthly purchase commodity quantities corresponding to the i-th month of different comparison years and taking the average value to obtain the average monthly purchase commodity quantity PJSi of the i-th month of all comparison years;

[0025] Step S252, calculate the standard deviation BCGi of the purchased goods of the power company in the i-th month of the comparison year by the formula, the specific formula is as follows:

[0026] Wherein, j is the number of the comparison year, j = 1, 2, ..., z, z is a positive integer; LCGji is the monthly purchase volume of the i-th month in the comparison year j;

[0027] Step S253, calculate the data offset value SPYi of the i-th month in the selected year by a formula, the specific formula is as follows:

[0028] SPYi=|XCGi-PJSi| / BCGi; where XCGi is the monthly purchase volume of the i-th month in the selected year;

[0029] Step S254, repeatedly executing steps S251 to S253 until the data offset values ​​of the twelve months corresponding to the selected year are calculated;

[0030] Step S255, compare the data offset value of each month in the selected year with the standard offset threshold. If the data offset value is less than or equal to the standard offset threshold, no operation is performed. If the data offset value is greater than the standard offset threshold, the monthly purchase volume of the corresponding month of the selected year is deemed to be an abnormal value, and the monthly purchase volume of the corresponding month of the selected year is eliminated, and the value of the fluctuation coefficient is increased by one.

[0031] Furthermore, the step S3 includes the following sub-steps:

[0032] Step S31, obtaining the monthly purchase commodity volume in the selected year after elimination and the monthly purchase commodity volume in the comparison year, and generating the purchase commodity of the corresponding month;

[0033] Step S32, performing a differential operation on the purchased commodity sequence to obtain a first-order difference sequence of the purchased commodity sequence;

[0034] Step S33, performing a difference operation on the first-order difference sequence to obtain a second-order difference sequence corresponding to the purchased commodity sequence, and repeating the difference process until the difference sequence corresponding to the purchased commodity sequence has only one element left, and the order of the corresponding difference sequence is recorded as the difference order of the corresponding month;

[0035] Step S34, performing a lagged difference operation on the purchased commodity sequence to obtain a first-order lagged difference sequence of the purchased commodity sequence;

[0036] Step S35, perform lag difference on the first-order lag difference sequence to obtain a second-order lag difference sequence corresponding to the purchased goods sequence; repeat the lag difference operation until lag difference cannot be performed, and record the order of the corresponding lag difference sequence as the lag order of the corresponding month.

[0037] Furthermore, the differential operation of step S32 is specifically as follows:

[0038] Get the purchase commodity sequence CGXi = {CGXi1, CGXi2, CGXi3, CGXi4, CGXi5, CGXi6}, where i represents the month, i = 1, 2, ..., 12;

[0039] The first element corresponding to the first-order difference sequence is obtained by calculating CGXi2-CGXi1;

[0040] The second element corresponding to the first-order difference sequence is obtained by calculating CGXi3-CGXi2;

[0041] And so on, until the last element corresponding to the first-order difference sequence is obtained through CGXi6-CGXi5, and the elements corresponding to the first-order difference sequence are combined to obtain the first-order difference sequence corresponding to the purchased goods sequence: {CGXi2-CGXi1, CGXi3-CGXi2, CGXi4-CGXi3, CGXi5-CGXi4, CGXi6-CGXi5}.

[0042] Furthermore, the delayed differential operation of step S34 is specifically as follows:

[0043] Get the purchase commodity sequence CGXi = {CGXi1, CGXi2, CGXi3, CGXi4, CGXi5, CGXi6};

[0044] Perform lagged difference calculation, that is, when performing difference operation on the purchased goods sequence, use the separated elements for subtraction;

[0045] The first element corresponding to the first-order lagged difference sequence is obtained by calculating CGXi3-CGXi1;

[0046] The second element corresponding to the first-order lagged difference sequence is obtained by calculating CGXi4-CGXi2;

[0047] And so on, until the lagged difference calculation cannot be performed, the corresponding first-order lagged difference sequence is: {CGXi3-CGXi1, CGXi4-CGXi2, CGXi5-CGXi3, CGXi6-CGXi4}.

[0048] Furthermore, the step S4 includes the following sub-steps:

[0049] Step S41, obtaining the monthly purchase commodity volume and fluctuation coefficient corresponding to the selected year, and obtaining the difference order and lag order of each month in the selected year;

[0050] Step S42, obtain the difference order of each month of the selected year, add and average them to obtain the annual difference order of the selected year, and similarly, obtain the annual lag order of the selected year;

[0051] Step S43, substitute the monthly commodity purchase quantity, fluctuation coefficient BD, annual difference order CF and annual lag order ZH into the forecasting model to obtain the forecasted monthly purchase quantity of the current year.

[0052] Furthermore, the step S5 includes the following sub-steps:

[0053] Step S51, obtaining the predicted monthly purchase volume corresponding to the current year, obtaining the selected purchase data corresponding to the selected year after elimination, and then obtaining the compared purchase data corresponding to the year before the selected year, that is, the first compared year;

[0054] Step S52, calculate the purchase difference value CCY between the current year and the selected year by using a formula, the specific formula is as follows:

[0055] In the formula, i is the number of the month, YCGi represents the predicted monthly purchase volume of the i-th month, and XCGi represents the monthly purchase volume of the i-th month in the selected year;

[0056] Step S53, calculate the standard purchase difference value BCY corresponding to the selected year through the formula, the specific formula is as follows:

[0057] In the formula, ECGi represents the monthly purchase volume of goods in the i-th month of the first comparison year;

[0058] Step S54: if the value of the standard purchase difference value is zero, the purchase difference value between the current year and the selected year is recorded as the forecast accuracy value; if the value of the standard purchase difference value is not zero, the forecast accuracy value is obtained by dividing the purchase difference value by the standard purchase difference value;

[0059] Step S55, sending the predicted monthly purchase volume and the predicted accuracy value to the user terminal, the user receives the predicted monthly purchase volume, and obtains the accuracy of the prediction through the predicted accuracy value.

[0060] Furthermore, the closer the forecast accuracy value is to one, the more accurate the result of forecasting the monthly purchase volume is.

[0061] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0062] The present invention first obtains the historical procurement data and historical inventory data of the power company and analyzes the historical procurement data and historical inventory data to obtain the fluctuation coefficient corresponding to the selected year, and then calculates the differential order and lag order of the monthly procurement quantity corresponding to the selected year, thereby obtaining the annual differential order and annual lag order corresponding to the selected year, and substitutes the fluctuation coefficient, annual lag order and annual differential order into the prediction model to obtain the predicted monthly procurement volume corresponding to the current year, calculates the prediction accuracy value of the predicted monthly procurement volume, and the user terminal determines the accuracy of the prediction through the prediction accuracy value. The present invention eliminates the influence of abnormal data in the historical data on the prediction result, and ensures the prediction accuracy and prediction reliability of the corresponding procurement quantity of the purchased goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0064] Figure 1 The present invention is a flow chart of the method.

[0065] Figure 2 This is a sub-step flow chart of step S2 in the present invention.

[0066] Figure 3 This is a sub-step flow chart of step S3 in the present invention.

[0067] Figure 4 This is a sub-step flow chart of step S5 in the present invention.

[0068] Figure 5 It is a schematic diagram of the structure of the electronic device in the present invention. DETAILED DESCRIPTION

[0069] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0070] Embodiment 1: It should be noted in advance that when a power company makes a purchase, it usually purchases multiple products. In the present invention, purchasing multiple products is just a repeated execution of the method process of purchasing a single commodity. Therefore, when analyzing the purchase data related to the power company, the present invention assumes that the power company only purchases a single type of commodity.

[0071] See also Figure 1-Figure 4 As shown, the technical solution provided by the present invention is: an e-commerce procurement method for a power company, the method is specifically as follows:

[0072] Step S1, obtaining the historical purchase data and historical inventory data of the power company; wherein the historical purchase data specifically refers to the monthly purchase quantity of commodities by the power company in the collection year; the historical inventory data refers to the inventory quantity of commodities by the power company in the collection year;

[0073] The collection year is specifically recorded from the year before the current year, and in this embodiment, the collection year is preferably six years; that is, if the current year is 2023, the collection year is 2017-2022;

[0074] Step S2, analyzing the historical purchasing data and historical inventory data of the power company to obtain the fluctuation coefficient corresponding to the selected year;

[0075] The step S2 further comprises the following sub-steps:

[0076] Step S21, obtaining the historical purchase data corresponding to the power company, and obtaining the monthly purchase volume of goods by the power company in the collection year;

[0077] Step S22, obtaining the historical inventory data corresponding to the power company, obtaining the inventory quantity corresponding to the goods purchased by the power company in the collection year, and recording the inventory quantity at the end of the year as the annual inventory quantity of the corresponding year;

[0078] Step S23, adding up the monthly purchase quantities of goods in different months of the same year to obtain the annual purchase quantity of the corresponding year;

[0079] Compare the annual inventory and annual purchase volume corresponding to the same year;

[0080] If the annual inventory is greater than or equal to the correction factor multiplied by the annual purchase volume, the historical purchase data of the corresponding year will be discarded. The calculation process of the correction factor is as follows:

[0081] Obtain the ratio between the annual inventory and the annual purchase volume for each of the six years preceding the current year, and record the ratio as the inventory / purchase ratio;

[0082] The corresponding correction coefficient is obtained by adding up and averaging the inventory / purchase ratios of each year;

[0083] If the annual inventory is less than the correction factor multiplied by the annual purchase volume, no action will be taken;

[0084] Step S24, record the year before the current year as the selected year, record the monthly purchase volume of the corresponding purchased goods in the selected year as the selected purchase data, record the five years before the selected year as the comparison year, record the monthly purchase volume of the corresponding purchased goods in the comparison year as the comparison purchase data, and proceed to the next step;

[0085] Step S25, identifying and removing abnormal values ​​corresponding to the monthly purchase quantity of goods in the selected purchase data;

[0086] The step S25 further includes the following sub-steps:

[0087] Step S251, adding up the monthly purchase commodity quantities corresponding to the i-th month of different comparison years and taking the average value to obtain the average monthly purchase commodity quantity PJSi of the i-th month of all comparison years;

[0088] Step S252, calculate the standard deviation BCGi of the purchased goods of the power company in the i-th month of the comparison year by the formula, the specific formula is as follows:

[0089] Wherein, j is the number of the comparison year, j = 1, 2, ..., z, z is a positive integer; LCGji is the monthly purchase volume of the i-th month in the comparison year j;

[0090] Step S253, calculate the data offset value SPYi of the i-th month in the selected year by a formula, the specific formula is as follows:

[0091] SPYi=|XCGi-PJSi| / BCGi; where XCGi is the monthly purchase volume of the i-th month in the selected year;

[0092] Step S254, repeatedly executing steps S251 to S253 until the data offset values ​​of the twelve months corresponding to the selected year are calculated;

[0093] Step S255, compare the data offset value of each month in the selected year with the standard offset threshold. If the data offset value is less than or equal to the standard offset threshold, no operation is performed; if the data offset value is greater than the standard offset threshold, the monthly purchase commodity volume of the corresponding month of the selected year is determined to be an abnormal value, and the monthly purchase commodity volume of the corresponding month of the selected year is eliminated, and the value of the fluctuation coefficient is increased by one;

[0094] It should be noted that the initial value of the fluctuation coefficient is zero. The fluctuation coefficient is used to evaluate the fluctuation of the purchase volume of the selected year compared with the purchase volume of previous years. The larger the fluctuation coefficient, the more unstable the purchase volume of the selected year.

[0095] Step S3, calculating the difference order and lag order of the monthly purchase quantity of the selected year;

[0096] The step S3 further comprises the following sub-steps:

[0097] Step S31, obtaining the monthly purchase commodity volume of the i-th month in the selected year after elimination and the monthly purchase commodity volume of the i-th month in the comparison year, and generating a purchase commodity sequence CGXi={CGXi1, CGXi2, CGXi3, CGXi4, CGXi5, CGXi6} of the corresponding month, wherein, since there are twelve months in a year, there are twelve purchase commodity sequences, that is, the upper limit value of i is 12;

[0098] Step S32, performing a difference operation on the purchased commodity sequence, that is, calculating the difference between adjacent elements in the purchased commodity sequence to obtain a first-order difference sequence of the purchased commodity sequence;

[0099] For example, if the monthly purchase commodity quantities in July of the six years before the current year are all normal data, there is no data exclusion. Therefore, the purchase commodity sequence corresponding to July of the six years before the current year is {4, 8, 6, 5, 3, 4}, and the length of the sequence is 6. The purchase commodity sequence is differentiated:

[0100] The first step is to calculate the difference between the second element and the first element, which is 8-4=4.

[0101] The second step is to calculate the difference between the third element and the second element, that is, 6-8=-2. Similarly, the first-order difference sequence corresponding to the purchased product sequence is calculated as: {4, -2, -1, -2, 1}. After one difference, the original sequence will become one element shorter.

[0102] Step S33, performing differentiation on the first-order difference sequence to obtain a second-order difference sequence corresponding to the purchased commodity sequence, and repeating the differentiation process until the difference sequence corresponding to the purchased commodity sequence has only one element left, and the order of the corresponding difference sequence is recorded as the difference order;

[0103] Step S34, calculating the lagged difference operation corresponding to the purchased commodity sequence, that is, when performing the difference operation on the purchased commodity sequence, instead of subtracting adjacent elements, the separated elements are used for subtraction;

[0104] For example, if the monthly purchase volume of goods in July of the six years before the current year is normal data, there is no data exclusion. Therefore, the purchase commodity sequence corresponding to July of the six years before the current year is {4, 8, 6, 5, 3, 4}, and the purchase commodity sequence is lagged and differencing is performed:

[0105] The first step is to calculate the difference between the third element and the first element, which is 6-4=2;

[0106] The second step is to calculate the difference between the fourth element and the second element, that is, 5-8=-3. Similarly, the first-order lagged difference sequence corresponding to the purchased goods sequence is calculated: {2, -3, -3, -1}.

[0107] Step S35, performing a lagged difference operation on the first-order lagged difference sequence to obtain a second-order lagged difference sequence corresponding to the purchased goods sequence; repeatedly performing the lagged difference operation until no lagged difference operation can be performed, and recording the order of the corresponding lagged difference sequence as the lagged order;

[0108] Step S4, predicting the predicted monthly purchase volume of the current year by selecting the monthly purchase volume of commodities corresponding to the year;

[0109] The step S4 also includes the following sub-steps:

[0110] Step S41, obtaining the monthly purchase commodity volume and fluctuation coefficient corresponding to the selected year, and obtaining the difference order and lag order of each month in the selected year;

[0111] Step S42, obtain the difference order of each month of the selected year, add and average them to obtain the annual difference order of the selected year, and similarly, obtain the annual lag order of the selected year;

[0112] Step S43, substitute the monthly purchase quantity, fluctuation coefficient BD, annual difference order CF and annual lag order ZH into the forecasting model to obtain the forecasted monthly purchase quantity of the current year;

[0113] The code for the prediction model establishment process is as follows:

[0114]

[0115]

[0116] Step S5, calculating the forecast accuracy value corresponding to the forecast monthly purchase volume;

[0117] The step S5 further comprises the following sub-steps:

[0118] Step S51, obtaining the predicted monthly purchase volume corresponding to the current year, obtaining the selected purchase data corresponding to the selected year after elimination, and then obtaining the compared purchase data corresponding to the year before the selected year, that is, the first compared year;

[0119] Step S52, calculate the purchase difference value CCY between the current year and the selected year by using a formula, the specific formula is as follows:

[0120] Among them, i is the number of the month, YCGi represents the predicted monthly purchase volume of the i-th month, and XCGi represents the monthly purchase volume of the i-th month in the selected year;

[0121] Step S53, calculate the standard purchase difference value BCY corresponding to the selected year through the formula, the specific formula is as follows:

[0122] In the formula, ECGi represents the monthly purchase volume of goods in the i-th month of the first comparison year;

[0123] Step S54: if the value of the standard purchase difference value is zero, the purchase difference value between the current year and the selected year is recorded as the forecast accuracy value; if the value of the standard purchase difference value is not zero, the forecast accuracy value is obtained by dividing the purchase difference value by the standard purchase difference value; the closer the forecast accuracy value is to one, the more accurate the result of forecasting the monthly purchase volume is;

[0124] Step S55, sending the predicted monthly purchase volume and the predicted accuracy value to the user terminal, the user receives the predicted monthly purchase volume, and obtains the accuracy of the prediction through the predicted accuracy value;

[0125] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0126] Embodiment 2: Figure 5 An example of a structural schematic diagram of an electronic device is provided, and the electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call the logic instructions in the memory to execute an e-commerce procurement method for an electric power company, and the method includes: obtaining the historical procurement data and historical inventory data of the electric power company; analyzing the historical procurement data and historical inventory data of the electric power company to obtain the fluctuation coefficient corresponding to the selected year; calculating the difference order and lag order of the monthly purchase quantity of the selected year; predicting the predicted monthly purchase quantity corresponding to the current year by the monthly purchase quantity of the selected year; and calculating the predicted accuracy value corresponding to the predicted monthly purchase quantity.

[0127] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0128] Example 3: The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute an e-commerce procurement method for an electric power company provided by the above methods, the method including: obtaining the historical procurement data and historical inventory data of the electric power company; analyzing the historical procurement data and historical inventory data of the electric power company to obtain the fluctuation coefficient corresponding to the selected year; calculating the difference order and lag order of the monthly procurement quantity of goods corresponding to the selected year; predicting the predicted monthly procurement quantity corresponding to the current year by the monthly procurement quantity corresponding to the selected year; and calculating the prediction accuracy value corresponding to the predicted monthly procurement quantity.

[0129] Example 4: The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the above-mentioned e-commerce procurement method for an electric power company, the method comprising: obtaining the historical procurement data and historical inventory data of the electric power company; analyzing the historical procurement data and historical inventory data of the electric power company to obtain the coefficient of fluctuation corresponding to the selected year; calculating the difference order and lag order of the monthly procurement quantity of goods corresponding to the selected year; predicting the predicted monthly procurement quantity corresponding to the current year by the monthly procurement quantity corresponding to the selected year; and calculating the prediction accuracy value corresponding to the predicted monthly procurement quantity.

[0130] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0131] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An e-commerce purchasing method for an electric power company, characterized in that: Methods include: Step S1, obtaining the historical purchasing data and historical inventory data of the power company; Step S2, analyzing the historical purchasing data and historical inventory data of the power company to obtain the fluctuation coefficient corresponding to the selected year; Wherein, the step S2 includes the following sub-steps: Step S21, obtaining the historical purchase data corresponding to the power company, and obtaining the monthly purchase volume of goods by the power company in the collection year; Step S22, obtaining the historical inventory data corresponding to the power company, obtaining the inventory quantity corresponding to the goods purchased by the power company in the collection year, and recording the inventory quantity at the end of the year as the annual inventory quantity of the corresponding year; Step S23, adding up the monthly purchase quantities of goods in different months of the same year to obtain the annual purchase quantity of the corresponding year; Compare the annual inventory and annual purchase volume corresponding to the same year. If the annual inventory is greater than or equal to the correction coefficient multiplied by the annual purchase volume, the historical purchase data of the corresponding year will be discarded. If the annual inventory is less than the correction coefficient multiplied by the annual purchase volume, no operation will be performed. Step S24, record the year before the current year as the selected year, record the monthly purchase volume of the corresponding purchased goods in the selected year as the selected purchase data, record the five years before the selected year as the comparison year, record the monthly purchase volume of the corresponding purchased goods in the comparison year as the comparison purchase data, and proceed to the next step; Step S25, identifying and removing abnormal values ​​corresponding to the monthly purchase quantity of goods in the selected purchase data; Step S3, calculating the difference order and lag order corresponding to the purchase quantity of goods in the corresponding month of the selected year; Wherein, the step S3 includes the following sub-steps: Step S31, obtaining the monthly purchase commodity volume in the selected year after elimination and the monthly purchase commodity volume in the comparison year, and generating the purchase commodity of the corresponding month; Step S32, performing a differential operation on the purchased commodity sequence to obtain a first-order difference sequence of the purchased commodity sequence; Step S33, performing a difference operation on the first-order difference sequence to obtain a second-order difference sequence corresponding to the purchased commodity sequence, and repeating the difference process until the difference sequence corresponding to the purchased commodity sequence has only one element left, and the order of the corresponding difference sequence is recorded as the difference order of the corresponding month; Step S34, performing a lagged difference operation on the purchased commodity sequence to obtain a first-order lagged difference sequence of the purchased commodity sequence; Step S35, performing a lagged difference operation on the first-order lagged difference sequence to obtain a second-order lagged difference sequence corresponding to the purchased goods sequence; repeatedly performing the lagged difference operation until no lagged difference operation can be performed, and recording the order of the corresponding lagged difference sequence as the lagged order of the corresponding month; Step S4, predicting the predicted monthly purchase volume of the current year by selecting the monthly purchase volume of commodities corresponding to the year; Wherein, the step S4 includes the following sub-steps: Step S41, obtaining the monthly purchase commodity volume and fluctuation coefficient corresponding to the selected year, and obtaining the difference order and lag order of each month in the selected year; Step S42, obtain the difference order of each month of the selected year, add and average them to obtain the annual difference order of the selected year, and similarly, obtain the annual lag order of the selected year; Step S43, substitute the monthly purchase quantity, fluctuation coefficient BD, annual difference order CF and annual lag order ZH into the forecasting model to obtain the forecasted monthly purchase quantity of the current year; Step S5, calculating the forecast accuracy value corresponding to the forecast monthly purchase volume.

2. The e-commerce purchasing method for electric power companies according to claim 1, characterized in that: The historical procurement data specifically refers to the monthly purchase volume of commodities by the power company in the year of collection; The historical inventory data refers to the inventory of goods of the power company in the collection year, and the collection year is recorded from the year before the current year onwards.

3. The e-commerce purchasing method for electric power companies according to claim 1, characterized in that: The step S25 includes the following sub-steps: Step S251, adding up the monthly purchase commodity quantities corresponding to the i-th month of different comparison years and taking the average value to obtain the average monthly purchase commodity quantity PJSi of the i-th month of all comparison years; Step S252, calculate the standard deviation BCGi of the purchased goods of the power company in the i-th month of the comparison year by the formula, the specific formula is as follows: Wherein, j is the number of the comparison year, j = 1, 2, ..., z, z is a positive integer; LCGji is the monthly purchase volume of the i-th month in the comparison year j; Step S253, calculate the data offset value SPYi of the i-th month in the selected year by a formula, the specific formula is as follows: SPYi=|XCGi-PJSi| / BCGi; where XCGi is the monthly purchase volume of the i-th month in the selected year; Step S254, repeatedly executing steps S251 to S253 until the data offset values ​​of the twelve months corresponding to the selected year are calculated; Step S255, compare the data offset value of each month in the selected year with the standard offset threshold. If the data offset value is less than or equal to the standard offset threshold, no operation is performed. If the data offset value is greater than the standard offset threshold, the monthly purchase volume of the corresponding month of the selected year is deemed to be an abnormal value, and the monthly purchase volume of the corresponding month of the selected year is eliminated, and the value of the fluctuation coefficient is increased by one.

4. The e-commerce purchasing method for electric power companies according to claim 1, characterized in that: The differential operation of step S32 is specifically as follows: Get the purchase commodity sequence CGXi = {CGXi1, CGXi2, CGXi3, CGXi4, CGXi5, CGXi6}, where i represents the month, i = 1, 2, ..., 12; The first element corresponding to the first-order difference sequence is obtained by calculating CGXi2-CGXi1; The second element corresponding to the first-order difference sequence is obtained by calculating CGXi3-CGXi2; And so on, until the last element corresponding to the first-order difference sequence is obtained through CGXi6-CGXi5, and the elements corresponding to the first-order difference sequence are combined to obtain the first-order difference sequence corresponding to the purchased goods sequence: {CGXi2-CGXi1, CGXi3-CGXi2, CGXi4-CGXi3, CGXi5-CGXi4, CGXi6-CGXi5}.

5. The e-commerce purchasing method for electric power companies according to claim 4 is characterized in that: The delayed differential operation of step S34 is specifically as follows: Get the purchase commodity sequence CGXi = {CGXi1, CGXi2, CGXi3, CGXi4, CGXi5, CGXi6}; Perform lagged difference calculation, that is, when performing difference operation on the purchased goods sequence, use the separated elements for subtraction; The first element corresponding to the first-order lagged difference sequence is obtained by calculating CGXi3-CGXi1; The second element corresponding to the first-order lagged difference sequence is obtained by calculating CGXi4-CGXi2; And so on, until the lagged difference calculation cannot be performed, the corresponding first-order lagged difference sequence is: {CGXi3-CGXi1, CGXi4-CGXi2, CGXi5-CGXi3, CGXi6-CGXi4}.

6. The e-commerce purchasing method for electric power companies according to claim 1, characterized in that: The step S5 comprises the following sub-steps: Step S51, obtaining the predicted monthly purchase volume corresponding to the current year, obtaining the selected purchase data corresponding to the selected year after elimination, and then obtaining the compared purchase data corresponding to the year before the selected year, that is, the first compared year; Step S52, calculate the purchase difference value CCY between the current year and the selected year by using a formula, the specific formula is as follows: In the formula, i is the number of the month, YCGi represents the predicted monthly purchase volume of the i-th month, and XCGi represents the monthly purchase volume of the i-th month in the selected year; Step S53, calculate the standard purchase difference value BCY corresponding to the selected year through the formula, the specific formula is as follows: In the formula, ECGi represents the monthly purchase volume of goods in the i-th month of the first comparison year; Step S54: if the value of the standard purchase difference value is zero, the purchase difference value between the current year and the selected year is recorded as the forecast accuracy value; if the value of the standard purchase difference value is not zero, the forecast accuracy value is obtained by dividing the purchase difference value by the standard purchase difference value; Step S55, sending the predicted monthly purchase volume and the predicted accuracy value to the user terminal, the user receives the predicted monthly purchase volume, and obtains the accuracy of the prediction through the predicted accuracy value.

7. The e-commerce purchasing method for electric power companies according to claim 6, characterized in that: The closer the forecast accuracy value is to 1, the more accurate the result of forecasting the monthly purchase volume is.

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