Information equipment full-life-cycle digital management method and system

Abstract

The invention discloses a full-life-cycle digital management method and system for information equipment, and relates to the technical field of full-life-cycle management of the information equipment, and the method comprises the following steps: obtaining a power grid coverage area range, dividing the power grid coverage area range into a plurality of areas, obtaining power grid equipment in each area, labeling each region and power grid equipment to construct a data set; obtaining maintenance evaluation data of the power grid equipment, and constructing a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data; obtaining a maintenance evaluation grade of the to-be-tested area according to the maintenance evaluation model; carrying out coefficient correction on the maintenance evaluation grade of the to-be-detected area based on the maintenance evaluation grade of the surrounding to-be-detected area; according to the final maintenance evaluation grade, descending sorting is carried out, and a descending sorting table is obtained.According to the maintenance evaluation model establishment method and device, the maintenance evaluation model is established, and the problems of troubleshooting of equipment faults and delay of maintenance decision making are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of information equipment full life cycle management, and more specifically, to an information equipment full life cycle digital management method and system. Background Art

[0002] With the rapid development of information technology, modern enterprises and organizations are increasingly dependent on various information devices and systems, such as servers, storage devices, terminal devices, and network devices in their daily operations. As the core of supporting information infrastructure, these devices carry key business processes and data processing tasks. However, the management of information equipment has always faced many challenges, especially in the process of managing the entire life cycle of equipment. How to efficiently and accurately digitize and automate the procurement, use, maintenance, upgrade, and scrapping of equipment has become the key to improving management efficiency and reducing operation and maintenance costs.

[0003] Digital management methods achieve real-time monitoring, automated management and intelligent decision-making of the entire life cycle of equipment by introducing information technology means, such as the Internet of Things (IoT), big data, cloud computing and artificial intelligence. Specifically, IoT technology helps companies track and monitor equipment in real time, and keep abreast of the equipment's usage, health status and environmental conditions. Big data technology helps companies make scientific operation and maintenance decisions and predict equipment failure risks by collecting and analyzing equipment operation data, thereby reducing equipment downtime and improving equipment efficiency.

[0004] The above disclosed technical solutions have at least the following technical problems: Traditional equipment management often relies on manual records, paper documents and decentralized management platforms, resulting in poor information flow, low management efficiency and difficulty in optimizing equipment utilization. Equipment status and maintenance records often cannot be updated synchronously in real time, resulting in delays in troubleshooting equipment failures and maintenance decisions. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for digital management of the entire life cycle of information equipment, which solves the problems of equipment failure detection and maintenance decision delays by establishing a maintenance evaluation model.

[0006] To achieve the above object, the present invention provides the following technical solutions: A digital management method for the entire life cycle of information equipment includes obtaining the scope of a power grid coverage area, dividing the power grid coverage area into multiple areas, obtaining power grid equipment in each area, labeling each area and power grid equipment to construct a data set; obtaining maintenance evaluation data of the power grid equipment, the maintenance evaluation data including an electrical anomaly coefficient, an equipment overload tolerance index and power quality, and constructing a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data; obtaining a maintenance evaluation coefficient of each power grid equipment in the area to be tested according to the maintenance evaluation model, and obtaining a maintenance evaluation level of the area to be tested according to the maintenance evaluation coefficient of each power grid equipment; performing coefficient correction on the maintenance evaluation level of the area to be tested based on the maintenance evaluation levels of surrounding areas to be tested to obtain a final maintenance evaluation level; performing descending sorting according to the final maintenance evaluation level to obtain a descending sorting table, and performing maintenance and scrapping according to the descending sorting table.

[0007] In a preferred embodiment, the scope of the grid coverage area is obtained, the grid coverage area is divided into multiple areas, the grid equipment in each area is obtained, and each area and the grid equipment are numbered to construct a data set, specifically as follows: the scope of the grid coverage area is obtained, the grid coverage area is divided into multiple areas by pentagons, and each area is numbered; the grid equipment in each area is obtained, the grid equipment in each area is numbered and the grid equipment numbering in each area starts from an initial number; the grid coverage area is taken as the main body, each area is a subset of the grid coverage area, and the grid equipment in each area is a subset of each area; the data set is constructed according to the rule that the grid coverage area is taken as the main body, each area is a subset of the grid coverage area, and the grid equipment in each area is a subset of each area.

[0008] In a preferred embodiment, the specific steps for obtaining the electrical anomaly coefficient are as follows: measure the current of each phase of the device to be tested within a period of time, record the current data of each phase, clean the recorded current data of each phase, and retain normal data; sort the recorded current value data of each phase according to the measurement time, take three phases as a group, and calculate based on the three-phase current balance formula to obtain the three-phase current balance coefficient of each group of data, add and average the three-phase current balance coefficient of each group of data to obtain the characteristic coefficient of the three-phase current balance coefficient; obtain the grid frequency data of the power grid device to be tested for a period of time, take the normal standard value of the grid frequency as the reference value, draw a curve graph with the x-axis as time and the y-axis as the grid frequency, and establish the grid based on the curve graph and the differential equation Frequency fluctuation formula, calculated according to the power grid frequency fluctuation formula, to obtain the power grid frequency fluctuation coefficient; obtain the voltage state of the power grid equipment to be tested for a period of time, and add and average them to obtain the voltage average value of this period of time, take the voltage average value as the threshold, and set the voltage fluctuation range at the same time; obtain the number of observation points of the voltage state of the power grid equipment to be tested for the period of time and the number of observation points exceeding the set voltage fluctuation range, calculate based on the preset voltage fluctuation formula according to the number of observation points of the voltage state of the power grid equipment to be tested for the period of time and the number of observation points exceeding the set voltage fluctuation range, to obtain the voltage fluctuation; calculate based on the preset electrical anomaly coefficient formula according to the three-phase current balance coefficient, the power grid frequency fluctuation coefficient and the voltage fluctuation, to obtain the electrical anomaly coefficient.

[0009] In a preferred embodiment, the specific steps for obtaining the equipment overload tolerance index are as follows: obtaining the harmonic changes of the power grid equipment to be tested under normal voltage and current conditions, and plotting the harmonic changes of the power grid equipment to be tested under normal voltage and current conditions into a wave curve graph with time as the horizontal axis and harmonics as the vertical axis; obtaining the peak values ​​and valley values ​​of all harmonics according to the harmonic wave curve graph, and adding and averaging the peak values ​​and valley values ​​of the harmonics to obtain the average value of the peak values ​​and valley values ​​of the harmonics; adding and averaging the harmonic changes of the power grid equipment to be tested under normal voltage and current conditions to obtain a reference value of the harmonics; obtaining the temperature changes of the power grid equipment to be tested during the process of slowly increasing voltage and current Data and harmonic change data, when the voltage and current reach the safety threshold of the equipment, but the temperature does not reach the safety temperature, the experiment is stopped; when the voltage and current do not reach the safety threshold of the equipment, but the temperature reaches the safety temperature, the experiment is stopped; according to the temperature change data and harmonic change data of the power grid equipment to be tested in the process of slowly increasing the voltage and current, a temperature function is obtained based on a neural network; according to the temperature function and the harmonic change data, the harmonic influence coefficient is calculated based on a preset harmonic influence coefficient formula to obtain the harmonic influence coefficient; according to the average value of the harmonic peak value, the average value of the harmonic valley value, the harmonic baseline value and the harmonic influence coefficient, the equipment overload withstand index is calculated based on a preset equipment overload withstand index formula.

[0010] In a preferred embodiment, the specific steps for obtaining the power quality are as follows: obtaining harmonic change data of multiple time periods under normal operation of the power grid to be tested, performing data cleaning on the harmonic change data of the multiple time periods, removing abnormal data and missing values, and adding and averaging the cleaned harmonic change data of the multiple time periods to obtain characteristic values ​​of the harmonics; selecting the power grid equipment to be tested, obtaining the voltage and current waveforms of the power grid equipment to be tested when the system is switched on and off, and obtaining the amplitude of abnormal voltage or current fluctuations in the power system in a short period of time, that is, the transient amplitude, based on neural network analysis; obtaining the voltage and current waveforms of the power grid equipment to be tested under normal operation, and obtaining the phase difference of voltage and current; obtaining the power factor and frequency fluctuation of the power grid equipment to be tested, and calculating the power quality coefficient based on the preset power quality formula according to the harmonic, transient amplitude, phase difference, power factor and frequency fluctuation.

[0011] In a preferred embodiment, the maintenance evaluation coefficient of each power grid equipment in the area to be tested is obtained according to the maintenance evaluation model, and the maintenance evaluation level of the area to be tested is obtained according to the maintenance evaluation coefficient of each power grid equipment, as follows: according to the obtained power grid coverage area range, the power grid coverage area range is divided into multiple areas; all power grid equipment nodes to be tested in each area are obtained, and the maintenance evaluation coefficient of each power grid equipment is calculated according to the maintenance evaluation model; according to the regional division, the maintenance evaluation coefficients of all power grid equipment in each area are added and averaged to obtain the maintenance evaluation coefficient of each area, and the areas are graded according to the maintenance evaluation coefficients.

[0012] In a preferred embodiment, the final maintenance evaluation level is sorted in descending order to obtain a descending sorting table, and maintenance and scrapping are performed according to the descending sorting table, as follows: the areas to be tested are sorted in descending order according to the final maintenance evaluation level, and the power grid equipment in each area to be tested is also sorted in descending order to obtain a detailed descending sorting table; if the final maintenance evaluation level is higher than a preset value, the area to be tested will be comprehensively maintained and overhauled according to the descending sorting table; if the maintenance evaluation coefficient of the power grid equipment to be tested is higher than a preset value, and is still higher than the preset value after a comprehensive overhaul, the equipment will be scrapped; if the maintenance evaluation coefficient of the power grid equipment is lower than a preset value, and the service life of the equipment to be tested is longer than the initially set period, the equipment will be reported; equipment will be purchased according to the reported quantity and the scrapped quantity.

[0013] A system for a digital management method for the entire life cycle of information equipment, characterized in that it includes a data acquisition module, a maintenance evaluation model construction module, a maintenance evaluation coefficient calculation module, a maintenance evaluation coefficient correction module and a data form module, and there are connections between the modules; the data acquisition module obtains the coverage area of ​​the power grid, divides the coverage area of ​​the power grid into multiple areas, obtains the power grid equipment in each area, and labels each area and power grid equipment to construct a data set; the maintenance evaluation model construction module obtains the maintenance evaluation data of the power grid equipment, the maintenance evaluation data includes an electrical anomaly coefficient, an equipment overload bearing index, power quality and equipment power dispatching performance index, and constructs a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data; the maintenance evaluation coefficient calculation module obtains the maintenance evaluation coefficient of each power grid equipment in the test area according to the maintenance evaluation model, and obtains the maintenance evaluation level of the test area according to the maintenance evaluation coefficient of each power grid equipment; the maintenance evaluation coefficient correction module corrects the maintenance evaluation level of the test area based on the maintenance evaluation levels of the surrounding test areas to obtain a final maintenance evaluation level; the data form module performs descending sorting according to the final maintenance evaluation level to obtain a descending sorting table, and performs maintenance and scrapping according to the descending sorting table.

[0014] The technical effects and advantages of the digital management method for the entire life cycle of information equipment of the present invention are as follows: 1. The present invention divides the power grid coverage area into multiple sub-areas, and numbers and identifies each area and the power grid equipment therein, so that equipment management becomes structured and clear. This data set construction method based on geographical areas and equipment numbers helps to quickly locate and track equipment information, greatly improving management efficiency and information traceability.

[0015] 2. The present invention obtains the final maintenance evaluation level by further correcting the calculated maintenance evaluation coefficient based on the evaluation coefficient of the surrounding area. This correction mechanism that takes local and overall environmental factors into consideration more accurately reflects the performance of power grid equipment in actual operation, thereby better optimizing equipment maintenance strategies and ensuring equipment reliability and operational safety.

[0016] 3. The present invention sorts the final maintenance evaluation levels in descending order. The system automatically generates a priority maintenance and scrapping plan for the equipment based on the actual status of the equipment. Areas and equipment with poor equipment health will be given priority, reducing the operating time of high-risk equipment. This effectively avoids large-scale failures caused by inadequate maintenance of equipment and improves the stability and security of the power grid system.

[0017] 4. The present invention helps decision makers to formulate reasonable equipment maintenance and procurement plans by establishing a detailed descending sorting table. For equipment with excessively high maintenance costs or that has reached its service life, timely scrapping decisions can be made to reduce unnecessary maintenance expenses. In addition, based on the scrapping quantity and maintenance status, the system provides equipment procurement recommendations to ensure the healthy circulation of power grid equipment and the optimal allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the digital management method for the entire life cycle of information equipment of the present invention.

[0019] Figure 2 It is a schematic diagram of the system structure of the method for digital management of information equipment throughout its life cycle according to the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] Embodiment 1, Figure 1 The present invention provides a method for digital management of information equipment throughout its life cycle, comprising the following steps: S1, obtaining the coverage area of ​​the power grid, dividing the coverage area of ​​the power grid into multiple areas, obtaining the power grid equipment in each area, and labeling each area and power grid equipment to construct a data set.

[0022] In this embodiment, the grid coverage area is obtained, the grid coverage area is divided into multiple areas, the grid equipment in each area is obtained, and each area and grid equipment is labeled to construct a data set, as follows: Obtaining a coverage area of ​​a power grid, dividing the coverage area of ​​the power grid into a plurality of areas using pentagons, and labeling each area; Obtain the power grid equipment in each area, number the power grid equipment in each area, and the numbering of the power grid equipment in each area starts from an initial number; Taking the grid coverage area as the main body, each area is a subset of the grid coverage area, and the grid equipment in each area is a subset of each area; The data set is constructed based on the rule that the power grid coverage area is the main body, each area is a subset of the power grid coverage area, and the power grid equipment in each area is a subset of each area.

[0023] S2, obtaining maintenance evaluation data of power grid equipment, wherein the maintenance evaluation data includes electrical anomaly coefficient, equipment overload tolerance index, power quality and equipment power dispatching performance index, and constructing a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data.

[0024] In this embodiment, the maintenance evaluation data of the power grid equipment is obtained, and the maintenance evaluation data includes an electrical anomaly coefficient, an equipment overload tolerance index, power quality, and an equipment power dispatching performance index. A maintenance evaluation model is constructed based on the machine learning algorithm according to the maintenance evaluation data, as follows: The maintenance evaluation data include electrical anomaly coefficient, equipment overload tolerance index and power quality, as follows: The electrical anomaly coefficient is an indicator that reflects the operating stability and abnormal conditions of power grid equipment by comprehensively evaluating the current, frequency and voltage status of power grid equipment within a certain period of time. Specifically, the electrical anomaly coefficient is calculated by combining the three-phase current balance coefficient, the power grid frequency fluctuation coefficient and the voltage fluctuation through a preset formula. This coefficient can effectively identify abnormal fluctuations and faults that occur in power grid equipment during operation, and provide an important decision-making basis for the monitoring and maintenance of the power system.

[0025] The specific steps for obtaining the electrical anomaly coefficient are as follows: Measure the current of each phase of the device under test within a period of time, record the current data of each phase, clean up the recorded current data of each phase, and retain normal data; The recorded current value data of each phase are sorted according to the measurement time, and three phases are grouped together. The three-phase current balance coefficient of each group of data is calculated based on the three-phase current balance formula, and the three-phase current balance coefficient of each group of data is added and averaged to obtain the characteristic coefficient of the three-phase current balance coefficient; Obtain the grid frequency data of the grid equipment to be tested for a period of time, and draw a curve graph with the x-axis being time and the y-axis being grid frequency based on the normal standard value of the grid frequency as a reference value, establish a grid frequency fluctuation formula based on the curve graph and the differential equation, and calculate according to the grid frequency fluctuation formula to obtain the grid frequency fluctuation coefficient; Obtain the voltage status of the power grid equipment to be tested for a period of time, and add and average them to obtain the average voltage value during this period. The average voltage value is used as the threshold, and the range of voltage fluctuation is set at the same time; Obtain the number of observation points of the voltage state of the power grid equipment to be tested during the period of time and the number of observation points exceeding the set voltage fluctuation interval range, and calculate based on the preset voltage fluctuation formula according to the number of observation points of the voltage state of the power grid equipment to be tested during the period of time and the number of observation points exceeding the set voltage fluctuation interval range to obtain the voltage fluctuation; The electrical anomaly coefficient is calculated based on the three-phase current balance coefficient, the grid frequency fluctuation coefficient and the voltage fluctuation based on a preset electrical anomaly coefficient formula to obtain the electrical anomaly coefficient.

[0026] The calculation formula of the electrical anomaly coefficient is as follows:

[0027] The calculation formula of the three-phase current balance coefficient is as follows:

[0028] The calculation formula of the power grid frequency fluctuation is as follows:

[0029] The calculation formula of the voltage fluctuation is as follows:

[0030] In the formula, is the electrical anomaly coefficient, is the three-phase current balance coefficient, is the maximum value among the three phase currents, is the minimum of the three phase currents, , and is the current value in each set of three-phase current data, is the grid frequency fluctuation, is the grid frequency, is the normal standard value of the power grid frequency, is the measured time, It is voltage fluctuation, is the total number of observation points of the voltage state, is the number of observation points that exceed the set voltage fluctuation range. is the natural logarithm.

[0031] It should be noted that a three-phase current balance coefficient of 0 indicates that the three-phase current is completely balanced, while a three-phase current balance coefficient exceeding the preset threshold value indicates that there is a large imbalance problem in the power grid system and equipment maintenance and adjustment are required; a power grid frequency fluctuation coefficient of 1 indicates that the power grid frequency is stable, and the smaller the power grid frequency fluctuation coefficient, the worse the power grid frequency fluctuation stability.

[0032] Equipment overload tolerance index: Equipment overload tolerance index refers to the maximum ability of equipment to withstand loads exceeding its rated value without failure or damage under normal operating conditions. This ability is affected by but not limited to many factors such as equipment design, manufacturing materials, operating environment and maintenance. It is one of the important indicators for measuring equipment reliability and safety.

[0033] The specific steps for obtaining the equipment overload tolerance index are as follows: Obtaining harmonic changes of the power grid device under test under normal voltage and current conditions, and plotting the harmonic changes of the power grid device under test under normal voltage and current conditions into a wave curve graph with the horizontal axis being time and the vertical axis being harmonics; Obtaining the peak values ​​and valley values ​​of all harmonics according to the wave curve diagram of the harmonics, and adding and averaging the peak values ​​and valley values ​​of the harmonics to obtain the average values ​​of the peak values ​​and valley values ​​of the harmonics; Adding and averaging the harmonic changes of the power grid equipment to be tested under normal voltage and current conditions to obtain a reference value of the harmonics; Obtain the temperature change data and harmonic change data of the power grid equipment under test in the process of slowly increasing the voltage and current. When the voltage and current reach the safety threshold of the equipment, but the temperature does not reach the safety temperature, stop the experiment. When the voltage and current do not reach the safety threshold of the equipment, but the temperature reaches the safety temperature, stop the experiment. Obtaining a temperature function based on a neural network according to the temperature change data and harmonic change data of the power grid equipment to be tested in the process of slowly increasing the voltage and current; The harmonic influence coefficient is calculated based on the temperature function and the harmonic change data based on a preset harmonic influence coefficient formula to obtain the harmonic influence coefficient; The equipment overload tolerance index is obtained by calculating the average value of the harmonic peak value, the average value of the harmonic valley value, the harmonic reference value and the harmonic influence coefficient based on a preset equipment overload tolerance index formula.

[0034] The calculation formula of the equipment overload tolerance index is as follows:

[0035] The calculation formula of the harmonic influence coefficient is as follows:

[0036] In the formula, is the equipment overload tolerance index, and is the weight coefficient, is the peak value of the harmonic, represents the average value of the harmonic peaks, is the valley value of the harmonic, represents the average value of the harmonic valley values, and is the coefficient for adjusting harmonic deviation and temperature effect, is the reference value of harmonics, is the harmonic influence coefficient, is the influence coefficient of adjusting the square term of temperature and harmonic deviation, is a function of temperature.

[0037] Power quality refers to the quality of electric energy in the power system, involving voltage, frequency, waveform, and the continuity and stability of power supply. High-quality electricity should ensure stable voltage, accurate frequency, and no waveform distortion, and can respond quickly to load changes to avoid abnormal situations. It is an important indicator for measuring the performance of the power system, and is crucial to protecting electrical equipment, improving equipment operating efficiency, and ensuring production safety and the quality of life of residents. The quality of power directly affects the stable operation of the social economy and the quality of life of the people, and is one of the indispensable basic elements in the development of modern social economy.

[0038] The specific steps for obtaining the power quality are as follows: Acquire harmonic change data of multiple time periods under normal operation of the power grid to be tested, perform data cleaning on the harmonic change data of the multiple time periods, remove abnormal data and missing values, and add and average the cleaned harmonic change data of the multiple time periods to obtain characteristic values ​​of the harmonics; Select the power grid equipment to be tested, obtain the voltage and current waveforms of the power grid equipment to be tested when the system is switched on and off, and obtain the amplitude of abnormal voltage or current fluctuation in the power system in a short period of time, that is, the transient amplitude, based on the neural network analysis; Obtain the voltage and current waveforms of the power grid equipment under normal operation to obtain the phase difference between the voltage and current; The power factor and frequency fluctuation of the power grid equipment to be tested are obtained, and the power quality coefficient is calculated based on the preset power quality formula according to the amplitude, phase difference, power factor and frequency fluctuation of the harmonics and transients.

[0039] The calculation formula of the power quality is as follows:

[0040] In the formula, is the power quality, is the phase difference between voltage and current, is the frequency, is the power factor, is the amplitude of the transient, and is a constant. It's a harmonic.

[0041] The maintenance evaluation model is constructed based on the maintenance evaluation data and the machine learning algorithm, specifically as follows: The calculation formula of the maintenance evaluation model is as follows:

[0042] In the formula, is the maintenance evaluation coefficient, , and It is an adjustment parameter used to control the influence of various factors on the final evaluation coefficient. is the power quality, is the preset power quality, is the equipment overload tolerance index, is the electrical anomaly coefficient.

[0043] S3, obtaining a maintenance evaluation coefficient of each power grid device in the area to be measured according to the maintenance evaluation model, and obtaining a maintenance evaluation level of the area to be measured according to the maintenance evaluation coefficient of each power grid device.

[0044] In this embodiment, the maintenance evaluation coefficient of each power grid device in the test area is obtained according to the maintenance evaluation model, and the maintenance evaluation level of the test area is obtained according to the maintenance evaluation coefficient of each power grid device, as follows: According to the obtained power grid coverage area range, the power grid coverage area range is divided into multiple areas; Obtain all the power grid equipment nodes to be tested in each area, and calculate the maintenance evaluation coefficient of each power grid equipment according to the maintenance evaluation model; According to the regional division, the maintenance evaluation coefficients of all power grid equipment in each area are added and averaged to obtain the maintenance evaluation coefficient of each area, and the areas are graded according to the maintenance evaluation coefficient.

[0045] S4, performing coefficient correction on the maintenance evaluation level of the to-be-tested area based on the maintenance evaluation levels of the surrounding to-be-tested areas to obtain a final maintenance evaluation level.

[0046] In this embodiment, the maintenance evaluation level of the area to be measured is coefficient-corrected based on the maintenance evaluation levels of the surrounding areas to be measured to obtain the final maintenance evaluation level, as follows: The calculation formula for the maintenance evaluation coefficient correction is as follows:

[0047] In the formula, is the maintenance evaluation coefficient correction, is the maintenance evaluation coefficient, is the area connected around the area to be tested, is the weight factor.

[0048] S5, sorting in descending order according to the final maintenance evaluation level to obtain a descending sorting table, and performing maintenance and scrapping according to the descending sorting table.

[0049] In this embodiment, the final maintenance evaluation levels are sorted in descending order to obtain a descending sorting table, and maintenance and scrapping are performed according to the descending sorting table, as follows: The areas to be tested are sorted in descending order according to the final maintenance evaluation level, and the power grid equipment in each area to be tested is also sorted in descending order to obtain a detailed descending sorting table; If the final maintenance evaluation level is higher than the preset value, comprehensive maintenance and inspection will be carried out on the tested area according to the descending sorting table; If the maintenance evaluation coefficient of the power grid equipment to be tested is higher than the preset value, and is still higher than the preset value after a comprehensive overhaul, the equipment will be scrapped; If the maintenance evaluation coefficient of the power grid equipment is lower than the preset value, and the service life of the equipment under test is longer than the initially set period, the equipment will be reported; Equipment is purchased based on the reported quantity and scrapped quantity.

[0050] Embodiment 2, Figure 2 A system of a digital management method for the entire life cycle of information equipment is provided, comprising a data acquisition module, a maintenance evaluation model construction module, a maintenance evaluation coefficient calculation module, a maintenance evaluation coefficient correction module and a data form module, wherein the modules are connected; The data acquisition module obtains the coverage area of ​​the power grid, divides the coverage area of ​​the power grid into multiple areas, obtains the power grid equipment in each area, and labels each area and power grid equipment to construct a data set; A maintenance evaluation model building module is used to obtain maintenance evaluation data of power grid equipment, wherein the maintenance evaluation data includes an electrical anomaly coefficient, an equipment overload tolerance index, and an equipment performance index, and to build a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data; A maintenance evaluation coefficient calculation module, which obtains the maintenance evaluation coefficient of each power grid device in the test area according to the maintenance evaluation model, and obtains the maintenance evaluation level of the test area according to the maintenance evaluation coefficient of each power grid device; A maintenance evaluation coefficient correction module is used to correct the maintenance evaluation level of the area to be measured based on the maintenance evaluation levels of the surrounding areas to be measured to obtain a final maintenance evaluation level; The data form module performs descending sorting according to the final maintenance evaluation level to obtain a descending sorting table, and performs maintenance and scrapping according to the descending sorting table.

[0051] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0052] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0053] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0054] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0056] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital management method for the entire life cycle of information equipment, characterized in that: These include: Obtaining a coverage area of ​​a power grid, dividing the coverage area of ​​the power grid into multiple areas, obtaining power grid equipment in each area, and labeling each area and power grid equipment to construct a data set; Acquire maintenance evaluation data of power grid equipment, the maintenance evaluation data including electrical anomaly coefficient, equipment overload tolerance index and power quality, and construct a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data; Obtaining a maintenance evaluation coefficient of each power grid device in the area to be tested according to the maintenance evaluation model, and obtaining a maintenance evaluation level of the area to be tested according to the maintenance evaluation coefficient of each power grid device; Performing coefficient correction on the maintenance evaluation level of the area to be tested based on the maintenance evaluation levels of surrounding areas to be tested to obtain a final maintenance evaluation level; The final maintenance evaluation levels are sorted in descending order to obtain a descending sorting table, and maintenance and scrapping are performed according to the descending sorting table.

2. The method for digital management of information equipment throughout its life cycle according to claim 1, characterized in that: The scope of the grid coverage area is obtained, the grid coverage area is divided into multiple areas, the grid equipment in each area is obtained, and each area and grid equipment is labeled to construct a data set, as follows: Obtaining a coverage area of ​​a power grid, dividing the coverage area of ​​the power grid into a plurality of areas using pentagons, and labeling each area; Obtain the power grid equipment in each area, number the power grid equipment in each area, and the numbering of the power grid equipment in each area starts from an initial number; Taking the grid coverage area as the main body, each area is a subset of the grid coverage area, and the grid equipment in each area is a subset of each area; The data set is constructed based on the rule that the power grid coverage area is the main body, each area is a subset of the power grid coverage area, and the power grid equipment in each area is a subset of each area.

3. The method for digital management of information equipment throughout its life cycle according to claim 2, characterized in that: The specific steps for obtaining the electrical anomaly coefficient are as follows: Measure the current of each phase of the device under test within a period of time, record the current data of each phase, clean up the recorded current data of each phase, and retain normal data; The recorded current value data of each phase are sorted according to the measurement time, and three phases are grouped together. The three-phase current balance coefficient of each group of data is calculated based on the three-phase current balance formula, and the three-phase current balance coefficient of each group of data is added and averaged to obtain the characteristic coefficient of the three-phase current balance coefficient; Obtain the grid frequency data of the grid equipment to be tested for a period of time, and draw a curve graph with the x-axis being time and the y-axis being grid frequency based on the normal standard value of the grid frequency as a reference value, establish a grid frequency fluctuation formula based on the curve graph and the differential equation, and calculate according to the grid frequency fluctuation formula to obtain the grid frequency fluctuation coefficient; Obtain the voltage status of the power grid equipment to be tested for a period of time, and add and average them to obtain the average voltage value during this period. The average voltage value is used as the threshold, and the range of voltage fluctuation is set at the same time; Obtain the number of observation points of the voltage state of the power grid equipment to be tested during the period of time and the number of observation points exceeding the set voltage fluctuation interval range, and calculate based on the preset voltage fluctuation formula according to the number of observation points of the voltage state of the power grid equipment to be tested during the period of time and the number of observation points exceeding the set voltage fluctuation interval range to obtain the voltage fluctuation; The electrical anomaly coefficient is calculated based on the three-phase current balance coefficient, the grid frequency fluctuation coefficient and the voltage fluctuation based on a preset electrical anomaly coefficient formula to obtain the electrical anomaly coefficient.

4. The method for digital management of information equipment throughout its life cycle according to claim 3, characterized in that: The specific steps for obtaining the equipment overload tolerance index are as follows: Obtaining harmonic changes of the power grid device under test under normal voltage and current conditions, and plotting the harmonic changes of the power grid device under test under normal voltage and current conditions into a wave curve graph with the horizontal axis being time and the vertical axis being harmonics; Obtaining peak values ​​and valley values ​​of all harmonics according to the wave curve diagram of the harmonics, and adding and averaging the peak values ​​and valley values ​​of the harmonics to obtain the average values ​​of the peak values ​​and valley values ​​of the harmonics; Adding and averaging the harmonic changes of the power grid equipment to be tested under normal voltage and current conditions to obtain a reference value of the harmonics; Obtain the temperature change data and harmonic change data of the power grid equipment under test in the process of slowly increasing the voltage and current. When the voltage and current reach the safety threshold of the equipment, but the temperature does not reach the safety temperature, stop the experiment. When the voltage and current do not reach the safety threshold of the equipment, but the temperature reaches the safety temperature, stop the experiment. Obtaining a temperature function based on a neural network according to the temperature change data and harmonic change data of the power grid equipment to be tested in the process of slowly increasing the voltage and current; The harmonic influence coefficient is calculated based on the temperature function and the harmonic change data based on a preset harmonic influence coefficient formula to obtain the harmonic influence coefficient; The equipment overload tolerance index is calculated based on the average value of the harmonic peak value, the average value of the harmonic valley value, the harmonic reference value and the harmonic influence coefficient based on a preset equipment overload tolerance index formula to obtain the equipment overload tolerance index.

5. The method for digital management of information equipment throughout its life cycle according to claim 4, characterized in that: The specific steps for obtaining the power quality are as follows: Acquire harmonic change data of multiple time periods under normal operation of the power grid to be tested, perform data cleaning on the harmonic change data of the multiple time periods, remove abnormal data and missing values, and add and average the cleaned harmonic change data of the multiple time periods to obtain characteristic values ​​of the harmonics; Select the power grid equipment to be tested, obtain the voltage and current waveforms of the power grid equipment to be tested when the system is switched on and off, and obtain the amplitude of abnormal voltage or current fluctuation in the power system in a short period of time, that is, the transient amplitude, based on the neural network analysis; Obtain the voltage and current waveforms of the power grid equipment under normal operation to obtain the phase difference between the voltage and current; The power factor and frequency fluctuation of the power grid equipment to be tested are obtained, and the power quality coefficient is calculated based on the preset power quality formula according to the amplitude, phase difference, power factor and frequency fluctuation of the harmonics and transients.

6. The method for digital management of information equipment throughout its life cycle according to claim 5, characterized in that: The maintenance evaluation coefficient of each power grid device in the test area is obtained according to the maintenance evaluation model, and the maintenance evaluation level of the test area is obtained according to the maintenance evaluation coefficient of each power grid device, as follows: According to the obtained power grid coverage area range, the power grid coverage area range is divided into multiple areas; Obtain all the power grid equipment nodes to be tested in each area, and calculate the maintenance evaluation coefficient of each power grid equipment according to the maintenance evaluation model; According to the regional division, the maintenance evaluation coefficients of all power grid equipment in each area are added and averaged to obtain the maintenance evaluation coefficient of each area, and the areas are graded according to the maintenance evaluation coefficient.

7. The method for digital management of information equipment throughout its life cycle according to claim 6, characterized in that: The final maintenance evaluation level is sorted in descending order to obtain a descending sorting table, and maintenance and scrapping are performed according to the descending sorting table, as follows: The areas to be tested are sorted in descending order according to the final maintenance evaluation level, and the power grid equipment in each area to be tested is also sorted in descending order to obtain a detailed descending sorting table; If the final maintenance evaluation level is higher than the preset value, comprehensive maintenance and inspection will be carried out on the tested area according to the descending sorting table; If the maintenance evaluation coefficient of the power grid equipment to be tested is higher than the preset value, and is still higher than the preset value after a comprehensive overhaul, the equipment will be scrapped; If the maintenance evaluation coefficient of the power grid equipment is lower than the preset value, and the service life of the equipment under test is longer than the initially set period, the equipment will be reported; Equipment is purchased based on the reported quantity and scrapped quantity.

8. The method for digital management of information equipment throughout its life cycle according to claim 7, characterized in that: The calculation formula of the electrical anomaly coefficient is as follows: The calculation formula of the three-phase current balance coefficient is as follows: The calculation formula of the power grid frequency fluctuation is as follows: The calculation formula of the voltage fluctuation is as follows: In the formula, is the electrical anomaly coefficient, is the three-phase current balance coefficient, is the maximum value among the three phase currents, is the minimum of the three phase currents, , and is the current value in each set of three-phase current data, is the grid frequency fluctuation, is the grid frequency, is the normal standard value of the power grid frequency, is the measured time, It is voltage fluctuation, is the total number of observation points of the voltage state, is the number of observation points that exceed the set voltage fluctuation range. is the natural logarithm.

9. The method for digital management of information equipment throughout its life cycle according to claim 8, characterized in that: The calculation formula of the maintenance evaluation model is as follows: The calculation formula for the maintenance evaluation coefficient correction is as follows: In the formula, is the maintenance evaluation coefficient, , and It is an adjustment parameter used to control the influence of various factors on the final evaluation coefficient. is the power quality, is the preset power quality, is the equipment overload tolerance index, is the electrical anomaly coefficient, is the maintenance evaluation coefficient correction, is the area connected around the area to be tested, is the weight factor.

10. A system using the method for digital management of the entire life cycle of information equipment as claimed in any one of claims 1 to 9, characterized in that: It includes a data collection module, a maintenance evaluation model building module, a maintenance evaluation coefficient calculation module, a maintenance evaluation coefficient correction module and a data form module, and there are connections between the modules; The data acquisition module obtains the coverage area of ​​the power grid, divides the coverage area of ​​the power grid into multiple areas, obtains the power grid equipment in each area, and labels each area and power grid equipment to construct a data set; A maintenance evaluation model building module is used to obtain maintenance evaluation data of power grid equipment, wherein the maintenance evaluation data includes electrical anomaly coefficient, equipment overload tolerance index, power quality and equipment power dispatch performance index, and to build a maintenance evaluation model based on a machine learning algorithm according to the maintenance evaluation data; A maintenance evaluation coefficient calculation module, which obtains the maintenance evaluation coefficient of each power grid device in the test area according to the maintenance evaluation model, and obtains the maintenance evaluation level of the test area according to the maintenance evaluation coefficient of each power grid device; A maintenance evaluation coefficient correction module is used to correct the maintenance evaluation level of the area to be measured based on the maintenance evaluation levels of the surrounding areas to be measured to obtain a final maintenance evaluation level; The data form module performs descending sorting according to the final maintenance evaluation level to obtain a descending sorting table, and performs maintenance and scrapping according to the descending sorting table.

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