Electric parameter data intelligent management system and method based on big data

Abstract

The invention relates to the technical field of electric parameter data management, in particular to an electric parameter data intelligent management system and method based on big data, and the method comprises the steps: constructing an electric parameter management cloud platform, calculating an equipment operation efficiency deviation value of electrical equipment, and obtaining abnormal time data; analyzing an electric parameter reference range of an electric parameter in the electric parameter reference data and an equipment matching degree of the electric cabinet equipment to obtain abnormal electric parameter reference data; analyzing the influence degree of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment, and optimizing and adjusting the electric parameter reference data of the electric cabinet equipment to obtain characteristic electric parameter reference data; and monitoring the operation environment of the electric cabinet equipment in the current period, obtaining the electric parameters of the electric cabinet equipment in the current period, evaluating the equipment state of the electric cabinet equipment in combination with the characteristic electric parameter reference data of the electric cabinet equipment, and performing intelligent management on the electric cabinet equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of electric parameter data management, and in particular to an intelligent management system and method for electric parameter data based on big data. Background Art

[0002] Big data technology refers to the technology and methods used to collect, store, process and analyze large amounts of diverse and rapidly changing data. The application of big data technology brings a series of benefits to power parameter management, including but not limited to the following: 1. Enhanced prediction capabilities, using historical power parameter data and machine learning algorithms to predict equipment load, thereby helping power agencies to do a good job in power dispatch and resource allocation, and reduce the risk of power shortage or surplus; 2. Real-time early warning of equipment, quickly discovering equipment anomalies, and issuing early warnings in time to reduce potential safety risks; 3. Improving energy management capabilities. Through in-depth analysis of power parameter data, power agencies can achieve more intelligent energy management and improve the flexibility of the power system.

[0003] At present, in the equipment status and fault analysis of electrical cabinet equipment, the main method is to compare the corresponding values ​​of the electrical parameters of the electrical cabinet equipment obtained with the preset reference values ​​of the electrical parameters of the electrical cabinet equipment, so as to determine the equipment operation status of the electrical cabinet equipment and manage the equipment of the electrical cabinet equipment. However, in real life, the equipment models of various electrical cabinet equipment are different, and even the same electrical cabinet equipment is located in different environments and the equipment for power transmission and distribution. These will have an impact on the operation status of the electrical cabinet equipment. However, the preset reference values ​​of the electrical parameters of the electrical cabinet equipment are very simple and cannot be dynamically adjusted according to the equipment model of the electrical cabinet equipment and the working environment of the electrical cabinet equipment. This means that even if the electrical parameters of the electrical cabinet equipment are obtained, the preset reference values ​​of the electrical parameters do not conform to the actual situation of the electrical cabinet equipment, which can easily lead to errors in the judgment of the equipment status of the electrical cabinet equipment, which is not only easy to cause damage to the equipment of the electrical cabinet equipment, but also may lead to the occurrence of safety accidents, resulting in huge losses. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent management system and method for electrical parameter data based on big data to solve the problems existing in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for intelligent management of electrical parameter data based on big data, the method comprising: Step S100: construct an electric parameter management cloud platform, use electric cabinet equipment to distribute power to electrical equipment, obtain historical equipment operation data of electrical equipment, calculate the equipment operation efficiency deviation value of electrical equipment, and obtain abnormal time data; Step S200: Obtain the historical equipment operation records of the electric cabinet equipment, and in combination with the abnormal time data of the electric cabinet equipment, obtain the electrical parameter reference data of the electric cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtain the abnormal electrical parameter reference data; Step S300: Obtain the historical environmental change records of the electric cabinet equipment, and analyze the influence of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment in combination with the abnormal electric parameter reference data, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain characteristic electric parameter reference data; Step S400: Monitor the operating environment of the electric cabinet equipment in the current cycle, obtain the electrical parameters of the electric cabinet equipment in the current cycle, evaluate the equipment status of the electric cabinet equipment in combination with the characteristic electrical parameter reference data of the electric cabinet equipment, and perform intelligent management of the electric cabinet equipment.

[0006] Further, step S100 includes: Step S101: construct an electric parameter management cloud platform to obtain historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment, the historical equipment operation data including data corresponding to the input power and output power of the electrical equipment; Step S102: Calculate the equipment operation efficiency η=P of the electrical equipment output / P input , where P output Expressed as the output power of electrical equipment, P input Expressed as the input power of the electrical equipment, the average value η of the equipment operating efficiency of the electrical equipment is obtained δ ; Step S103: Calculate the equipment operation efficiency deviation value η of the electrical equipment △ =|η-η δ |, obtaining the equipment operation efficiency deviation value of each electrical equipment for power distribution by the electric cabinet equipment, when the equipment operation efficiency deviation value of a certain electrical equipment among the various electrical equipment is greater than the equipment operation efficiency deviation threshold value preset for a certain electrical equipment, obtaining the historical period in which the equipment operation efficiency deviation value of a certain electrical equipment is greater than the equipment operation efficiency deviation threshold value, and determining that the power distribution of a certain electrical equipment by the electric cabinet equipment is abnormal within the historical period, and recording the historical period as the abnormal historical period of the electric cabinet equipment; Step S104: Acquire and aggregate several abnormal historical time periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment.

[0007] Further, step S200 includes: Step S201: Acquire abnormal time data of the electric cabinet equipment, acquire several abnormal historical time periods of the electric cabinet equipment from the abnormal time data, acquire and mark historical equipment operation records of the electric cabinet equipment in the several abnormal historical time periods, and acquire several marked historical equipment operation records of the electric cabinet equipment; Step S202: obtaining historical electrical parameter data of the electrical cabinet equipment from the marked historical equipment operation records, wherein the historical electrical parameter data includes data corresponding to various electrical parameters of the electrical cabinet equipment; Step S203: obtaining preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform, the electrical parameter reference data including electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment, and obtaining average values ​​of various electrical parameters of the electrical cabinet equipment in several marked historical equipment operation records; Step S204: randomly select the bth electrical parameter from the electrical parameters of the electrical cabinet equipment. When the bth electrical parameter in the electrical cabinet equipment is the average value in a certain marked historical equipment operation record and is outside the electrical parameter reference range of the bth electrical parameter, the certain historical equipment operation record is recorded as an abnormal historical equipment operation record of the bth electrical parameter, and the equipment matching value P between the bth electrical parameter in the electrical parameter reference data and the electrical cabinet equipment is calculated. b =M b / M sum , where M b It is expressed as the total number of abnormal historical equipment operation records of the bth electrical parameter, M sum Indicates the total number of historical equipment operation records marked as electrical cabinet equipment; Step S205: When the equipment matching value of the b-th electrical parameter and the electric cabinet equipment is greater than the preset equipment matching threshold, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data matches the electric cabinet equipment; otherwise, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data does not match the electric cabinet equipment, and the b-th electrical parameter is recorded as a reference abnormal electrical parameter. Several reference abnormal electrical parameters in the electric cabinet equipment are obtained and aggregated to obtain the abnormal electrical parameter reference data of the electric cabinet equipment.

[0008] Furthermore, step S300 includes: Step S301: monitor and record the environment of the electric cabinet equipment in a historical period, obtain a historical environmental change record of the electric cabinet equipment, and obtain data corresponding to various environmental parameters of the electric cabinet equipment from the historical environmental change record; Step S302: obtaining each historical equipment operation record of the electric cabinet equipment, wherein the total number of the historical equipment operation records and the historical environment change records of the electric cabinet equipment are the same and the interval length is the same, obtaining data corresponding to each electrical parameter of the electric cabinet equipment from the historical equipment operation records, and obtaining several reference abnormal electrical parameters of the electric cabinet equipment from the abnormal electrical parameter reference data of the electric cabinet equipment; Step S303: The specific process of analyzing the influence of various environmental parameters of the electric cabinet equipment on the e-th reference abnormal electrical parameter of the electric cabinet equipment is to obtain the average value of the c-th environmental parameter of the electric cabinet equipment in each historical environmental change record, and calculate the parameter influence coefficient r of the c-th environmental parameter on the e-th reference abnormal electrical parameter. c,e : , Where, j represents the total number of historical equipment operation records of the electrical cabinet equipment; X e,i It is expressed as the average value of the e-th reference abnormal electrical parameter in the i-th historical equipment operation record; X e It is represented by the mean value of the average value of the reference abnormal electrical parameters of the eth item in each historical equipment operation record; Y c,i It is expressed as the average value of the cth environmental parameter in the ith historical environmental change record; Y c It is expressed as the mean of the average values ​​of the cth environmental parameters in each historical environmental change record of the electrical cabinet equipment; Step S304: Obtain the maximum value of the coefficient of influence of various environmental parameters of the electrical cabinet equipment on the parameter of the e-th reference abnormal electrical parameter , when the maximum value is greater than the parameter influence coefficient threshold, the environmental parameter corresponding to the maximum value is recorded as the target influencing environmental parameter of the e-th reference abnormal electrical parameter; Step S305: Obtain the average value of several historical equipment operation records of the electrical cabinet equipment that are not marked, and calculate the target dynamic upper limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment. e,max : , Where n represents the total number of historical equipment operation records that are not marked in the electrical cabinet equipment; X z e It is expressed as the average value of the e-th reference abnormal electrical parameter in the z-th historical equipment operation record marked; μ z It is expressed as the mean of the average values ​​of several marked historical equipment operation records; It is represented as the monitoring value of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in the electrical cabinet equipment; Fmax and Fmin respectively represent the maximum and minimum values ​​of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in each historical environmental change record of the electrical cabinet equipment; k is a preset reference constant; Step S306: Calculate the target dynamic lower limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment e,min : , Get the target electrical parameter reference range S(e)∈[S e,min ,S e,max ], obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment; The above steps first analyze the influence of various environmental parameters of the electrical cabinet equipment on several reference abnormal electrical parameters of the electrical cabinet equipment. Different environmental parameters of the electrical cabinet equipment have different influences on the reference abnormal electrical parameters of the electrical cabinet equipment. By calculating the coefficient of influence of various environmental parameters on several reference abnormal electrical parameters, the reasons for the abnormality of the preset electrical parameter reference data can be accurately analyzed, which also makes the adjustment of the electrical parameter reference data of the electrical cabinet equipment in subsequent steps more scientific and reasonable.

[0009] Furthermore, step S400 includes: Step S401: monitor the environment in the electric cabinet equipment, obtain data corresponding to various environmental parameters of the electric cabinet equipment in the current cycle, obtain characteristic electrical parameter reference data of the electric cabinet equipment, and obtain optimized electrical parameter reference ranges of various electrical parameters of the electric cabinet equipment based on the data corresponding to various environmental parameters of the electric cabinet equipment in the current cycle; Step S402: Obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

[0010] In order to better implement the above method, an intelligent management system for electric parameter data based on big data is also proposed. The system includes an abnormal time data module, an abnormal electric parameter reference data module, a characteristic electric parameter reference data module, and an intelligent management module; The abnormal time data module is used to calculate the equipment operation efficiency deviation value of the electrical equipment and obtain the abnormal time data; The abnormal electric parameter reference data module is used to obtain the electric parameter reference data of the electric cabinet equipment from the cloud platform, analyze the electric parameter reference range of the electric parameters in the electric parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtain the abnormal electric parameter reference data; The characteristic electric parameter reference data module is used to obtain the historical environmental change records of the electric cabinet equipment, and analyze the influence of the environmental parameters of the electric cabinet equipment on the different electric parameters of the electric cabinet equipment in combination with the abnormal electric parameter reference data, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data; The intelligent management module is used to monitor the operating environment of the electric cabinet equipment, obtain the electrical parameters of the electric cabinet equipment in the current cycle, and evaluate the equipment status of the electric cabinet equipment in combination with the characteristic electrical parameter reference data of the electric cabinet equipment, and perform intelligent management of the electric cabinet equipment.

[0011] Further, the abnormal time data module includes an equipment operation efficiency deviation value unit and an abnormal time data unit; The equipment operation efficiency deviation value unit is used to obtain the historical equipment operation data of each electrical equipment that the electrical cabinet equipment is responsible for distributing power, and calculate the equipment operation efficiency deviation value of the electrical equipment; The abnormal time data unit is used to acquire and collect several abnormal historical time periods of the electric cabinet equipment to obtain the abnormal time data of the electric cabinet equipment.

[0012] Further, the abnormal electrical parameter reference data module includes a device matching value unit and an abnormal electrical parameter reference data unit; The device matching value unit is used to obtain the preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform, and calculate the device matching value between each electrical parameter in the electrical parameter reference data and the electrical cabinet equipment; The abnormal electrical parameter reference data unit is used to acquire and collect several reference abnormal electrical parameters in the electrical cabinet equipment to obtain abnormal electrical parameter reference data of the electrical cabinet equipment.

[0013] Further, the characteristic electric parameter reference data module includes a parameter influence degree coefficient unit and a characteristic electric parameter reference data unit; The parameter influence degree coefficient unit is used to analyze the influence degree of various environmental parameters of the electric cabinet equipment on several reference abnormal electrical parameters of the electric cabinet equipment, and calculate the parameter influence degree coefficient of various environmental parameters on several reference abnormal electrical parameters; The characteristic electric parameter reference data unit is used to obtain the target electric parameter reference range of several reference abnormal electric parameters of the electric cabinet equipment, and optimize the preset electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data of the electric cabinet equipment.

[0014] Further, the intelligent management module includes an intelligent management unit; The intelligent management unit is used to obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention realizes intelligent analysis of the equipment status of the electric cabinet equipment. In the process of equipment analysis of the electric cabinet equipment, the differences between different electric cabinet equipment and the influence of the equipment environment on the reference range of the electrical parameters of the electric cabinet are taken into consideration. In the analysis process, the preset electrical parameter reference data in the cloud platform is first considered to determine whether it is consistent with the actual equipment situation of the electric cabinet equipment, and the electrical parameters with abnormal references are found. When analyzing the influence of various environmental parameters in the electric cabinet equipment on the abnormal reference electrical parameters, and considering that the corresponding values ​​of the environmental parameters in the electric cabinet equipment are dynamically changing, the electrical parameter reference data of the electrical parameters of the electric cabinet equipment are optimized and adjusted, so that the judgment of the equipment status of the electric cabinet equipment through electrical parameters is more scientific and accurate, and the risk of failure of the electric cabinet equipment is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide a further understanding of the present invention and constitute part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a method flow chart of an intelligent management system and method of electrical parameter data based on big data of the present invention; Figure 2 It is a module schematic diagram of an intelligent management system and method for electrical parameter data based on big data of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1-Figure 2 The present invention provides a technical solution: an intelligent management method for electrical parameter data based on big data, the method comprising: Step S100: construct an electric parameter management cloud platform, use electric cabinet equipment to distribute power to electrical equipment, obtain historical equipment operation data of electrical equipment, calculate the equipment operation efficiency deviation value of electrical equipment, and obtain abnormal time data; Wherein, step S100 includes: Step S101: construct an electric parameter management cloud platform to obtain historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment, the historical equipment operation data including data corresponding to the input power and output power of the electrical equipment; Step S102: Calculate the equipment operation efficiency η=P of the electrical equipment output / P input , where P output Expressed as the output power of electrical equipment, P input Expressed as the input power of the electrical equipment, the average value η of the equipment operating efficiency of the electrical equipment is obtained δ ; Step S103: Calculate the equipment operation efficiency deviation value η of the electrical equipment △ =|η-η δ |, obtaining the equipment operation efficiency deviation value of each electrical equipment for power distribution by the electric cabinet equipment, when the equipment operation efficiency deviation value of a certain electrical equipment among the various electrical equipment is greater than the equipment operation efficiency deviation threshold value preset for a certain electrical equipment, obtaining the historical period in which the equipment operation efficiency deviation value of a certain electrical equipment is greater than the equipment operation efficiency deviation threshold value, and determining that the power distribution of a certain electrical equipment by the electric cabinet equipment is abnormal within the historical period, and recording the historical period as the abnormal historical period of the electric cabinet equipment; Step S104: Acquire and aggregate several abnormal historical periods of the electrical cabinet equipment to obtain abnormal time data of the electrical cabinet equipment; Step S200: Obtain the historical equipment operation records of the electric cabinet equipment, and in combination with the abnormal time data of the electric cabinet equipment, obtain the electrical parameter reference data of the electric cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtain the abnormal electrical parameter reference data; Wherein, step S200 includes: Step S201: Acquire abnormal time data of the electric cabinet equipment, acquire several abnormal historical time periods of the electric cabinet equipment from the abnormal time data, acquire and mark historical equipment operation records of the electric cabinet equipment in the several abnormal historical time periods, and acquire several marked historical equipment operation records of the electric cabinet equipment; Step S202: obtaining historical electrical parameter data of the electrical cabinet equipment from the marked historical equipment operation records, wherein the historical electrical parameter data includes data corresponding to various electrical parameters of the electrical cabinet equipment; For example, the electrical parameters of the electrical cabinet equipment include the voltage, current, frequency, etc. of the electrical cabinet equipment; Step S203: obtaining preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform, the electrical parameter reference data including electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment, and obtaining average values ​​of various electrical parameters of the electrical cabinet equipment in several marked historical equipment operation records; Step S204: randomly select the bth electrical parameter from the electrical parameters of the electrical cabinet equipment. When the bth electrical parameter in the electrical cabinet equipment is the average value in a certain marked historical equipment operation record and is outside the electrical parameter reference range of the bth electrical parameter, the certain historical equipment operation record is recorded as an abnormal historical equipment operation record of the bth electrical parameter, and the equipment matching value P between the bth electrical parameter in the electrical parameter reference data and the electrical cabinet equipment is calculated. b =M b / M sum , where M b It is expressed as the total number of abnormal historical equipment operation records of the bth electrical parameter, M sum Indicates the total number of historical equipment operation records marked as electrical cabinet equipment; Step S205: When the equipment matching value of the b-th electrical parameter and the electric cabinet device is greater than the preset equipment matching threshold, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data matches the electric cabinet device; otherwise, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data does not match the electric cabinet device, and the b-th electrical parameter is recorded as a reference abnormal electrical parameter, and several reference abnormal electrical parameters in the electric cabinet device are obtained and collected to obtain abnormal electrical parameter reference data of the electric cabinet device; Step S300: Obtain the historical environmental change records of the electric cabinet equipment, and analyze the influence of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment in combination with the abnormal electric parameter reference data, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain characteristic electric parameter reference data; Wherein, step S300 includes: Step S301: monitor and record the environment of the electric cabinet equipment in a historical period, obtain a historical environmental change record of the electric cabinet equipment, and obtain data corresponding to various environmental parameters of the electric cabinet equipment from the historical environmental change record; For example, various environmental parameters include temperature and humidity inside the electrical cabinet equipment; Step S302: obtaining each historical equipment operation record of the electric cabinet equipment, wherein the total number of the historical equipment operation records and the historical environment change records of the electric cabinet equipment are the same and the interval length is the same, obtaining data corresponding to each electrical parameter of the electric cabinet equipment from the historical equipment operation records, and obtaining several reference abnormal electrical parameters of the electric cabinet equipment from the abnormal electrical parameter reference data of the electric cabinet equipment; Step S303: The specific process of analyzing the influence of various environmental parameters of the electric cabinet equipment on the e-th reference abnormal electrical parameter of the electric cabinet equipment is to obtain the average value of the c-th environmental parameter of the electric cabinet equipment in each historical environmental change record, and calculate the parameter influence coefficient r of the c-th environmental parameter on the e-th reference abnormal electrical parameter. c,e : , Where, j represents the total number of historical equipment operation records of the electrical cabinet equipment; X e,i It is expressed as the average value of the e-th reference abnormal electrical parameter in the i-th historical equipment operation record; X e It is represented by the mean value of the average value of the reference abnormal electrical parameters of the eth item in each historical equipment operation record; Y c,i It is expressed as the average value of the cth environmental parameter in the ith historical environmental change record; Y c It is expressed as the mean of the average values ​​of the cth environmental parameters in each historical environmental change record of the electrical cabinet equipment; For example, the total number of historical equipment operation records of the electrical cabinet equipment is 3; the average value of the abnormal electrical parameter X in the second item of the first historical equipment operation record is 2,1 It is expressed as 5; the average value of the second reference abnormal electrical parameter X in the second historical equipment operation record 2,2 Expressed as 2; the average value of the second reference abnormal electrical parameter in the third historical equipment operation record is X 2,3 The average value X2 of the average value of the second reference abnormal electrical parameter in each historical equipment operation record is expressed as 4; the average value Y of the third environmental parameter in the first historical environmental change record is expressed as 3,1 Expressed as 10; the average value Y of the third environmental parameter in the second historical environmental change record 3,2 Expressed as 6; the average value Y of the third environmental parameter in the third historical environmental change record 3,3 It is expressed as 5; the mean value Y3 of the average value of the third environmental parameter in each historical environmental change record of the electrical cabinet equipment is expressed as 7; Calculate the parameter influence coefficient r of the third environmental parameter on the second reference abnormal electrical parameter 3,2 : , Step S304: Obtain the maximum value of the coefficient of influence of various environmental parameters of the electrical cabinet equipment on the parameter of the e-th reference abnormal electrical parameter , when the maximum value is greater than the parameter influence coefficient threshold, the environmental parameter corresponding to the maximum value is recorded as the target influence environmental parameter of the e-th reference abnormal electrical parameter; Step S305: Obtain the average value of several historical equipment operation records of the electrical cabinet equipment that are not marked, and calculate the target dynamic upper limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment. e,max : , Where n represents the total number of historical equipment operation records that are not marked in the electrical cabinet equipment; X z e It is expressed as the average value of the e-th reference abnormal electrical parameter in the z-th historical equipment operation record marked; μ z It is expressed as the mean of the average values ​​of several marked historical equipment operation records; It is represented as the monitoring value of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in the electrical cabinet equipment; Fmax and Fmin respectively represent the maximum and minimum values ​​of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in each historical environmental change record of the electrical cabinet equipment; k is a preset reference constant; Step S306: Calculate the target dynamic lower limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment e,min : , Get the target electrical parameter reference range S(e)∈[S e,min ,S e,max ], obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment; Step S400: monitor the operating environment of the electric cabinet equipment in the current cycle, obtain the electrical parameters of the electric cabinet equipment in the current cycle, evaluate the equipment status of the electric cabinet equipment in combination with the characteristic electrical parameter reference data of the electric cabinet equipment, and perform intelligent management of the electric cabinet equipment; Wherein, step S400 includes: Step S401: monitor the environment in the electric cabinet equipment, obtain data corresponding to various environmental parameters of the electric cabinet equipment in the current cycle, obtain characteristic electrical parameter reference data of the electric cabinet equipment, and obtain optimized electrical parameter reference ranges of various electrical parameters of the electric cabinet equipment based on the data corresponding to various environmental parameters of the electric cabinet equipment in the current cycle; Step S402: obtaining data corresponding to various electrical parameters of the electric cabinet equipment in the current cycle, and evaluating the operating status of the electric cabinet equipment in the current cycle in combination with the optimized reference range of the electrical parameters of the electric cabinet equipment, and performing intelligent management of the electric cabinet equipment; In order to better implement the above method, an intelligent management system for electric parameter data based on big data is also proposed. The system includes an abnormal time data module, an abnormal electric parameter reference data module, a characteristic electric parameter reference data module, and an intelligent management module; The abnormal time data module is used to calculate the equipment operation efficiency deviation value of the electrical equipment and obtain the abnormal time data; The abnormal electric parameter reference data module is used to obtain the electric parameter reference data of the electric cabinet equipment from the cloud platform, analyze the electric parameter reference range of the electric parameters in the electric parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtain the abnormal electric parameter reference data; The characteristic electric parameter reference data module is used to obtain the historical environmental change records of the electric cabinet equipment, and analyze the influence of the environmental parameters of the electric cabinet equipment on the different electric parameters of the electric cabinet equipment in combination with the abnormal electric parameter reference data, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data; The intelligent management module is used to monitor the operating environment of the electric cabinet equipment, obtain the electrical parameters of the electric cabinet equipment in the current cycle, and evaluate the equipment status of the electric cabinet equipment in combination with the characteristic electrical parameter reference data of the electric cabinet equipment, and perform intelligent management of the electric cabinet equipment; Among them, the abnormal time data module includes an equipment operation efficiency deviation value unit and an abnormal time data unit; The equipment operation efficiency deviation value unit is used to obtain the historical equipment operation data of each electrical equipment that the electrical cabinet equipment is responsible for distributing power, and calculate the equipment operation efficiency deviation value of the electrical equipment; The abnormal time data unit is used to obtain and collect several abnormal historical periods of the electric cabinet equipment to obtain the abnormal time data of the electric cabinet equipment; Among them, the abnormal electrical parameter reference data module includes a device matching value unit and an abnormal electrical parameter reference data unit; The device matching value unit is used to obtain the preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform, and calculate the device matching value between each electrical parameter in the electrical parameter reference data and the electrical cabinet equipment; The abnormal electrical parameter reference data unit is used to obtain and collect several reference abnormal electrical parameters in the electrical cabinet equipment to obtain abnormal electrical parameter reference data of the electrical cabinet equipment; Among them, the characteristic electric parameter reference data module includes a parameter influence degree coefficient unit and a characteristic electric parameter reference data unit; The parameter influence degree coefficient unit is used to analyze the influence degree of various environmental parameters of the electric cabinet equipment on several reference abnormal electrical parameters of the electric cabinet equipment, and calculate the parameter influence degree coefficient of various environmental parameters on several reference abnormal electrical parameters; The characteristic electric parameter reference data unit is used to obtain the target electric parameter reference range of several reference abnormal electric parameters of the electric cabinet equipment, and optimize the preset electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data of the electric cabinet equipment; Wherein, the intelligent management module includes an intelligent management unit; The intelligent management unit is used to obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

[0019] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0020] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for intelligent management of electrical parameter data based on big data, characterized in that: The method comprises: Step S100: constructing an electric parameter management cloud platform, using electric cabinet equipment to distribute power to the electrical equipment, obtaining the historical equipment operation data of the electrical equipment, calculating the equipment operation efficiency deviation value of the electrical equipment, and obtaining abnormal time data; Step S200: Obtain the historical equipment operation record of the electric cabinet equipment, and in combination with the abnormal time data of the electric cabinet equipment, obtain the electrical parameter reference data of the electric cabinet equipment from the cloud platform, analyze the electrical parameter reference range of the electrical parameters in the electrical parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtain the abnormal electrical parameter reference data; Step S300: Obtain the historical environmental change records of the electric cabinet equipment, and analyze the influence of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment in combination with the abnormal electric parameter reference data, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain characteristic electric parameter reference data; Step S400: Monitor the operating environment of the electric cabinet equipment in the current cycle, obtain the electrical parameters of the electric cabinet equipment in the current cycle, evaluate the equipment status of the electric cabinet equipment in combination with the characteristic electrical parameter reference data of the electric cabinet equipment, and intelligently manage the electric cabinet equipment.

2. The method for intelligent management of electrical parameter data based on big data according to claim 1, characterized in that: The step S100 includes: Step S101: construct an electric parameter management cloud platform to obtain historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment, wherein the historical equipment operation data includes data corresponding to the input power and output power of the electrical equipment; Step S102: Calculate the equipment operation efficiency η=P of the electrical equipment output / P input , where P output Expressed as the output power of the electrical equipment, P input The average value η of the equipment operation efficiency of the electrical equipment is obtained by expressing the input power of the electrical equipment. δ ; Step S103: Calculate the equipment operation efficiency deviation value η of the electrical equipment △ =|η-η δ |, obtaining equipment operation efficiency deviation values ​​of various electrical equipments for power distribution by the electric cabinet equipment, when the equipment operation efficiency deviation value of a certain electrical equipment among the various electrical equipments is greater than the equipment operation efficiency deviation threshold value preset for the certain electrical equipment, obtaining the historical period in which the equipment operation efficiency deviation value of the certain electrical equipment is greater than the equipment operation efficiency deviation threshold value, and determining that the power distribution of the certain electrical equipment by the electric cabinet equipment in the historical period is abnormal, and recording the historical period as the abnormal historical period of the electric cabinet equipment; Step S104: Acquire and aggregate several abnormal historical time periods of the electric cabinet equipment to obtain abnormal time data of the electric cabinet equipment.

3. The method for intelligent management of electrical parameter data based on big data according to claim 2, characterized in that: The step S200 includes: Step S201: Acquire abnormal time data of the electric cabinet device, acquire several abnormal historical time periods of the electric cabinet device from the abnormal time data, acquire and mark historical equipment operation records of the electric cabinet device in the several abnormal historical time periods, and acquire several marked historical equipment operation records of the electric cabinet device; Step S202: acquiring historical electrical parameter data of the electric cabinet device from the marked historical equipment operation records, wherein the historical electrical parameter data includes data corresponding to various electrical parameters of the electric cabinet device; Step S203: obtaining preset electrical parameter reference data of the electrical cabinet equipment from the cloud platform, wherein the electrical parameter reference data includes electrical parameter reference ranges of various electrical parameters of the electrical cabinet equipment, and obtaining average values ​​of various electrical parameters of the electrical cabinet equipment in a number of marked historical equipment operation records; Step S204: randomly select the bth electrical parameter from the electrical parameters of the electrical cabinet equipment; when the bth electrical parameter in the electrical cabinet equipment is the average value in a certain marked historical equipment operation record and is outside the electrical parameter reference range of the bth electrical parameter, the certain historical equipment operation record is recorded as an abnormal historical equipment operation record of the bth electrical parameter; calculate the equipment matching value P between the bth electrical parameter in the electrical parameter reference data and the electrical cabinet equipment b =M b / M sum , where M b It is represented by the total number of abnormal historical equipment operation records of the electrical parameter b, M sum Indicates the total number of several historical equipment operation records marked for the electrical cabinet equipment; Step S205: When the equipment matching value of the b-th electrical parameter and the electric cabinet equipment is greater than the preset equipment matching threshold, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data matches the electric cabinet equipment; otherwise, it is determined that the electrical parameter reference range of the b-th electrical parameter in the electrical parameter reference data does not match the electric cabinet equipment, and the b-th electrical parameter is recorded as a reference abnormal electrical parameter. Several reference abnormal electrical parameters in the electric cabinet equipment are obtained and aggregated to obtain the abnormal electrical parameter reference data of the electric cabinet equipment.

4. The method for intelligent management of electrical parameter data based on big data according to claim 3 is characterized in that: The step S300 includes: Step S301: monitoring and recording the environment of the electric cabinet device in a historical period, obtaining a historical environmental change record of the electric cabinet device, and obtaining data corresponding to various environmental parameters of the electric cabinet device from the historical environmental change record; Step S302: obtaining each historical equipment operation record of the electric cabinet equipment, wherein the total number of the historical equipment operation records and the historical environment change records of the electric cabinet equipment are the same and the interval length is the same, obtaining data corresponding to each electrical parameter of the electric cabinet equipment from the historical equipment operation records, and obtaining several reference abnormal electrical parameters of the electric cabinet equipment from the abnormal electrical parameter reference data of the electric cabinet equipment; Step S303: The specific process of analyzing the influence of various environmental parameters of the electric cabinet equipment on the e-th reference abnormal electrical parameter of the electric cabinet equipment is to obtain the average value of the c-th environmental parameter of the electric cabinet equipment in each historical environmental change record, and calculate the parameter influence coefficient r of the c-th environmental parameter on the e-th reference abnormal electrical parameter. c,e : ， Wherein, j represents the total number of historical equipment operation records of the electrical cabinet equipment; X e,i It is represented by the average value of the e-th reference abnormal electrical parameter in the i-th historical equipment operation record; X e Y is represented by the mean value of the average values ​​of the e-th reference abnormal electrical parameters in the historical equipment operation records; c,i Y is expressed as the average value of the cth environmental parameter in the i-th historical environmental change record; c It is represented by the mean of the average values ​​of the cth environmental parameter in each historical environmental change record of the electric cabinet equipment; Step S304: Obtain the maximum value of the coefficient of influence of various environmental parameters of the electrical cabinet equipment on the parameter of the e-th reference abnormal electrical parameter , when the maximum value is greater than the parameter influence coefficient threshold, the environmental parameter corresponding to the maximum value is recorded as the target influence environmental parameter of the e-th reference abnormal electrical parameter; Step S305: Obtain the average value of several historical equipment operation records of the electrical cabinet equipment that are not marked, and calculate the target dynamic upper limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment. e,max : ， Wherein, n represents the total number of historical equipment operation records of the electrical cabinet equipment that are not marked; X z e It is represented by the average value of the e-th reference abnormal electrical parameter in the z-th historical equipment operation record marked; μ z It is expressed as the mean of the average values ​​of several marked historical equipment operation records; It is represented as the monitoring value of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in the electrical cabinet device; Fmax and Fmin respectively represent the maximum and minimum values ​​of the target influencing environmental parameter of the e-th reference abnormal electrical parameter in each historical environmental change record of the electrical cabinet device; k is a preset reference constant; Step S306: Calculate the target dynamic lower limit reference value S of the e-th reference abnormal electrical parameter of the electrical cabinet equipment e,min : ， Obtain the target electrical parameter reference range S(e)∈[S e,min ,S e,max ], obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment.

5. The method for intelligent management of electrical parameter data based on big data according to claim 4, characterized in that: The step S400 includes: Step S401: monitor the environment in the electric cabinet equipment, obtain data corresponding to various environmental parameters of the electric cabinet equipment in the current cycle, obtain characteristic electrical parameter reference data of the electric cabinet equipment, and obtain optimized electrical parameter reference ranges of various electrical parameters of the electric cabinet equipment based on the data corresponding to various environmental parameters of the electric cabinet equipment in the current cycle; Step S402: Obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

6. An intelligent management system for electric parameter data based on big data, used to execute an intelligent management method for electric parameter data based on big data as described in any one of claims 1 to 5, characterized in that: The system includes an abnormal time data module, an abnormal electrical parameter reference data module, a characteristic electrical parameter reference data module, and an intelligent management module; The abnormal time data module is used to calculate the equipment operation efficiency deviation value of the electrical equipment to obtain abnormal time data; The abnormal electric parameter reference data module is used to obtain the electric parameter reference data of the electric cabinet equipment from the cloud platform, analyze the electric parameter reference range of the electric parameters in the electric parameter reference data and the equipment matching degree of the electric cabinet equipment, and obtain the abnormal electric parameter reference data; The characteristic electric parameter reference data module is used to obtain the historical environmental change records of the electric cabinet equipment, and analyze the influence of the environmental parameters of the electric cabinet equipment on different electric parameters of the electric cabinet equipment in combination with the abnormal electric parameter reference data, and optimize and adjust the electric parameter reference data of the electric cabinet equipment to obtain the characteristic electric parameter reference data; The intelligent management module is used to monitor the operating environment of the electric cabinet equipment, obtain the electrical parameters of the electric cabinet equipment in the current cycle, and evaluate the equipment status of the electric cabinet equipment in combination with the characteristic electrical parameter reference data of the electric cabinet equipment, and perform intelligent management of the electric cabinet equipment.

7. The intelligent management system for electrical parameter data based on big data according to claim 6 is characterized in that: The abnormal time data module includes an equipment operation efficiency deviation value unit and an abnormal time data unit; The equipment operation efficiency deviation value unit is used to obtain historical equipment operation data of each electrical equipment responsible for power distribution of the electrical cabinet equipment, and calculate the equipment operation efficiency deviation value of the electrical equipment; The abnormal time data unit is used to acquire and collect several abnormal historical time periods of the electric cabinet equipment to obtain the abnormal time data of the electric cabinet equipment.

8. The intelligent management system for electrical parameter data based on big data according to claim 6 is characterized in that: The abnormal electrical parameter reference data module includes a device matching value unit and an abnormal electrical parameter reference data unit; The device matching value unit is used to obtain the preset electrical parameter reference data of the electric cabinet device from the cloud platform, and calculate the device matching value between each electrical parameter in the electrical parameter reference data and the electric cabinet device; The abnormal electrical parameter reference data unit is used to acquire and collect several reference abnormal electrical parameters in the electrical cabinet equipment to obtain abnormal electrical parameter reference data of the electrical cabinet equipment.

9. The intelligent management system for electrical parameter data based on big data according to claim 6, characterized in that: The characteristic electric parameter reference data module includes a parameter influence degree coefficient unit and a characteristic electric parameter reference data unit; The parameter influence degree coefficient unit is used to analyze the influence degree of each environmental parameter of the electric cabinet equipment on several reference abnormal electrical parameters of the electric cabinet equipment, and calculate the parameter influence degree coefficient of each environmental parameter on the several reference abnormal electrical parameters; The characteristic electrical parameter reference data unit is used to obtain the target electrical parameter reference range of several reference abnormal electrical parameters of the electrical cabinet equipment, and optimize the preset electrical parameter reference data of the electrical cabinet equipment to obtain the characteristic electrical parameter reference data of the electrical cabinet equipment.

10. The intelligent management system for electrical parameter data based on big data according to claim 6, characterized in that: The intelligent management module includes an intelligent management unit; The intelligent management unit is used to obtain data corresponding to various electrical parameters of the electrical cabinet equipment in the current cycle, and evaluate the operating status of the electrical cabinet equipment in the current cycle in combination with the optimized electrical parameter reference range of various electrical parameters of the electrical cabinet equipment, and perform intelligent management of the electrical cabinet equipment.

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