CN112862605B

CN112862605B - Enterprise operation abnormity prompting method and system based on electricity consumption data

Info

Publication number: CN112862605B
Application number: CN202110454357.7A
Authority: CN
Inventors: 蔡高琰; 陈声荣; 梁炳基; 文享龙; 陈迪; 林江渚
Original assignee: Guangdong Haodi Zhiyun Technology Co ltd
Current assignee: Guangdong Haodi Zhiyun Technology Co ltd
Priority date: 2021-04-26
Filing date: 2021-04-26
Publication date: 2022-06-24
Anticipated expiration: 2041-04-26
Also published as: CN112862605A

Abstract

The invention provides an enterprise operation abnormity reminding method and system based on electricity utilization data, wherein electricity utilization characteristic data of an enterprise are obtained; acquiring the operation state information of the enterprise according to the electricity utilization characteristic data; calculating an abnormal judgment value of the enterprise according to the operation state information; sending corresponding reminding information according to the abnormal judgment value; therefore, the financial institution can know whether the operation of the enterprise is abnormal or not in time according to the reminding information, and the financial institution can find the abnormal operation condition of the enterprise more timely.

Description

Enterprise operation abnormity prompting method and system based on electricity consumption data

Technical Field

The invention relates to the technical field of data analysis, in particular to an enterprise operation abnormity reminding method and system based on electricity consumption data.

Background

In recent years, loan inclination of each large financial institution to medium and small enterprises is gradually increased, but the operation defense risk capability of the medium and small enterprises is weak, the operation development of the enterprises has a lot of uncertainty, the production and operation information of the enterprises is not public and opaque, and great difficulty is brought to the financial institutions to master the operation information of the enterprises.

The financial institution needs to guarantee the safety of the loan, and must timely and accurately master the business information of the enterprise so as to timely avoid the business risk of the enterprise and guarantee the safety of the loan. Currently, a financial institution mainly obtains production and operation information of an enterprise through the following channels: 1. production and management monthly financial reports provided by enterprises; 2. enterprise tax bills for tax departments; 3. fund flow information of the enterprise bank account; 4. paying bills of enterprise electric charge; 5. enterprise public information inquired through a professional website; 6. and the client manager visits the enterprise on the spot after the loan, and the like.

The enterprise information acquired by the financial institution through the channels has the following problems: 1. the provided monthly financial report of production and management has the problems of information lag, excessively sparse production and management information, difficulty in checking and verifying false accounts in the production and management information and the like; 2. the enterprise tax bill has the problems of information lag, mismatching of tax return time and generation and operation time, weak relevance of tax information and production and operation condition information and the like; 3. the bank flow information is difficult to directly judge the daily production and operation state of an enterprise, and the bank flow is easy to be constructed in an account walking mode; 4. the electricity bill acquisition period of an enterprise is usually one month or more, so the current operation state of the enterprise cannot be reflected by the electricity bill; 5. enterprise public information inquired through a professional website is more difficult to judge the authenticity of the information; 6. after the client manager visits the enterprise on the spot, the client manager can only see the daily operation representation of the enterprise, and cannot deeply and accurately know the real operation state of the enterprise.

Therefore, the financial institution is difficult to find the abnormal operation condition of the enterprise in time due to the problems of the channel for mastering the enterprise operation information and the problem of low working efficiency when the acquired information is not objective, inaccurate and timely, and the loan loss is easy to be large once the abnormal operation condition of the enterprise is found by the financial institution.

Disclosure of Invention

In view of the defects of the prior art, the embodiments of the present application provide a method and a system for reminding an abnormal operation of an enterprise based on electricity consumption data, which is beneficial for a financial institution to find the abnormal operation of the enterprise more timely.

In a first aspect, an embodiment of the present application provides an enterprise operation exception reminding method based on power consumption data, including the steps of:

A1. acquiring power utilization characteristic data of an enterprise; the electricity utilization characteristic data comprises at least one of electricity consumption, electricity utilization load, current, voltage, electricity utilization duration, active power, reactive power and power factor;

A2. acquiring the operation state information of the enterprise according to the electricity utilization characteristic data;

A3. calculating an abnormal judgment value of the enterprise according to the operation state information;

A4. and sending corresponding reminding information according to the abnormal judgment value.

In the method for reminding enterprise operation abnormity based on electricity consumption data, step a1 includes:

collecting power utilization characteristic data according to a preset sampling period;

executing each time the electricity utilization characteristic data is collected:

calculating the average value of the power utilization characteristic data in the current processing period; the processing period refers to a time period with a preset length before the current moment;

judging whether the following formula is satisfied:

；

wherein t is the time corresponding to the currently acquired electricity utilization characteristic data,

for the ith kind of electricity utilization characteristic data collected currently,

the average value of the ith electricity utilization characteristic data in the current processing period is shown, and K is a preset threshold value;

and if so, rejecting the currently acquired electricity utilization characteristic data, and replacing the currently acquired electricity utilization characteristic data by the mean value of a plurality of previous electricity utilization characteristic data sampling values.

In the method for reminding enterprise operation abnormity based on electricity consumption data, the operation state information comprises at least one of working time length information, peak electricity load information, periodic electricity consumption information, equipment operation rate information and personnel arrival rate information.

In some embodiments, the electricity usage characteristic data includes electricity usage; the operation state information comprises periodic power consumption information;

step a2 includes:

the periodic power usage in each calculation period is calculated according to the following formula:

;

wherein, the first and the second end of the pipe are connected with each other,

for the periodic power usage in the current calculation period,

for the periodic power usage in the last calculation period,

is the sampled value of the power usage at the current sampling time,

sampling values of the electricity consumption at the sampling time of the calculation period at an interval before the current sampling time, wherein N is the number of sampling periods contained in each calculation period.

In some embodiments, step a3 includes:

inquiring in a basic abnormity judgment value inquiry table according to the operation state information to obtain basic abnormity judgment values corresponding to various operation state information;

and calculating a weighted average value of the basic abnormality judgment values of the various operation state information as an effective abnormality judgment value.

In some embodiments, after step A3 and before step a4, the method further comprises the steps of:

acquiring the information of the popularity of the corresponding industry according to the information of the operation state of each enterprise in the same industry;

and correcting the abnormal judgment value according to the information of the popularity.

Further, the step of correcting the abnormality judgment value according to the popularity information includes:

calculating to obtain a first correction coefficient according to the information of the popularity;

and multiplying the first correction coefficient by the abnormality judgment value to obtain a corrected abnormality judgment value.

acquiring unit capacity energy consumption data/unit sales energy consumption data of the enterprise and average unit capacity energy consumption data/average unit sales energy consumption data of corresponding industries;

and correcting the abnormal judgment value according to the unit productivity energy consumption data/unit sales energy consumption data of the enterprise and the average unit productivity energy consumption data/average unit sales energy consumption data of the corresponding industry.

In the enterprise operation abnormity reminding method based on the electricity utilization data, the electricity utilization characteristic data comprises at least one of total electricity utilization characteristic data and electricity utilization characteristic data of key equipment.

In some embodiments, the power usage characteristic data includes total power usage characteristic data and power usage characteristic data of key devices;

step a2 includes:

acquiring first operation state information of the enterprise according to the total electricity utilization characteristic data;

acquiring second operation state information of the enterprise according to the electricity utilization characteristic data of the key equipment;

and acquiring final operation state information according to the first operation state information and the second operation state information.

In a second aspect, an embodiment of the application provides an enterprise operation abnormity reminding system based on power consumption data, which comprises an enterprise power consumption data acquisition device, a cloud data acquisition and storage center, a power consumption data analysis and processing system and an enterprise operation risk analysis display and alarm system;

the enterprise electricity utilization data acquisition device is used for acquiring electricity utilization characteristic data of an enterprise and uploading the electricity utilization characteristic data to the cloud data acquisition and storage center;

the cloud data acquisition and storage center is used for storing the electricity utilization characteristic data;

the power utilization data analysis and processing system is used for acquiring power utilization characteristic data of an enterprise from the cloud data acquisition and storage center, acquiring operation state information of the enterprise according to the power utilization characteristic data, calculating an abnormal judgment value of the enterprise according to the operation state information, and sending corresponding reminding information to the enterprise operation analysis display and warning system according to the abnormal judgment value;

and the enterprise operation risk analysis display and alarm system is used for displaying the reminding information.

Has the advantages that:

according to the enterprise operation abnormity reminding method and system based on the electricity utilization data, the electricity utilization characteristic data of an enterprise are obtained; acquiring the operation state information of the enterprise according to the electricity utilization characteristic data; calculating an abnormal judgment value of the enterprise according to the operation state information; sending corresponding reminding information according to the abnormal judgment value; therefore, the financial institution can know whether the operation of the enterprise is abnormal or not in time according to the reminding information, and the financial institution can find the abnormal operation condition of the enterprise more timely; through real-time measurement and the power consumption data information of gathering enterprise, the power consumption information of enterprise that acquires is true reliable, high frequency high speed, and the power consumption data of enterprise is closely related with enterprise's operation conditions, and is difficult to make fake, is favorable to more accurately, discovers the abnormal condition of enterprise more in real time.

Drawings

Fig. 1 is a flowchart of an enterprise operation abnormality reminding method based on electricity consumption data according to an embodiment of the present application.

Fig. 2 is a schematic structural diagram of an enterprise operation abnormality reminding system based on electricity consumption data according to an embodiment of the present application.

FIG. 3 is a schematic diagram of calculating the periodic power usage by a sliding window approach.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

The following disclosure provides embodiments or examples for implementing different configurations of the present invention. To simplify the disclosure of the present invention, specific example components and arrangements are described below. Of course, they are merely examples and are not intended to limit the present invention. Moreover, the present invention may repeat reference numerals and/or reference letters in the various examples, which have been repeated for purposes of simplicity and clarity and do not in themselves dictate a relationship between the various embodiments and/or configurations discussed. In addition, the present invention provides examples of various specific processes and materials, but those of ordinary skill in the art will recognize applications of other processes and/or uses of other materials.

Referring to fig. 1, an enterprise operation abnormality reminding method based on electricity consumption data provided in an embodiment of the present application includes the steps of:

A1. acquiring power utilization characteristic data of an enterprise; the electricity characteristic data comprises at least one of (but not limited to) electricity consumption, electricity load, current, voltage, electricity duration, active power, reactive power and power factor;

In some embodiments, the power consumption data-based enterprise operation abnormity prompting method can be applied to the power consumption data analysis processing system 3 of the power consumption data-based enterprise operation abnormity prompting system shown in fig. 2; the enterprise power utilization data acquisition device 1 can be arranged in an enterprise power supply system to acquire electrical characteristic data of an enterprise (the enterprise power utilization data acquisition device can comprise intelligent electric meters, wherein the number and the installation position of the intelligent electric meters can be set according to actual needs, for example, the intelligent electric meters can be arranged on a total power supply line of the enterprise to acquire the total electrical characteristic data, the intelligent electric meters can also be arranged on a power supply line of key equipment of the enterprise to acquire the electrical characteristic data of the key equipment), data acquisition can be carried out according to a preset acquisition cycle, and the acquisition cycle can be set according to actual needs (so that high-frequency and high-speed acquisition can be realized); in step a4, a warning message is sent to the enterprise operation risk analysis display and warning system 4, wherein the enterprise operation analysis display and warning system 4 may include at least one of a computer deployed at a financial institution and a mobile terminal of a financial institution administrator, and the enterprise operation analysis display and warning system may display the warning message after receiving the warning message, so that the financial institution administrator can know whether the operation of the enterprise is abnormal in time.

The electricity utilization characteristic data comprises at least one of electricity consumption, electricity utilization load, current, voltage, electricity utilization duration, active power, reactive power and power factor. Because the electricity utilization characteristic data is real and reliable, can be collected at high frequency and high speed, is closely related to the business conditions of enterprises, and carries out analysis and calculation on the abnormal conditions of the enterprises through the electricity characteristic data, the enterprises are difficult to counterfeit, and the financial institutions are favorable for knowing the business conditions of the enterprises more real-timely, accurately and truly.

Generally, when the power utilization characteristic data of an enterprise are obtained, the power utilization characteristic data are collected according to a preset sampling period, the specific sampling period can be set as required, high-frequency and high-speed collection is facilitated by adjusting the sampling period, and the financial institution is facilitated to solve the business condition of the enterprise in real time. Preferably, in order to avoid misjudgment of the business operation state caused by large distortion interference of the outlier to the electricity consumption data statistics, the collected electricity consumption feature data may be subjected to outlier elimination processing, so that, in some preferred embodiments, step a1 includes:

judging whether the following formula is satisfied:

；

Preferably, the operation state information includes at least one of working duration information, peak power load information, periodic power consumption information, operation rate information of the equipment, and staff arrival rate information.

In some embodiments, the method for reminding the enterprise operation abnormity based on the electricity consumption data further comprises the following steps:

and generating and sending the running state display information according to the operation state information.

For the condition that the power consumption data-based enterprise operation abnormity reminding method is applied to the power consumption data analysis processing system 3 of the power consumption data-based enterprise operation abnormity reminding system shown in fig. 2, the operation state display information is sent to the enterprise operation analysis display and warning system 4, so that the enterprise operation analysis display and warning system 4 can display the operation state display information. The method provides a basis for the financial institution to monitor and analyze the conditions of the capacity saturation, the equipment operating rate and the like of the enterprise according to the information, so that the financial institution can find out sudden abnormal conditions of sudden closing, escaping and moving of the enterprise and the like in time.

The working duration information, the peak power load information, the power load information and the operating rate of the equipment can be directly obtained by identification according to the change curves of power utilization characteristic data such as power loads, currents, voltages and the like; for example, the working duration information is obtained by subtracting the working duration from the working duration by taking the time when the power load is changed from being lower than the preset power load threshold value to being higher than the preset power load threshold value as the working duration, and taking the time when the power load is changed from being higher than the preset power load threshold value to being lower than the preset power load threshold value as the working duration; the actual acquired electric load is divided by the average electric load of the single equipment (measured and recorded in advance) to obtain the number of equipment for starting operation, and then the number of the equipment for starting operation is divided by the total number of the equipment (recorded in advance) to obtain the operation rate of the equipment. For an enterprise (for example, a software development company, the production equipment of which is a computer, and the number of the computer is generally equal to that of the software developers) in which the equipment number corresponds to the staff number, the staff arrival rate can be calculated under the condition that the operation rate of the equipment is obtained.

The real-time collected power consumption is the power consumption in each sampling period, and if the sampling period is 1min, the power consumption collected each time is the total power consumption in 1min before the current moment; and the periodic electricity consumption is the electricity consumption in a preset calculation period, if the calculation period is 1h, the periodic electricity consumption at each sampling moment is the total electricity consumption 1h before the current moment, and the value of the periodic electricity consumption is equal to the sum of all the electricity consumption sampling values acquired 1h before the current moment. Thus, the periodic power usage may be calculated in a sliding window manner based on the power usage (as shown in FIG. 3), and in some preferred embodiments, the power usage characteristic data includes the power usage; the operation state information comprises periodic power consumption information;

step a2 includes:

the periodic power usage during each calculation period is calculated according to the following formula:

;

wherein the content of the first and second substances,

for the periodic power usage in the current calculation period,

for the periodic power usage in the last calculation period,

the sampled value of the used electricity amount at the current sampling moment,

and sampling the electricity consumption at the sampling time of the calculation period at the interval before the current sampling time, wherein N is the number of sampling periods contained in each calculation period.

In some embodiments, corresponding basic anomaly determination values can be assigned to different value ranges of each business state information in advance and a basic anomaly determination value lookup table can be formed (for example, for the operation rate information of the equipment, 0% -100% can be divided into five value ranges of [0%, 20%), [20%, 40%), [40%, 60%), [60%, 80%), [80%, 100%) and corresponding basic anomaly determination values can be assigned to the five value ranges: x1, x2, x3, x4 and x5, and when the value of the actually acquired operation rate information falls into a certain value range, the basic abnormality judgment value takes a corresponding value); step a3 includes:

A301. inquiring in the basic abnormality judgment value inquiry table according to the operation state information to obtain basic abnormality judgment values corresponding to various operation state information;

A302. and calculating a weighted average value (wherein the weight is preset) of basic abnormal judgment values of various business state information as an effective abnormal judgment value.

The abnormity judgment value obtained in the mode can more accurately reflect the abnormal operation condition of the enterprise, so that the financial institution can more accurately know the abnormal operation condition of the enterprise.

In fact, the method is not limited to the method for calculating the abnormal judgment value of the enterprise; for example, in other embodiments, a corresponding basic abnormality determination value calculation model may be preset for each business state information, and in step a3, each business state information is pre-input into the corresponding basic abnormality determination value calculation model to obtain a basic abnormality determination value for each business state information, and then a weighted average value (where a weight is preset) of the basic abnormality determination values for each business state information is calculated as an effective abnormality determination value.

In some embodiments, in step a4, the reminder information includes the abnormality determination value itself; for example, the reminding information is "the abnormal judgment value of the enterprise is X", wherein X is the abnormal judgment value. The reminding information can also comprise character color information, so that when the enterprise operation analysis display and warning system 4 displays the reminding information, the corresponding character color is used for displaying the characters so as to attract the attention of managers; for example, the character color is green when the abnormality judgment value is smaller than the first preset value, the character color is yellow when the abnormality judgment value is between the first preset value and the second preset value, and the character color is red when the abnormality judgment value is larger than the second preset value; but is not limited thereto.

In other embodiments, in step a4, the reminding information is preset warning statement information corresponding to the abnormal judgment value (the specific content of the preset warning statement may be set according to actual needs); for example, the preset warning statement information includes three types, and when the abnormality judgment value is smaller than the first preset value, the first type of preset warning statement information is called as the reminding information to be sent, when the abnormality judgment value is between the first preset value and the second preset value, the second type of preset warning statement information is called as the reminding information to be sent, and when the abnormality judgment value is larger than the second preset value, the third type of preset warning statement information is called as the reminding information to be sent; but is not limited thereto.

In a further embodiment, in step a4, the reminding information is preset image information corresponding to the abnormality judgment value (the specific content of the preset image can be set according to actual needs); for example, the preset image information includes three types, the first type of preset image information is called to be sent as reminding information when the abnormality judgment value is smaller than the first preset value, the second type of preset image information is called to be sent as reminding information when the abnormality judgment value is between the first preset value and the second preset value, and the third type of preset image information is called to be sent as reminding information when the abnormality judgment value is larger than the second preset value; but is not limited thereto.

In fact, the business situation of an enterprise can be influenced by the landscape situation of the whole industry, if the industry where the enterprise is located is not landscape, even if the enterprise does not have an abnormal situation at present, the probability of the subsequent abnormal situation is greatly increased, therefore, if the landscape situation of the industry is considered when the abnormal judgment value is calculated, the judgment result of the abnormal situation of the enterprise is more scientific, and the scientificity and the practicability of the enterprise business abnormality reminding method based on the power consumption data can be improved. Thus, in some preferred embodiments, after step A3 and before step a4, the method further comprises the steps of:

A5. acquiring the information of the popularity of the corresponding industry according to the information of the operation state of each enterprise in the same industry;

A6. and correcting the abnormal judgment value according to the information of the popularity.

The power utilization characteristic data of a plurality of enterprises in the same industry can be acquired to acquire the operation state information of the enterprises (refer to steps A1 and A2), and the information of the business prospect degree can be acquired according to the operation state information of each enterprise in the same industry.

For example, the corresponding business value lookup table may be formed by allocating corresponding business data values to different ranges of different business status information in advance, after the business status information of each business enterprise is identified, the corresponding business value may be obtained by querying in the business value lookup table, then the total business data value (sum of the business data values of various business status information) of each business enterprise may be calculated, and then the average value of the business data total values corresponding to each business enterprise may be calculated, so as to obtain the business information of the corresponding business.

For another example, whether the operation state of each business enterprise is good or not can be judged according to the deviation of the current operation state information of each business enterprise and the operation state information of the last preset period (the preset period is 1 day, one week, one month, one quarter, one year and the like) (for example, the judgment is carried out according to the positive and negative of the deviation, if the deviation is 0 or regular, the operation state is good, and if the deviation is negative, the operation state is poor, or the judgment is carried out according to whether the deviation is greater than a preset deviation threshold value, the operation state is good or not, otherwise, the operation state is poor); if the business state information comprises a plurality of kinds of business state information, whether the business state of a same-industry enterprise is good or not is judged according to each kind of business state information, and whether the times y1 that the business state is good or the times y2 that the business state is poor is larger is judged, if the times y1 that the business state is good is larger than the times y2 that the business state is poor is finally judged, if the times y1 that the business state is good is smaller than the times y2 that the business state is poor, the business state of the same-industry enterprise is finally judged to be poor, if the times y1 that the business state is good is equal to the times y2 that the business state is poor, the judgment result when the judgment is carried out by using the pre-designated main business state information is used as the final judgment result (for example, the main business state information is working time length information, when y1= y2, the operation state is finally judged to be good if the operation state is judged to be good through the working time length information, and the operation state is finally judged to be poor if the operation state is judged to be poor through the working time length information);

then calculating the prosperity information of the corresponding industry according to the proportion of the quantity of the enterprises in the same industry with good operation state in the total quantity of the enterprises in the same industry; for example, the ratio may be calculated by substituting it into a preset calculation formula; or distributing corresponding information of the popularity for different proportion ranges in advance, and then obtaining the corresponding information of the popularity according to the proportion range in which the proportion obtained actually falls.

Further, a6, the step of correcting the abnormality judgment value according to the popularity information includes:

Wherein, when the industry is less scenic (i.e. the information of the scenery degree is smaller), the business risk of the enterprise is larger, and therefore, the first correction coefficient should be larger. For example, the first correction coefficient may be calculated by substituting the information of the popularity into a preset first correction coefficient calculation formula, but is not limited thereto.

In fact, if the technical level and the management level of an enterprise are higher, the production cost of the unit capacity is lower, so that the competitiveness of the enterprise is stronger, the possibility of the enterprise generating the abnormal operation condition is smaller, therefore, if the cost of the unit capacity can be considered when calculating the abnormal judgment value, the judgment result of the abnormal operation condition of the enterprise is more scientific, and the scientificity and the practicability of the method for reminding the abnormal operation condition of the enterprise based on the electricity consumption data can be improved. Thus, in some preferred embodiments, after step A3 and before step a4, the method further comprises the steps of:

A7. acquiring unit capacity energy consumption data of the enterprise and average unit capacity energy consumption data of corresponding industries;

A8. and correcting the abnormal judgment value according to the unit productivity energy consumption data of the enterprise and the average unit productivity energy consumption data of the corresponding industry.

The capacity data of the enterprise can be uploaded regularly by the enterprise (generally, the capacity data and the power consumption data of the enterprise and a plurality of enterprises in the same industry in preset periods can be obtained in step a7, then the unit capacity energy consumption data of the enterprise and each enterprise in the same industry can be obtained by dividing the capacity data by the corresponding power consumption data, and then the average value of the unit capacity energy consumption data of the enterprise and each enterprise in the same industry is calculated as the average unit capacity energy consumption data of the corresponding industry.

In some embodiments, step A8 includes: calculating the difference value between the unit capacity energy consumption data of the enterprise and the average unit capacity energy consumption data of the corresponding industry; calculating a second correction coefficient according to the difference (which is the difference between the unit productivity energy consumption data of the enterprise and the average unit productivity energy consumption data of the corresponding industry); and multiplying the second correction coefficient by the abnormality judgment value to obtain a corrected abnormality judgment value. The larger the difference between the unit capacity energy consumption data of the enterprise and the average unit capacity energy consumption data of the corresponding industry is, the lower the electricity consumption cost of the unit capacity of the enterprise is, the higher the technical level and the management level are, the lower the operation risk of the enterprise is, and the smaller the second correction coefficient is.

In fact, the unit sales energy consumption of an enterprise can also reflect the technical level and the management level of an enterprise, and the lower the unit sales energy consumption is, the higher the technical level and the management level are; if the cost of unit sales can be considered when calculating the abnormal judgment value, the judgment result of the abnormal condition of the enterprise is more scientific, and the scientificity and the practicability of the enterprise operation abnormity reminding method based on the electricity consumption data can be improved. Thus, in some preferred embodiments, after step A3 and before step a4, the method further comprises the steps of:

A9. acquiring unit sales energy consumption data of the enterprise and average unit sales energy consumption data of corresponding industries;

A10. and correcting the abnormal judgment value according to the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry.

The sales data of the enterprise can be uploaded by the enterprise regularly (generally, the sales data are uploaded in the form of operation reports such as weekly operation reports, monthly operation reports, quarterly operation reports, annual operation reports and the like), so that in step a9, the sales and power consumption data of the enterprise and a plurality of enterprises in the same industry in a preset period can be obtained, then the sales is divided by the corresponding power consumption data to obtain unit sales energy consumption data of the enterprise and each enterprise in the same industry, and then the average value of the unit sales energy consumption data of the enterprise and each enterprise in the same industry is calculated to be used as the average unit sales energy consumption data of the corresponding industry.

In some embodiments, step a10 includes: calculating the difference value between the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry; calculating a third correction coefficient according to the difference (the difference between the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry); and multiplying the third correction coefficient by the abnormality judgment value to obtain a corrected abnormality judgment value. The larger the difference between the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry is, the lower the electricity consumption cost of the unit sales of the enterprise is, the higher the technical level and the management level are, the lower the operation risk of the enterprise is, and the smaller the third correction coefficient is.

In practical application, in addition to the total electricity consumption data of the enterprise, the electricity consumption data of the key equipment (for example, for a productive enterprise, the production equipment is the key equipment, for some enterprises mainly working with computers, such as a software development enterprise, the office computer is the key equipment) can well reflect the production and operation conditions of the enterprise, so that the operation abnormal condition analysis can be performed only by using the total electricity consumption characteristic data of the enterprise, the operation abnormal condition analysis can be performed only by using the electricity characteristic data of the key equipment, or the comprehensive operation abnormal condition analysis can be performed by using the total electricity consumption characteristic data and the electricity characteristic data of the key equipment. That is, the electricity usage characteristic data includes at least one of total electricity usage characteristic data and electricity usage characteristic data of key equipment.

In some preferred embodiments, the power usage characteristic data comprises total power usage characteristic data and power usage characteristic data of key equipment;

thus, step a2 includes:

The operation state information respectively obtained by the total power utilization characteristic data and the power utilization characteristic data of the key equipment is compared and verified mutually, so that the accuracy of judging the abnormal production and operation conditions of enterprises can be improved.

The specific method for acquiring the first operation state information of the enterprise according to the total power consumption characteristic data and acquiring the second operation state information of the enterprise according to the power consumption characteristic data of the key equipment can refer to the method described above.

The step of acquiring final business status information according to the first business status information and the second business status information may include: taking the poorer business state in the first business state information and the second business state information as final business state information (if the business state information comprises a plurality of kinds of business state information, the step is carried out on each kind of business state information); the mode is adopted for processing, and the timeliness of finding abnormal production and operation conditions of enterprises is better facilitated. Specifically, the larger the basic abnormality determination value corresponding to the operation state information is, the worse the operation state is. The step of acquiring final business status information according to the first business status information and the second business status information may also include: calculating average operation state information of the first operation state information and the second operation state information as final operation state information (if a plurality of operation state information is included, the step is carried out on each operation state information); the method is adopted for processing, and the accuracy of finding abnormal production and operation conditions of enterprises is improved.

According to the method, the power utilization characteristic data of the enterprise are acquired; acquiring the operation state information of the enterprise according to the electricity utilization characteristic data; calculating an abnormal judgment value of the enterprise according to the operation state information; sending corresponding reminding information according to the abnormal judgment value; therefore, the financial institution can know whether the operation of the enterprise is abnormal or not in time according to the reminding information, and the financial institution can find the abnormal operation condition of the enterprise more timely. In particular, the following advantages are provided:

1. the method has the advantages that massive enterprise electricity utilization data information is automatically measured and collected in real time, the obtained enterprise electricity utilization information is real and reliable, high in frequency and speed and multiple in dimensionality, the enterprise electricity utilization data is closely related to enterprise operation conditions and is difficult to counterfeit, and the method is favorable for accurately, real-timely and truly obtaining abnormal states of enterprises;

2. the power utilization data can be acquired in real time at high frequency and high density to acquire the enterprise operation state information such as working duration information, peak power utilization load information, periodic power consumption information, equipment operation rate information and personnel on-duty rate information, so that the capacity saturation and equipment operation rate of the enterprise production and operation can be monitored in real time, and sudden abnormal conditions such as sudden closing of the enterprise, escaping of debt relocation and the like can be found in time;

3. the abnormal judgment numerical calculation is carried out by combining with the industry popularity, the abnormal operation condition of the enterprise can be more accurately pre-judged, the misjudgment of the enterprise operation risk caused by the industry popularity is reduced, and the judgment result of the abnormal operation condition of the enterprise is more scientific and accurate;

4. and the abnormal judgment numerical value calculation is carried out by combining the unit productivity energy consumption and unit sales volume energy consumption data information of the enterprise, so that the judgment result of the abnormal operation condition of the enterprise is more scientific and accurate.

Referring to fig. 2, an enterprise operation abnormality reminding system based on electricity consumption data is further provided in the embodiment of the present application, and includes an enterprise electricity consumption data acquisition device 1, a cloud data acquisition storage center 2, an electricity consumption data analysis processing system 3, and an enterprise operation risk analysis display and alarm system 4;

the enterprise electricity consumption data acquisition device 1 is used for acquiring electricity consumption characteristic data of an enterprise and uploading the electricity consumption characteristic data to the cloud data acquisition and storage center 2;

the cloud data acquisition and storage center 2 is used for storing the electricity utilization characteristic data;

the electricity consumption data analysis processing system 3 is configured to acquire electricity consumption feature data of an enterprise from the cloud data acquisition and storage center 2, acquire operation state information of the enterprise according to the electricity consumption feature data, calculate an abnormality judgment value of the enterprise according to the operation state information, and send corresponding reminding information to the enterprise operation analysis display and warning system 4 according to the abnormality judgment value (specifically, refer to corresponding steps in the above-mentioned electricity consumption data-based enterprise operation abnormality reminding method);

and the enterprise operation risk analysis display and alarm system 4 is used for displaying the reminding information.

Wherein, enterprise power consumption data acquisition device 1 can include smart electric meter, and wherein smart electric meter's quantity and mounted position can set up according to actual need, for example, can set up smart electric meter on the total power supply line of enterprise and gather total power consumption characteristic data, also can set up smart electric meter on the power supply line of enterprise key equipment and gather the power consumption characteristic data of key equipment.

The enterprise operation analysis display and warning system 4 may include at least one of a computer deployed at a financial institution and a mobile terminal of a financial institution manager, and the enterprise operation analysis display and warning system 4 may display the risk reminding information after receiving the risk reminding information; in addition, the electricity consumption data analysis and processing system 3 can also be used for sending the acquired electricity consumption characteristic data to the enterprise operation risk analysis display and alarm system 4, and the enterprise operation risk analysis display and alarm system 4 can display the electricity consumption characteristic data in a data curve mode so as to facilitate analysis by managers of financial institutions; therefore, the financial institution can monitor the enterprise operation condition through the enterprise operation risk analysis display and alarm system 4. For example, a financial institution manager may log in the management platform with a registered account on a computer or a mobile terminal, the electricity data analysis and processing system 3 sends the risk reminding information and the electricity feature data to the corresponding account, and the computer or the mobile terminal logged in the management platform can receive the information.

According to the above, the enterprise operation abnormity reminding system based on the electricity utilization data obtains the electricity utilization characteristic data of the enterprise; acquiring the operation state information of the enterprise according to the electricity utilization characteristic data; calculating an abnormal judgment value of the enterprise according to the operation state information; sending corresponding reminding information according to the abnormal judgment value; therefore, the financial institution can know whether the operation of the enterprise is abnormal or not in time according to the reminding information, and the financial institution can find the abnormal operation condition of the enterprise more timely. In particular, the following advantages are provided:

1. the method has the advantages that massive enterprise electricity utilization data information is automatically measured and collected in real time, the obtained enterprise electricity utilization information is real and reliable, high in frequency and speed and multiple in dimensionality, the enterprise electricity utilization data is closely related to enterprise operation conditions and is difficult to counterfeit, and the method is favorable for accurately, real-timely and really obtaining abnormal states of enterprises;

In summary, although the present invention has been described with reference to the preferred embodiments, the above-described preferred embodiments are not intended to limit the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, which are substantially the same as the present invention.

Claims

1. An enterprise operation abnormity reminding method based on electricity utilization data is characterized by comprising the following steps:

A1. acquiring power utilization characteristic data of an enterprise; the electricity utilization characteristic data comprises at least one of electricity consumption, electricity utilization load, current, voltage, electricity utilization duration, active power, reactive power and power factor; the power utilization characteristic data comprises total power utilization characteristic data and power utilization characteristic data of key equipment;

A2. acquiring the operation state information of the enterprise according to the electricity utilization characteristic data; the management state information comprises at least two items of working duration information, peak power load information, periodic power consumption information, equipment operation rate information and personnel arrival rate information;

A4. sending corresponding reminding information according to the abnormal judgment value; the reminding information comprises an abnormal judgment value;

step a2 includes:

acquiring first operation state information of the enterprise according to the total electricity utilization characteristic data; acquiring second operation state information of the enterprise according to the electricity utilization characteristic data of the key equipment; acquiring final operation state information according to the first operation state information and the second operation state information;

the step of obtaining final business state information according to the first business state information and the second business state information comprises: taking a poor operator in the first operation state information and the second operation state information as final operation state information, or calculating average operation state information of the first operation state information and the second operation state information as final operation state information;

step a3 includes:

pre-inputting various operation state information into corresponding basic abnormality judgment value calculation models to obtain basic abnormality judgment values of the various operation state information, and then calculating a weighted average value of the basic abnormality judgment values of the various operation state information as an effective abnormality judgment value;

after the step A3 and before the step A4, the method further comprises the steps of:

A6. correcting the abnormal judgment value according to the popularity information;

step a6 includes:

calculating to obtain a first correction coefficient according to the information of the popularity; the smaller the information of the degree of scenery is, the larger the first correction coefficient is;

multiplying the abnormality judgment value by the first correction coefficient to obtain a corrected abnormality judgment value;

the step of obtaining the popularity information of the corresponding industry according to the operation state information of the enterprises in the same industry comprises the following steps:

judging whether the operation state of each business enterprise is good or not according to each kind of operation state information, judging which of the times y1 of good operation state and the times y2 of bad operation state is larger, finally judging that the operation state of the corresponding business enterprise is good if the times y1 of good operation state is larger than the times y2 of bad operation state, finally judging that the operation state of the corresponding business enterprise is bad if the times y1 of good operation state is smaller than the times y2 of bad operation state, and adopting a judgment result when the times y1 of good operation state is equal to the times y2 of bad operation state as a final judgment result; then calculating the prosperity information of the corresponding industry according to the proportion of the quantity of the enterprises in the same industry with good operation state in the total quantity of the enterprises in the same industry;

the step of judging whether the operation state of each enterprise in the same industry is good or not according to each operation state information comprises the following steps:

judging whether the operation state of each same-industry enterprise is good or not according to the positive and negative of the deviation, or judging whether the operation state of each same-industry enterprise is good or not according to whether the deviation is greater than a preset deviation threshold value or not; the deviation refers to the deviation between the current operation state information of the enterprises in the same industry and the corresponding operation state information of the previous preset period;

A8. correcting the abnormal judgment value according to the unit productivity energy consumption data of the enterprise and the average unit productivity energy consumption data of the corresponding industry;

step A8 includes:

calculating the difference value between the unit capacity energy consumption data of the enterprise and the average unit capacity energy consumption data of the corresponding industry; calculating a second correction coefficient according to the difference value between the unit productivity energy consumption data of the enterprise and the average unit productivity energy consumption data of the corresponding industry, wherein the larger the difference value between the unit productivity energy consumption data of the enterprise and the average unit productivity energy consumption data of the corresponding industry is, the smaller the second correction coefficient is; multiplying the second correction coefficient by the abnormality judgment value to obtain a corrected abnormality judgment value;

A10. correcting the abnormal judgment value according to the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry;

step a10 includes:

calculating the difference value between the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry; calculating a third correction coefficient according to the difference value between the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry, wherein the larger the difference value between the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry is, the smaller the third correction coefficient is; and multiplying the third correction coefficient by the abnormality judgment value to obtain a corrected abnormality judgment value.

2. The method for reminding an enterprise of an abnormal operation based on electricity consumption data as claimed in claim 1, wherein the step A1 comprises:

executing the following steps every time the electricity utilization characteristic data are collected:

judging whether the following formula is satisfied:

；

3. The power consumption data-based enterprise business anomaly reminding method according to claim 1, wherein the power consumption characteristic data comprises power consumption and power consumption load; the operation state information comprises periodic power consumption information, working time length information, equipment operation rate information and personnel post arrival rate information;

step a2 includes:

;

wherein the content of the first and second substances,

for the periodic power usage in the current calculation period,

for the periodic power usage in the last calculation period,

is the sampled value of the power usage at the current sampling time,

sampling the power consumption at the sampling time of the calculation period at the interval before the current sampling time, wherein N is the number of the sampling periods contained in each calculation period;

taking the moment when the power load is changed from being lower than the preset power load threshold value to being higher than the preset power load threshold value as the on-duty time, taking the moment when the power load is changed from being higher than the preset power load threshold value to being lower than the preset power load threshold value as the off-duty time, and subtracting the on-duty time from the off-duty time to obtain working duration information;

dividing the actually acquired power load by the average power load of the single device to obtain the number of devices for starting up, and dividing the number of the devices for starting up by the total number of the devices to obtain the information of the starting up rate of the devices;

and calculating to obtain the personnel arrival rate information according to the corresponding relation between the equipment number and the staff number and the operation rate information of the equipment.

4. An enterprise operation abnormity reminding system based on electricity consumption data is characterized by comprising an enterprise electricity consumption data acquisition device, a cloud data acquisition and storage center, an electricity consumption data analysis processing system and an enterprise operation risk analysis display and alarm system;

the enterprise electricity utilization data acquisition device is used for acquiring electricity utilization characteristic data of an enterprise and uploading the electricity utilization characteristic data to the cloud data acquisition and storage center; the power utilization characteristic data comprises total power utilization characteristic data and power utilization characteristic data of key equipment;

the electricity consumption data analysis processing system is used for acquiring electricity consumption characteristic data of an enterprise from the cloud data acquisition and storage center, acquiring operation state information of the enterprise according to the electricity consumption characteristic data, calculating an abnormal judgment value of the enterprise according to the operation state information, acquiring information of the information Sending corresponding reminding information to an enterprise operation analysis display and warning system according to the abnormal judgment value; the operation state information comprises at least two items of work duration information, peak power load information, periodic power consumption information, equipment operation rate information and personnel arrival rate information; the reminding information comprises an abnormal judgment numerical value;

the enterprise operation risk analysis display and alarm system is used for displaying the reminding information;

when the electricity utilization data analysis processing system acquires the operation state information of the enterprise according to the electricity utilization characteristic data:

when the electricity consumption data analysis processing system acquires final operation state information according to the first operation state information and the second operation state information: taking a poor operator in the first operation state information and the second operation state information as final operation state information, or calculating average operation state information of the first operation state information and the second operation state information as final operation state information;

when the electricity consumption data analysis processing system calculates the abnormal judgment value of the enterprise according to the operation state information:

when the power consumption data analysis processing system corrects the abnormal judgment value according to the information of the popularity:

when the power consumption data analysis processing system acquires the popularity information of the corresponding industry according to the operation state information of enterprises in the same industry:

judging whether the operation state of each business in the same industry is good or not according to each kind of operation state information, judging which of the times y1 for good operation state and the times y2 for bad operation state is larger, finally judging the operation state of the corresponding business in the same industry is good if the times y1 for good operation state is larger than the times y2 for bad operation state, finally judging the operation state of the corresponding business in the same industry is bad if the times y1 for good operation state is smaller than the times y2 for bad operation state, and adopting a judgment result when the judgment is carried out by using pre-designated main operation state information as a final judgment result if the times y1 for good operation state is equal to the times y2 for bad operation state;

then calculating the prosperity information of the corresponding industry according to the proportion of the quantity of the enterprises in the same industry with good operation state in the total quantity of the enterprises in the same industry;

when the power utilization data analysis and processing system judges whether the operation state of each enterprise in the same industry is good according to each operation state information:

when the electricity consumption data analysis and processing system corrects the abnormal judgment value according to the unit productivity energy consumption data of the enterprise and the average unit productivity energy consumption data of the corresponding industry:

when the electricity consumption data analysis and processing system corrects the abnormal judgment value according to the unit sales energy consumption data of the enterprise and the average unit sales energy consumption data of the corresponding industry: