UPS comprehensive management and control system based on big data

Abstract

The invention belongs to the field of UPS (Uninterrupted Power Supply) management and control, relates to a parallel networking configuration technology, is used for solving the problem that configuration analysis cannot be carried out on a UPS before parallel networking in the prior art, and particularly relates to a UPS comprehensive management and control system based on big data, which comprises a comprehensive management and control platform, the comprehensive management and control platform is in communication connection with a networking analysis module, a redundancy configuration module, an operation management and control module and a database; the networking analysis module is used for performing parallel networking analysis on the UPS power supplies: marking the UPS power supplies subjected to parallel networking as analysis objects, generating an analysis period, obtaining load values of the analysis objects when operation faults occur in the analysis period, forming an analysis set by the load values of all the analysis objects, and performing cleaning processing on the analysis set to obtain a load critical value FL; according to the method, parallel networking analysis can be carried out on the UPS, so that the load critical value is screened out by analyzing elements reserved in the set, and the setting accuracy of the basic configuration value is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of UPS power supply management and control, and relates to parallel networking configuration technology, in particular to a UPS power supply comprehensive management and control system based on big data. Background Art

[0002] UPS power supply is a device that can provide backup power when the mains power is interrupted to ensure that key equipment is not affected by power fluctuations or interruptions. UPS power supply is mainly composed of battery packs, rectifiers, inverters and static switches. It can charge the battery when the mains power is normal and provide a stable power supply when the mains power is interrupted. The redundant parallel structure of UPS power supply refers to connecting multiple UPS of the same model and capacity in parallel to jointly provide power to the load. Although the redundant parallel structure improves the reliability and fault tolerance of the system, it also brings disadvantages such as system complexity, reduced efficiency, difficulty in troubleshooting, and uneven load distribution. The existing technology cannot perform configuration analysis on the UPS power supply before parallel networking, resulting in the failure rate of a single UPS power supply cannot be controlled, and the performance of the overall power supply system cannot be guaranteed.

[0003] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0004] The purpose of the present invention is to provide a UPS power supply integrated management and control system based on big data, which is used to solve the problem that the prior art cannot perform configuration analysis on the UPS power supply before parallel networking; The technical problem to be solved by the present invention is: how to provide a UPS power supply comprehensive management and control system based on big data that can perform configuration analysis on the UPS power supply before parallel networking.

[0005] The purpose of the present invention can be achieved through the following technical solutions: A UPS power supply integrated management and control system based on big data, comprising an integrated management and control platform, wherein the integrated management and control platform is communicatively connected with a networking analysis module, a redundant configuration module, an operation management and control module and a database; The network analysis module is used to perform parallel network analysis on the UPS power supply: mark the UPS power supply that has completed parallel networking and is put into use as the analysis object, generate an analysis cycle, and obtain the load value when the analysis object has an operation failure during the analysis cycle; form an analysis set with the load values ​​of all analysis objects, and clean the analysis set to obtain the load critical value FL; obtain the maximum power consumption of the current load object and mark it as the power consumption value HD, and obtain the basic configuration value JP through the formula JP=ceil(HD / FL); The redundant configuration module is used to perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: mark the redundant parallel structure that has completed parallel networking and put into use as a configuration object, generate a power consumption range, mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as a screening object, obtain the voltage stability data YW, voltage distortion data YJ and efficiency data XL of the screening object in the analysis period and perform numerical calculation to obtain the performance coefficient XN, mark the screening object with the largest performance coefficient XN value as a priority object, mark the total number of UPS power supplies configured by the priority object as the priority configuration value YP, and obtain the redundant configuration value RP through the formula RP=YP-JP; build a redundant parallel structure for the current load object according to the basic configuration value JP and the redundant configuration value RP and obtain the configuration structure; The operation control module is used to perform operation environment control analysis on the configuration structure.

[0006] Furthermore, the specific process of cleaning the analysis set includes: performing variance calculation on the analysis set to obtain the concentration coefficient, obtaining the concentration threshold through the database, and comparing the concentration coefficient with the concentration threshold: if the concentration coefficient is greater than or equal to the concentration threshold, the maximum element and the minimum element in the analysis set are eliminated, and then the concentration coefficient is recalculated, and so on, until the concentration coefficient is less than the concentration threshold; if the concentration coefficient is less than the concentration threshold, the minimum element in the analysis set is marked as the load critical value FL.

[0007] Furthermore, the process of generating the power consumption range includes: obtaining the low power consumption value HDmin and the high power consumption value HDmax through the formulas HDmin=t1×HD and HDmax=t2×HD, wherein t1 and t2 are both proportional coefficients, and 0.85≤t1≤0.95, 1.05≤t2≤1.15; and the power consumption range is constituted by the low power consumption value HDmin and the high power consumption value HDmax.

[0008] Furthermore, the process of acquiring the voltage stability data YW includes: acquiring the proportion of the time period during which the output voltage value of the analysis object is within the output voltage range within the analysis period and marking it as the voltage stability value, and summing and averaging the voltage stability values ​​of all analysis objects in the screening object to obtain the voltage stability data YW; the process of acquiring the voltage distortion data YJ includes: acquiring the proportion of nonlinear distortion contained in the output voltage of the analysis object within the analysis period and marking it as the voltage variation value, and summing and averaging the voltage variation values ​​of all analysis objects in the screening object to obtain the voltage distortion data YJ; the process of acquiring the efficiency data XL includes: acquiring the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period and marking it as the efficiency value, and summing and averaging the efficiency values ​​of all analysis objects in the screening object to obtain the efficiency data XL.

[0009] Furthermore, the specific process of the operation control module performing operation environment control analysis on the configuration structure includes: generating a control cycle and dividing the control cycle into several control time periods, obtaining the air temperature value of the operating environment in which the configuration structure is located at the end of each control time period and marking it as the air temperature value, obtaining the air temperature range through the database, and determining whether the air temperature value is within the air temperature range: if so, it means that the operating environment temperature of the configuration structure meets the requirements; if not, it means that the operating environment temperature of the configuration structure does not meet the requirements, generating a temperature adjustment signal and sending the temperature adjustment signal to the mobile terminal of the manager through the comprehensive control platform.

[0010] Furthermore, the specific process of the operation control module performing operation environment control analysis on the configuration structure also includes: obtaining the number of times the temperature adjustment signal is generated and the number of failures of the UPS power supply in the configuration structure at the end of the control period and marking them as adjustment data TS and failure data GS respectively, and obtaining the maintenance coefficient BY through the formula BY=c1×TS+c2×GS, wherein c1 and c2 are both proportional coefficients, and c2>c1>1; and determining the necessity of battery maintenance for the configuration structure through the maintenance coefficient BY.

[0011] Furthermore, the specific process of determining the necessity of battery maintenance for the configuration structure includes: obtaining the maintenance threshold BYmax through the database, and comparing the maintenance coefficient BY with the maintenance threshold BYmax: if the maintenance coefficient BY is less than the maintenance threshold BYmax, it is determined that the current configuration structure does not require battery maintenance; if the maintenance coefficient BY is greater than or equal to the maintenance threshold BYmax, it is determined that the current configuration structure requires battery maintenance, generating a maintenance signal and sending the maintenance signal to the mobile terminal of the manager through the integrated management and control platform.

[0012] The present invention has the following beneficial effects: 1. Through the network analysis module, the UPS power supply can be analyzed for parallel networking, and basic configuration can be performed from the perspective of the failure rate of a single UPS power supply. The load value of the UPS power supply at the time of failure can be screened by means of data cleaning, so as to screen out the load critical value by analyzing the elements retained in the set, and ensure the accuracy of the setting of the basic configuration value; 2. The redundant configuration module can be used to analyze the redundant parallel structure of the UPS power supply, mark the redundant parallel structure that has completed parallel networking and put into use, and then screen it according to the maximum power consumption. Combined with multiple performance parameters of the screened object, a comprehensive analysis and calculation are performed to obtain the performance coefficient. The redundant configuration value of the configuration structure is marked by the performance coefficient, so that the basic configuration quantity and redundant configuration quantity of the configuration structure are set to the optimal value, taking into account the operating failure rate of a single UPS power supply and the operating performance of the overall power supply system; 3. The operation control module can be used to perform operation environment control analysis on the completed configuration structure, monitor the air temperature value of the configuration structure operation environment in a periodic time-segment analysis manner, and analyze the necessity of maintenance based on the number of temperature adjustments and the number of UPS power failures. While optimizing the networking rules, the actual operation of the configuration structure can be dynamically monitored in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1: Figure 1 As shown, a UPS power integrated management and control system based on big data includes an integrated management and control platform, which is communicatively connected to a networking analysis module, a redundant configuration module, an operation management and control module, and a database.

[0017] The network analysis module is used to perform parallel network analysis on UPS power supplies: mark the UPS power supplies that have completed parallel networking and are put into use as analysis objects, generate analysis cycles, and obtain the load values ​​of the analysis objects when operating failures occur within the analysis cycle. Operating failures include power failures, battery failures, output failures, and input failures; the load values ​​of all analysis objects constitute an analysis set, and the analysis set is cleaned: the variance of the analysis set is calculated to obtain the concentration coefficient, the concentration threshold is obtained through the database, and the concentration coefficient is compared with the concentration threshold: if the concentration coefficient is greater than or equal to the concentration threshold, the maximum element and the minimum element in the analysis set are eliminated, and then the concentration coefficient is recalculated, and so on, until the concentration coefficient is less than the concentration threshold; if the concentration coefficient is less than the concentration threshold, the minimum element in the analysis set is marked as the load critical value FL; the maximum power consumption of the current load object is obtained and marked as the power consumption value HD, and the basic configuration value JP is obtained through the formula JP=ceil(HD / FL); basic configuration is performed from the perspective of the failure rate of a single UPS power supply, and the load value of the UPS power supply at the time of failure is feature screened in a data cleaning manner, so that the load critical value is screened out through the elements retained in the analysis set to ensure the accuracy of the setting of the basic configuration value.

[0018] The redundant configuration module is used to perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: the redundant parallel structure that has completed parallel networking and is put into use is marked as a configuration object, and the low power consumption value HDmin and the high power consumption value HDmax are obtained through the formulas HDmin=t1×HD and HDmax=t2×HD, where t1 and t2 are both proportional coefficients, and 0.85≤t1≤0.95, 1.05≤t2≤1.15; the low power consumption value HDmin and the high power consumption value HDmax constitute a power consumption range, and the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range is marked as a screening object, The voltage stability data YW, voltage distortion data YJ and efficiency data XL of the screening object in the analysis period are obtained. The process of obtaining the voltage stability data YW includes: obtaining the proportion of the time when the output voltage value of the analysis object in the analysis period is within the output voltage range and marking it as the voltage stability value, summing and averaging the voltage stability values ​​of all analysis objects in the screening object to obtain the voltage stability data YW; the process of obtaining the voltage distortion data YJ includes: obtaining the proportion of nonlinear distortion contained in the output voltage of the analysis object in the analysis period and marking it as the voltage variation value, summing and averaging the voltage variation values ​​of all analysis objects in the screening object to obtain the voltage distortion data YW. The process of obtaining the efficiency data XL includes: obtaining the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period and marking it as the efficiency value, summing up the efficiency values ​​of all the analysis objects in the screening object and taking the average value to obtain the efficiency data XL; obtaining the performance coefficient XN of the screening object by the formula XN=k1×YW+k2×XL-k3×YJ, wherein k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1; marking the screening object with the largest performance coefficient XN as the priority object, marking the total number of UPS power supplies configured for the priority object as the priority configuration value YP, The redundant configuration value RP is obtained through the formula RP=YP-JP; a redundant parallel structure is built for the current load object according to the basic configuration value JP and the redundant configuration value RP to obtain the configuration structure; the redundant parallel structures that have completed parallel networking and are put into use are marked, and then screened according to the maximum power consumption, and the performance coefficient is obtained by comprehensive analysis and calculation based on multiple performance parameters of the screened objects. The redundant configuration value of the configuration structure is marked by the performance coefficient, so that the basic configuration quantity and the redundant configuration quantity of the configuration structure are set to the optimal values, taking into account both the operating failure rate of a single UPS power supply and the operating performance of the overall power supply system.

[0019] The operation control module is used to perform operation environment control analysis on the completed redundant parallel structure: generate a control cycle and divide the control cycle into several control periods, obtain the air temperature value of the operating environment of the configuration structure at the end of each control period and mark it as the air temperature value, obtain the air temperature range through the database, and determine whether the air temperature value is within the air temperature range: if so, it means that the operating environment temperature of the configuration structure meets the requirements; if not, it means that the operating environment temperature of the configuration structure does not meet the requirements, generate a temperature adjustment signal and send the temperature adjustment signal to the mobile terminal of the manager through the integrated control platform; at the same time, at the end of the control period, obtain the number of times the temperature adjustment signal is generated and the number of failures of the UPS power supply in the configuration structure and mark them as adjustment data TS and failure data GS respectively, and use the formula BY=c1×T S+c2×GS obtains the maintenance coefficient BY, where c1 and c2 are both proportional coefficients, and c2>c1>1; obtain the maintenance threshold BYmax through the database, and compare the maintenance coefficient BY with the maintenance threshold BYmax: if the maintenance coefficient BY is less than the maintenance threshold BYmax, it is determined that the current configuration structure does not require battery maintenance; if the maintenance coefficient BY is greater than or equal to the maintenance threshold BYmax, it is determined that the current configuration structure requires battery maintenance, and a maintenance signal is generated and sent to the mobile terminal of the manager through the integrated management and control platform; monitor the air temperature value of the configuration structure operating environment in a periodic time-division analysis manner, and analyze the necessity of maintenance in combination with the number of temperature adjustments and the number of UPS power failures. While optimizing the networking rules, the actual operating configuration structure is monitored in real-time dynamic monitoring.

[0020] Embodiment 2: Figure 2 As shown, a UPS power supply comprehensive management and control method based on big data includes the following steps: Step 1: Parallel networking analysis of UPS power supply: Mark the UPS power supply that has completed parallel networking and put into use as the analysis object, obtain the load value of the analysis object when the operation failure occurs during the analysis period, and form an analysis set with the load values ​​of all analysis objects. After cleaning the analysis set, the basic configuration value JP is obtained; Step 2: Perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as the screening object, obtain the voltage stability data YW, voltage distortion data YJ and efficiency data XL of the screening object in the analysis period, and perform numerical calculation to obtain the performance coefficient XN, and mark the redundant configuration value RP by the performance coefficient XN; Step 3: Build a redundant parallel structure for the current load object according to the basic configuration value JP and the redundant configuration value RP and obtain the configuration structure; Step 4: Perform operating environment control analysis on the completed redundant parallel structure: Generate a control cycle and divide the control cycle into several control periods, and determine the necessity of battery maintenance for the configuration structure at the end of each control period.

[0021] A UPS power supply integrated management and control system based on big data, when working, marks the UPS power supply that has completed parallel networking and is put into use as an analysis object, obtains the load value of the analysis object when an operation failure occurs in the analysis period, forms an analysis set with the load values ​​of all analysis objects, and obtains the basic configuration value JP after cleaning the analysis set; marks the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as a screening object, obtains the voltage stability data YW, voltage distortion data YJ and efficiency data XL of the screening object in the analysis period and performs numerical calculation to obtain the performance coefficient XN, and marks the redundant configuration value RP by the performance coefficient XN; and builds a redundant parallel structure for the current load object according to the basic configuration value JP and the redundant configuration value RP to obtain the configuration structure.

[0022] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0023] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to actual conditions; such as: formula XN=k1×YW+k2×XL-k3×YJ; technicians in this field collect multiple groups of sample data and set corresponding performance coefficients for each group of sample data; substitute the set performance coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation group, screen the calculated coefficients and take the average, and obtain the values ​​of k1, k2 and k3 as 3.58, 3.47 and 2.95 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding performance coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the performance coefficient is proportional to the value of the voltage stability data.

[0024] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0025] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A UPS power supply integrated management and control system based on big data, characterized in that: It includes an integrated management and control platform, which is communicatively connected to a networking analysis module, a redundant configuration module, an operation management and control module, and a database; The network analysis module is used to perform parallel network analysis on the UPS power supply: mark the UPS power supply that has completed parallel networking and is put into use as the analysis object, generate an analysis cycle, and obtain the load value when the analysis object has an operation failure during the analysis cycle; form an analysis set with the load values ​​of all analysis objects, and clean the analysis set to obtain the load critical value FL; obtain the maximum power consumption of the current load object and mark it as the power consumption value HD, and obtain the basic configuration value JP through the formula JP=ceil(HD / FL); The redundant configuration module is used to perform redundant configuration analysis on the redundant parallel structure of the UPS power supply: mark the redundant parallel structure that has completed parallel networking and put into use as a configuration object, generate a power consumption range, mark the configuration object whose maximum power consumption of the corresponding load object is within the power consumption range as a screening object, obtain the voltage stability data YW, voltage distortion data YJ and efficiency data XL of the screening object within the analysis period and perform numerical calculation to obtain the performance coefficient XN, mark the screening object with the largest performance coefficient XN value as a priority object, mark the total number of UPS power supplies configured by the priority object as the priority configuration value YP, and obtain the redundant configuration value RP through the formula RP=YP-JP; According to the basic configuration value JP and the redundant configuration value RP, a redundant parallel structure is built for the current load object and a configuration structure is obtained; The operation control module is used to perform operation environment control analysis on the configuration structure.

2. According to the big data-based UPS power integrated management and control system of claim 1, it is characterized in that: The specific process of cleaning the analysis set includes: calculating the variance of the analysis set to obtain the concentration coefficient, obtaining the concentration threshold through the database, and comparing the concentration coefficient with the concentration threshold: if the concentration coefficient is greater than or equal to the concentration threshold, the maximum element and the minimum element in the analysis set are eliminated, and then the concentration coefficient is recalculated, and so on, until the concentration coefficient is less than the concentration threshold; if the concentration coefficient is less than the concentration threshold, the minimum element in the analysis set is marked as the load critical value FL.

3. According to the big data-based UPS power integrated management and control system of claim 2, it is characterized in that: The process of generating the power consumption range includes: obtaining the low power consumption value HDmin and the high power consumption value HDmax through the formulas HDmin=t1×HD and HDmax=t2×HD, wherein t1 and t2 are both proportional coefficients, and 0.85≤t1≤0.95, 1.05≤t2≤1.15; and the power consumption range is formed by the low power consumption value HDmin and the high power consumption value HDmax.

4. A UPS power integrated management and control system based on big data according to claim 3, characterized in that: The process of obtaining the voltage stability data YW includes: obtaining the proportion of the time when the output voltage value of the analysis object within the analysis period is within the output voltage range and marking it as the voltage stability value, summing and averaging the voltage stability values ​​of all analysis objects in the screening object to obtain the voltage stability data YW; the process of obtaining the voltage distortion data YJ includes: obtaining the proportion of nonlinear distortion contained in the output voltage of the analysis object within the analysis period and marking it as the voltage variation value, summing and averaging the voltage variation values ​​of all analysis objects in the screening object to obtain the voltage distortion data YJ; the process of obtaining the efficiency data XL includes: obtaining the maximum value of the ratio of the output power to the input power of the analysis object within the analysis period and marking it as the efficiency value, summing and averaging the efficiency values ​​of all analysis objects in the screening object to obtain the efficiency data XL.

5. A UPS power integrated management and control system based on big data according to claim 4, characterized in that: The specific process of the operation control module to perform operation environment control analysis on the configuration structure includes: generating a control cycle and dividing the control cycle into several control periods, obtaining the air temperature value of the operating environment of the configuration structure at the end of each control period and marking it as the air temperature value, obtaining the air temperature range through the database, and determining whether the air temperature value is within the air temperature range: if so, it means that the operating environment temperature of the configuration structure meets the requirements; if not, it means that the operating environment temperature of the configuration structure does not meet the requirements, generating a temperature adjustment signal and sending the temperature adjustment signal to the mobile terminal of the manager through the comprehensive control platform.

6. A UPS power integrated management and control system based on big data according to claim 5, characterized in that: The specific process of the operation control module performing operation environment control analysis on the configuration structure also includes: obtaining the number of times the temperature control signal is generated and the number of failures of the UPS power supply in the configuration structure at the end of the control period and marking them as adjustment data TS and failure data GS respectively, and obtaining the maintenance coefficient BY through the formula BY=c1×TS+c2×GS, where c1 and c2 are both proportional coefficients, and c2>c1>1; and determining the necessity of battery maintenance for the configuration structure through the maintenance coefficient BY.

7. A UPS power integrated management and control system based on big data according to claim 6, characterized in that: The specific process of determining the necessity of battery maintenance for the configuration structure includes: obtaining the maintenance threshold BYmax through the database, and comparing the maintenance coefficient BY with the maintenance threshold BYmax: if the maintenance coefficient BY is less than the maintenance threshold BYmax, it is determined that the current configuration structure does not require battery maintenance; if the maintenance coefficient BY is greater than or equal to the maintenance threshold BYmax, it is determined that the current configuration structure requires battery maintenance, and a maintenance signal is generated and sent to the mobile terminal of the manager through the integrated management and control platform.

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