Safety risk identification method based on equipment maintenance

Abstract

The invention discloses a safety risk identification method based on equipment maintenance, relates to the technical field of big data analysis, and aims to realize structural storage and accurate association of historical maintenance data and provide an accurate basis for calculation of key parameters such as a total shutdown reference value by constructing safety risk identification analysis set analysis of to-be-maintained equipment. According to the method, a sliding window focuses recent records through total shutdown analysis, two-dimensional comparison of total shutdown time and a total shutdown reference value and two-dimensional comparison of fault detection time and a fault detection reference value are combined, optimistic attributes and lazy attributes are divided and weighted fusion is carried out, the predicted total shutdown time is dynamically determined, and the prediction accuracy is improved; and extracting the dispatch overhaul parallel number, calculating the average overhaul rate in combination with the overhaul rate of each time in history, converting the total accumulated time consumption of all the to-be-overhauled equipment into the time required by overhaul ending considering the cooperation of parallel working groups, and comparing the time with the grouping wheel change time to realize risk quantitative identification, thereby providing a basis for plan adjustment and ensuring that the overhaul is completed on schedule.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to a method for identifying safety risks based on equipment maintenance. Background Technology

[0002] As the core carrier of the power system, power equipment is the material basis for realizing the production, transmission and distribution of electricity. Its stable operation directly determines the continuity and reliability of power supply. It not only supports the normal operation of various social and economic sectors such as industrial production and people's livelihood, but also serves as a key support for ensuring energy security and promoting the transformation of energy structure. If a fault or performance degradation occurs, it may cause power outages, leading to production stagnation, impact on people's livelihood, or even major safety risks. Therefore, its efficient operation and maintenance and condition management are of irreplaceable importance.

[0003] During equipment maintenance, the time required for each stage of maintenance is inherently uncertain due to factors such as the complexity of the fault, resource scheduling, environmental constraints, and fault type, resulting in significant fluctuations in the total downtime. If these fluctuations prevent the completion of all equipment maintenance within the specified timeframe, it can easily lead to equipment operating beyond its lifespan, the accumulation of fault risks, and ultimately threaten the continuous and stable operation of the power system, or even trigger safety accidents. Therefore, there is an urgent need for a safety risk identification method based on equipment maintenance. Summary of the Invention

[0004] The purpose of this invention is to provide a safety risk identification method based on equipment maintenance, so as to solve the problems raised in the prior art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: The safety risk identification method based on equipment maintenance includes the following steps: Step S1: Obtain historical maintenance data of the equipment to be repaired, and extract the total downtime of each historical maintenance based on the historical maintenance data. The total downtime includes fault detection time, waiting time, and repair time. Store the total downtime of each historical maintenance of the equipment to be repaired to build a safety risk identification and analysis set. Fault detection time is defined as the time span from when the equipment exhibits fault symptoms to when the fault location is completed. After the equipment alarms, maintenance personnel use a multimeter to test the circuit and analyze the system logs to ultimately determine the time spent on fault location. For example, after the fault is located as "damaged pump body seal", the time spent waiting for the new seal to arrive and coordinating the schedule of the maintenance team is considered. The waiting time is defined as the time span from when fault detection is completed to when maintenance work officially begins. The maintenance time is defined as the time span from when maintenance work officially begins to when the equipment is repaired and passes the functional test. After replacing the pump body seal, the time spent on installation, debugging, and passing a 30-minute no-load operation test is considered. Step S2: Analyze and calculate based on the safety risk identification and analysis set to obtain the total downtime baseline value, total downtime optimistic value, and total downtime lazy value of the equipment to be repaired; Step S3: Set up a sliding window for total downtime analysis of the equipment to be repaired, and combine it with Step S2 to divide the total downtime attribute of the safety risk identification and analysis set to obtain the ratio of optimistic attribute and lazy attribute in the total downtime of the equipment to be repaired within the sliding window for total downtime analysis. Step S4: Determine the current total downtime of the equipment to be repaired by analyzing the ratio of optimistic and lazy attributes in the total downtime within the total downtime analysis sliding window, and record it as the estimated total downtime. Step S5: Based on the safety risk identification analysis set analysis, obtain the maintenance start time of the equipment to be maintained, and obtain the maintenance end time of the equipment to be maintained by combining the maintenance start time with the expected total downtime. Step S6: Through steps S1 to S5, the maintenance deadlines of each piece of equipment to be maintained are obtained by synchronously processing each piece of equipment to be maintained. Based on the maintenance deadlines of each piece of equipment to be maintained, the total cumulative maintenance time of all equipment to be maintained is calculated. Step S7: Obtain the group wheel replacement time and the average maintenance rate of each historical maintenance of the equipment to be repaired. Analyze the total cumulative maintenance time and the average maintenance rate of all equipment to be repaired to obtain the time required to complete the maintenance. Identify safety risks by combining the time required to complete the maintenance with the group wheel replacement time.

[0006] The specific steps of step S1 above are as follows: Step S1-1: Extract the fault detection time, waiting time and maintenance time included in the total downtime of each maintenance from the historical maintenance data. Specifically, analyze the start time and end time of each component to obtain the time span value of each component; then sum up the time span values ​​of each component to obtain the total downtime of a maintenance. Step S1-2: Select the unique identifier code of the equipment to be repaired as the key, and select the unique repair number, total downtime, fault detection time, waiting time and repair time of the corresponding single repair as the value. Store the historical repair time of each equipment to be repaired according to the key-value correspondence. Different repairs of the same equipment to be repaired are distinguished by the unique repair number in the value field, and a safety risk identification and analysis set is constructed.

[0007] Accurately extract the total downtime and time span of each component from historical maintenance, and store them in a structured manner through the key-value correspondence between the unique equipment identifier and the unique maintenance number. The constructed safety risk identification and analysis set provides an accurate and distinguishable historical data foundation for subsequent safety risk identification, ensuring the rigor and traceability of data association.

[0008] The specific steps of step S2 above are as follows: Step S2-1: Extract the total downtime corresponding to each maintenance from the safety risk identification and analysis set based on the unique maintenance number. Sum all the extracted total downtimes and divide by the total number of downtimes. The result is used as the total downtime baseline value of the equipment to be maintained. The formula for calculating the total shutdown baseline value is as follows: ; In the formula, T base t represents the total downtime baseline value for the equipment to be repaired; M represents the total number of historical repairs, i.e., the total downtime; i Let represent the total downtime of the i-th maintenance; Step S2-2: From the safety risk identification and analysis set, extract the total downtime corresponding to each maintenance based on the unique maintenance number. Compare all the extracted total downtimes, select the total downtime with the smallest value as the total downtime optimistic value, and select the total downtime with the largest value as the total downtime lazy value.

[0009] The total downtime for each event is extracted from the safety risk identification and analysis set. The average value is calculated to obtain the baseline value of total downtime. The minimum value is selected as the optimistic value of total downtime, and the maximum value is selected as the lazy value of total downtime. This comprehensively reflects the central trend and extreme cases of historical total downtime, providing a multi-dimensional quantitative reference for subsequent classification and prediction of total downtime attributes, and ensuring the comprehensiveness of the analysis and the accuracy of the data support.

[0010] The specific steps of step S3 above are as follows: Step S3-1: Set the window size of the total shutdown analysis sliding window to the preset number of consecutive maintenance times, and the sliding step size to 1 historical maintenance record; the initial position of the window corresponds to the consecutive N historical maintenance records traced back from the most recent maintenance, where N is the preset value of the window size. Step S3-2: Extract the total downtime and fault detection time corresponding to each maintenance operation covered by the current position of the total downtime analysis sliding window from the safety risk identification and analysis set; Step S3-3: Compare the total downtime of each maintenance in the total downtime analysis sliding window with the total downtime baseline value. Mark the total downtime less than the total downtime baseline value as optimistic attribute, and mark the total downtime not less than the total downtime baseline value as lazy attribute. The formulas defining the total downtime optimism value and the total downtime slack value are as follows: Total downtime optimism value T opt =min{t1, t2, ..., t M} Select the minimum value from all historical total downtime; Total downtime optimism value T lazy =max{t1, t2, ..., t M} Select the maximum value from all historical total downtime; Step S3-4: Count the total downtime marked as optimistic attribute in the total downtime analysis sliding window, and record it as the first optimistic attribute count; count the total downtime marked as lazy attribute, and record it as the first lazy attribute count.

[0011] Step S3 above also includes: Step S3-5: Sum the fault detection times of each maintenance within the total downtime analysis sliding window and divide the sum by the number of fault detection times within the total downtime analysis sliding window. The result is used as the fault detection baseline value. The formula for calculating the fault detection benchmark value of the equipment to be inspected is as follows: ; In the formula, D base This represents the fault detection baseline value within the total shutdown analysis sliding window; d j The fault detection time is represented by N, which is the j-th maintenance time within the total downtime analysis sliding window; N represents the size of the total downtime analysis sliding window, which is the preset number of consecutive maintenance times.

[0012] Step S3-6: Compare the fault detection time of each maintenance in the total downtime analysis sliding window with the fault detection benchmark value. Count the number of fault detection times that do not exceed the fault detection benchmark value and record them as the number of second optimistic attributes. Count the number of fault detection times that exceed the fault detection benchmark value and record them as the number of second lazy attributes.

[0013] A sliding window for total downtime analysis is set up, with a preset window size of N consecutive maintenance cycles and a sliding step size of one historical maintenance record. The initial position focuses on the N records traced back from the most recent maintenance cycle, ensuring that the analysis focuses on recent maintenance situations and is timely. The total downtime and fault detection time within the window are extracted from the safety risk identification and analysis set to provide a data foundation for attribute classification. By comparing the total downtime with the total downtime baseline value, optimistic and lazy attributes are marked, and the number of first optimistic and lazy attributes is counted. At the same time, the fault detection baseline value within the window is calculated, and by comparing the fault detection time with this baseline value, the number of second optimistic and lazy attributes is counted. Attribute classification is completed from two dimensions: total downtime and fault detection. Finally, the proportion of optimistic and lazy attributes within the window is obtained, which can dynamically capture the characteristics of recent maintenance time and improve the comprehensiveness of attribute judgment through two-dimensional analysis, providing accurate and multi-dimensional quantitative basis for subsequent prediction of total downtime.

[0014] In step S4 above, the comprehensive lazy attribute quantity is calculated by weighted fusion based on the quantity of the first lazy attribute and the quantity of the second lazy attribute, and the comprehensive optimistic attribute quantity is calculated by weighted fusion based on the quantity of the first optimistic attribute and the quantity of the second optimistic attribute. When the comprehensive optimistic attribute quantity exceeds the comprehensive lazy attribute quantity, the total downtime optimistic value is selected as the current total downtime of the equipment to be repaired. When the comprehensive optimistic attribute quantity does not exceed the comprehensive lazy attribute quantity, the total downtime lazy value is selected as the current total downtime of the equipment to be repaired, and the selected total downtime is recorded as the estimated total downtime.

[0015] The formula for calculating the total number of lazy attributes is as follows: L syn =w1×L1+w2×L2; In the formula, L syn L1 represents the total number of lazy attributes; L2 represents the number of the first lazy attributes; L3 represents the number of the second lazy attributes; w1 and w2 represent the preset weight coefficients, and satisfy += 1. The formula for calculating the overall number of optimistic attributes is as follows: Q syn =w1×Q1+w2×Q2; In the formula, Q syn Q1 represents the total number of optimistic attributes; Q2 represents the number of the first optimistic attribute; Q3 represents the number of the second optimistic attribute. The comprehensive lazy attribute quantity is obtained by weighted fusion of the first and second lazy attribute quantities, and the comprehensive optimistic attribute quantity is obtained by weighted fusion of the first and second optimistic attribute quantities. Based on the comparison between the comprehensive optimistic attribute quantity and the comprehensive lazy attribute quantity, the total downtime optimistic value or the total downtime lazy value is selected as the current estimated total downtime of the equipment to be repaired. This integrates the attribute characteristics of both total downtime and fault detection time, making the determination of the estimated total downtime more consistent with the actual maintenance situation and improving the accuracy and rationality of the estimated total downtime.

[0016] The specific steps of step S5 above are as follows: Step S5-1: Extract the maintenance start time corresponding to each historical maintenance of the equipment to be maintained from the safety risk identification and analysis set. Sort all the extracted maintenance start times and select the median value after sorting as the maintenance start time of the equipment to be maintained in the current group wheel change time. Step S5-2: Add the maintenance start time determined in step S5-1 to the estimated total downtime obtained in step S4, and use the result as the maintenance deadline for the equipment to be maintained.

[0017] The maintenance start time corresponding to each historical maintenance of the equipment to be maintained is extracted from the safety risk identification and analysis set. After sorting, the median value is selected as the maintenance start time within the current group rotation time, so that the maintenance start time more robustly reflects the historical pattern. The maintenance start time is added to the expected total downtime obtained in step S4 to obtain the maintenance end time. Combining the characteristics of historical data with the current expected situation, the determination of the maintenance end time is more in line with reality.

[0018] The specific steps of step S6 above are as follows: Step S6-1: Through steps S1 to S5, each piece of equipment to be repaired is processed synchronously to obtain the repair start time and repair end time of each piece of equipment in the current group wheel replacement time. Step S6-2: Calculate the time difference between the maintenance deadline and the maintenance start time for each piece of equipment to be maintained, and obtain the individual time required for each piece of equipment to complete maintenance. Step S6-3: Add up the individual times of all the equipment to be repaired, and use the result as the total cumulative time of all the equipment to be repaired.

[0019] By synchronously processing each piece of equipment to be repaired, the start and end times of repairs within the current group rotation time are obtained. The time difference between the repair end time and the repair start time of each piece of equipment is calculated to obtain the individual time consumption. Then, all individual times consumption are added together to obtain the total cumulative time consumption. This comprehensively covers the time consumption calculation of all equipment to be repaired, ensuring the completeness and accuracy of the total cumulative time consumption.

[0020] The specific steps of step S7 above are as follows: Step S7-1: Extract the number of parallel maintenance dispatches and the group rotation time set by the maintenance group from the preset parameter library of the equipment maintenance management system; the number of parallel maintenance dispatches represents the number of parallel work groups participating in maintenance within the same time period; the group rotation time is preset by the system and is a uniformly stipulated completion time applicable to the rotation maintenance of all equipment to be maintained within the group. Step S7-2: Calculate the average maintenance rate based on the historical maintenance rates. The maintenance rate is expressed as the quotient of the total cumulative time corresponding to the unique maintenance number divided by the number of parallel maintenance dispatches. The average maintenance rate is the sum of the maintenance rates in each historical maintenance divided by the total number of historical maintenance operations involved in the calculation of the average maintenance rate.

[0021] Step S7 above also includes: Step S7-3: Divide the total cumulative time of all equipment to be repaired by the average repair rate to obtain the theoretical total repair time; then divide the theoretical total repair time by the number of parallel repair dispatches to obtain the time required to complete the repair after considering the collaboration of parallel work groups. Step S7-4: Compare the time required to complete the maintenance with the group wheel replacement time. If the time required to complete the maintenance exceeds the group wheel replacement time, it is identified as a safety risk and it is determined that the maintenance of all equipment to be maintained cannot be completed within the specified wheel replacement cycle. A maintenance safety risk warning signal is issued. If the time required to complete the maintenance does not exceed the group wheel replacement time, it is identified as no safety risk at present and the maintenance of all equipment to be maintained can be completed within the specified wheel replacement cycle.

[0022] The safety risk identification method based on equipment maintenance mentioned in this invention has the following beneficial effects: 1. This invention constructs a safety risk identification and analysis set with the unique identification code of the equipment to be repaired as the key, the unique repair number corresponding to a single repair, and time span values ​​such as total downtime and fault detection time as values. This enables the structured storage and precise correlation of historical repair data, solving the problems of scattered and chaotic correlation in traditional data. It provides an accurate basis for calculating key parameters such as total downtime baseline, total downtime optimistic value, and total downtime lazy value, ensuring the rigor and traceability of data in subsequent analysis.

[0023] 2. This invention focuses on recent consecutive N maintenance records by setting a total downtime analysis sliding window. It combines a two-dimensional comparison of total downtime with total downtime baseline value and fault detection time with fault detection baseline value, classifies optimistic and lazy attributes and weighted fusion to obtain the number of comprehensive attributes, dynamically captures recent maintenance characteristics to determine the expected total downtime, overcomes the limitation of traditional methods that rely solely on a single historical average, makes the time prediction more realistic, and improves the accuracy and rationality of maintenance time estimation.

[0024] 3. This invention extracts the number of parallel maintenance dispatches from the preset parameter library of the equipment maintenance management system, calculates the average maintenance rate by combining it with the maintenance rate of each historical maintenance, and converts the total cumulative time of all equipment to be maintained into the time required to complete maintenance considering the collaboration of parallel working groups. This is compared with the group rotation time to achieve risk quantification and identification, which solves the risk misjudgment caused by ignoring the impact of parallel work in the traditional approach, provides a clear basis for adjusting maintenance plans, and ensures the reliability of maintenance completion within the specified cycle. Attached Figure Description

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart illustrating the safety risk identification method based on equipment maintenance according to the present invention. Detailed Implementation

[0026] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0027] Example 1: like Figure 1 As shown, the present invention provides a technical solution, a safety risk identification method based on equipment maintenance, the safety risk identification method comprising the following steps: Step S1: Obtain historical maintenance data of the equipment to be repaired, and extract the total downtime of each historical maintenance based on the historical maintenance data. The total downtime includes fault detection time, waiting time, and repair time. Store the total downtime of each historical maintenance of the equipment to be repaired to construct a safety risk identification and analysis set. Step S1-1: Extract the fault detection time, waiting time and maintenance time included in the total downtime of each maintenance from the historical maintenance data. Specifically, analyze the start time and end time of each component to obtain the time span value of each component; then sum up the time span values ​​of each component to obtain the total downtime of a maintenance. Step S1-2: Select the unique identifier code of the equipment to be repaired as the key, and select the unique repair number, total downtime, fault detection time, waiting time and repair time of the corresponding single repair as the value. Store the historical repair time of each equipment to be repaired according to the key-value correspondence. Different repairs of the same equipment to be repaired are distinguished by the unique repair number in the value field to construct a safety risk identification and analysis set. In practical implementation, it is necessary to first ensure the integrity of historical maintenance data, accurately extract the start and end times of fault detection time, waiting time, and maintenance time for each maintenance from the data, obtain the time span values ​​of each component through time span calculation rules, and sum them up to form the total downtime; then establish a key-value correspondence relationship between the unique identification code of the equipment to be maintained and the unique maintenance number of each maintenance, and ensure that different maintenances of the same equipment are clearly distinguished by the unique maintenance number during storage to avoid data confusion and lay a reliable data foundation for subsequent analysis.

[0028] Step S2: Analyze and calculate based on the safety risk identification and analysis set to obtain the total downtime baseline value, total downtime optimistic value, and total downtime lazy value of the equipment to be repaired; Step S2-1: Extract the total downtime corresponding to each maintenance from the safety risk identification and analysis set based on the unique maintenance number. Sum all the extracted total downtimes and divide by the total number of downtimes. The result is used as the total downtime baseline value of the equipment to be maintained. Step S2-2: From the safety risk identification and analysis set, extract the total downtime corresponding to each maintenance based on the unique maintenance number. Compare all the extracted total downtimes and select the total downtime with the smallest value as the total downtime optimistic value and the total downtime with the largest value as the total downtime lazy value. In practice, all historical total downtime is extracted from the safety risk identification and analysis set based on the unique maintenance number. When calculating the total downtime baseline value, it is necessary to ensure that no total downtime is omitted from the calculation. When selecting the optimistic value and lazy value of total downtime, all extracted total downtimes need to be comprehensively compared to avoid deviations in results due to missing data. This ensures that the three values ​​obtained can truly reflect the overall characteristics of the historical total downtime and provide an effective reference for attribute classification.

[0029] Step S3: Set up a sliding window for total downtime analysis of the equipment to be repaired, and combine it with Step S2 to divide the total downtime attribute of the safety risk identification and analysis set to obtain the ratio of optimistic attribute and lazy attribute in the total downtime of the equipment to be repaired within the sliding window for total downtime analysis. Step S3-1: Set the window size of the total shutdown analysis sliding window to the preset number of consecutive maintenance times, and the sliding step size to 1 historical maintenance record; the initial position of the window corresponds to the consecutive N historical maintenance records traced back from the most recent maintenance, where N is the preset value of the window size. Step S3-2: Extract the total downtime and fault detection time corresponding to each maintenance operation covered by the current position of the total downtime analysis sliding window from the safety risk identification and analysis set; Step S3-3: Compare the total downtime of each maintenance in the total downtime analysis sliding window with the total downtime baseline value. Mark the total downtime less than the total downtime baseline value as optimistic attribute, and mark the total downtime not less than the total downtime baseline value as lazy attribute. Step S3-4: Count the total downtime marked as optimistic attribute in the total downtime analysis sliding window, and record it as the first optimistic attribute count; count the total downtime marked as lazy attribute, and record it as the first lazy attribute count; Step S3-5: Sum the fault detection times of each maintenance within the total downtime analysis sliding window and divide the sum by the number of fault detection times within the total downtime analysis sliding window. The result is used as the fault detection baseline value. Step S3-6: Compare the fault detection time of each maintenance in the total downtime analysis sliding window with the fault detection benchmark value. Count the number of fault detection times that do not exceed the fault detection benchmark value and record it as the number of second optimistic attributes. Count the number of fault detection times that exceed the fault detection benchmark value and record it as the number of second lazy attributes. In practical implementation, the window size N of the total downtime analysis sliding window needs to be reasonably preset based on the maintenance frequency of the equipment to be inspected and the number of historical maintenance records to ensure that the window can effectively capture recent maintenance patterns. When extracting the total downtime and fault detection time corresponding to each maintenance covered by the current position of the total downtime analysis sliding window from the safety risk identification and analysis set, it must accurately correspond to the maintenance records covered by the window. When marking optimistic and lazy attributes and counting the number of the first optimistic attribute, the first lazy attribute, the second optimistic attribute, and the second lazy attribute, the comparison rules with the corresponding benchmark values ​​must be strictly followed to ensure the accuracy of the statistical results and provide reliable data for the calculation of the comprehensive attribute quantity.

[0030] Step S4: Determine the current total downtime of the equipment to be repaired by analyzing the ratio of optimistic and lazy attributes in the total downtime within the total downtime analysis sliding window, and record it as the estimated total downtime. The total number of lazy attributes is calculated by weighted fusion based on the number of first lazy attributes and the number of second lazy attributes. The total number of optimistic attributes is calculated by weighted fusion based on the number of first optimistic attributes and the number of second optimistic attributes. When the total number of optimistic attributes exceeds the total number of lazy attributes, the total downtime optimism value is selected as the current total downtime of the equipment to be repaired. When the total number of optimistic attributes does not exceed the total number of lazy attributes, the total downtime laziness value is selected as the current total downtime of the equipment to be repaired. The selected total downtime is recorded as the estimated total downtime. In practical implementation, when performing weighted fusion calculations of the number of comprehensive optimistic attributes and the number of comprehensive lazy attributes, reasonable weights must be set based on the actual impact of total downtime and fault detection time on the maintenance process to avoid the comprehensive results deviating from the actual maintenance characteristics due to improper weight allocation. During the calculation process, it is necessary to ensure that the values ​​of the first optimistic attribute, the second optimistic attribute, the first lazy attribute, and the second lazy attribute are accurate to avoid data referencing errors. When comparing the number of comprehensive optimistic attributes and the number of comprehensive lazy attributes, the size relationship must be strictly determined. The rule of selecting the total downtime optimistic value when the number of comprehensive optimistic attributes exceeds the number of comprehensive lazy attributes, and selecting the total downtime lazy value otherwise, must be followed to ensure that the estimated total downtime can truly reflect the attribute proportion relationship within the total downtime analysis sliding window and improve the matching degree with the actual maintenance time.

[0031] Step S5: Based on the safety risk identification analysis set analysis, obtain the maintenance start time of the equipment to be maintained, and obtain the maintenance end time of the equipment to be maintained by combining the maintenance start time with the expected total downtime. Step S5-1: Extract the maintenance start time corresponding to each historical maintenance of the equipment to be maintained from the safety risk identification and analysis set. Sort all the extracted maintenance start times and select the median value after sorting as the maintenance start time of the equipment to be maintained in the current group wheel change time. Step S5-2: Add the maintenance start time determined in step S5-1 to the estimated total downtime obtained in step S4, and use the result as the maintenance deadline for the equipment to be maintained. In practical implementation, when extracting the maintenance start time corresponding to each historical maintenance of the equipment to be maintained from the safety risk identification and analysis set, it is necessary to ensure that the extraction is complete and without omissions; after sorting the extracted maintenance start times, the median value is selected as the maintenance start time within the current group rotation time, and it is necessary to ensure that the sorting is correct and the median value is selected correctly; when adding the maintenance start time to the expected total downtime to obtain the maintenance end time, it is necessary to ensure that the time unit is consistent and the accumulation calculation is accurate, so that the maintenance end time can truly reflect the maintenance completion time.

[0032] Step S6: Through steps S1 to S5, the maintenance deadlines of each piece of equipment to be maintained are obtained by synchronously processing each piece of equipment to be maintained. Based on the maintenance deadlines of each piece of equipment to be maintained, the total cumulative maintenance time of all equipment to be maintained is calculated. Step S6-1: Through steps S1 to S5, each piece of equipment to be repaired is processed synchronously to obtain the repair start time and repair end time of each piece of equipment in the current group wheel replacement time. Step S6-2: Calculate the time difference between the maintenance deadline and the maintenance start time for each piece of equipment to be maintained, and obtain the individual time required for each piece of equipment to complete maintenance. Step S6-3: Add up the individual time consumption of all the equipment to be repaired, and use the result as the total cumulative time consumption of all the equipment to be repaired; In practice, the simultaneous processing of each piece of equipment to be repaired must strictly follow the unified standards of steps S1 to S5 to ensure that the calculation logic of the start time and end time of each piece of equipment is consistent, and to avoid data incomparability due to processing differences. When calculating the time consumption of a single piece of equipment, the time difference between the end time and the start time of each piece of equipment to be repaired must be accurately calculated to ensure that the calculation of the time consumption of a single time consumption is correct. The total cumulative time consumption obtained by summing up all the individual time consumption must be carefully checked to avoid double counting or omissions, and to ensure that the total cumulative time consumption can comprehensively and accurately reflect the total time consumption of all equipment to be repaired.

[0033] Step S7: Obtain the group wheel replacement time and the average maintenance rate of each historical maintenance of the equipment to be repaired. Analyze the total cumulative maintenance time and the average maintenance rate of all equipment to be repaired to obtain the time required to complete the maintenance. Identify safety risks by combining the time required to complete the maintenance with the group wheel replacement time.

[0034] Step S7-1: Extract the number of parallel maintenance dispatches and the group rotation time set by the maintenance group from the preset parameter library of the equipment maintenance management system; the number of parallel maintenance dispatches represents the number of parallel work groups participating in maintenance within the same time period; the group rotation time is preset by the system and is a uniformly stipulated completion time applicable to the rotation maintenance of all equipment to be maintained within the group. Step S7-2: Calculate the average maintenance rate based on the historical maintenance rates. The maintenance rate is expressed as the quotient of the total cumulative time corresponding to the unique maintenance number divided by the number of parallel maintenance dispatches. The average maintenance rate is the sum of the maintenance rates in each historical maintenance divided by the total number of historical maintenances involved in the calculation of the average maintenance rate. Step S7-3: Divide the total cumulative time of all equipment to be repaired by the average repair rate to obtain the theoretical total repair time; then divide the theoretical total repair time by the number of parallel repair dispatches to obtain the time required to complete the repair after considering the collaboration of parallel work groups. Step S7-4: Compare the time required to complete the maintenance with the group wheel replacement time. If the time required to complete the maintenance exceeds the group wheel replacement time, it is identified as a safety risk and it is determined that the maintenance of all equipment to be maintained cannot be completed within the specified wheel replacement cycle. A maintenance safety risk warning signal is issued. If the time required to complete the maintenance does not exceed the group wheel replacement time, it is identified as no safety risk at present and the maintenance of all equipment to be maintained can be completed within the specified wheel replacement cycle.

[0035] In practice, the number of parallel maintenance dispatches and the group rotation time extracted from the preset parameter library of the equipment maintenance management system must match the current maintenance group to ensure that the parameters are true and valid. When calculating the average maintenance rate, it is necessary to ensure that the historical maintenance rates are calculated accurately and that the historical maintenance records used in the average calculation are representative. When comparing, it is necessary to accurately determine the relationship between the time required to complete the maintenance and the group rotation time to ensure that the safety risk identification results are reliable and provide a clear basis for adjusting the maintenance plan.

[0036] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and not restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description.

Claims

1. A safety risk identification method based on equipment maintenance, characterized in that, Includes the following steps: Step S1: Obtain historical maintenance data of the equipment to be repaired, and extract the total downtime of each historical maintenance based on the historical maintenance data. The total downtime includes fault detection time, waiting time, and repair time. Store the total downtime of each historical maintenance of the equipment to be repaired to construct a safety risk identification and analysis set. Step S2: Analyze and calculate based on the safety risk identification and analysis set to obtain the total downtime baseline value, total downtime optimistic value, and total downtime lazy value of the equipment to be repaired; Step S3: Set up a sliding window for total downtime analysis of the equipment to be repaired, and combine it with Step S2 to divide the total downtime attribute of the safety risk identification and analysis set to obtain the ratio of optimistic attribute and lazy attribute in the total downtime of the equipment to be repaired within the sliding window for total downtime analysis. Step S4: Determine the current total downtime of the equipment to be repaired by analyzing the ratio of optimistic and lazy attributes in the total downtime within the total downtime analysis sliding window, and record it as the estimated total downtime. Step S5: Based on the safety risk identification analysis set analysis, obtain the maintenance start time of the equipment to be maintained, and obtain the maintenance end time of the equipment to be maintained by combining the maintenance start time with the expected total downtime. Step S6: Through steps S1 to S5, the maintenance deadlines of each piece of equipment to be maintained are obtained by synchronously processing each piece of equipment to be maintained. Based on the maintenance deadlines of each piece of equipment to be maintained, the total cumulative maintenance time of all equipment to be maintained is calculated. Step S7: Obtain the group wheel replacement time and the average maintenance rate of each historical maintenance of the equipment to be repaired. Analyze the total cumulative maintenance time and the average maintenance rate of all equipment to be repaired to obtain the time required to complete the maintenance. Identify safety risks by combining the time required to complete the maintenance with the group wheel replacement time.

2. The safety risk identification method based on equipment maintenance according to claim 1, characterized in that, The specific steps of step S1 are as follows: Step S1-1: Extract the fault detection time, waiting time and maintenance time included in the total downtime of each maintenance from the historical maintenance data. Specifically, analyze the start time and end time of each component to obtain the time span value of each component; then sum up the time span values ​​of each component to obtain the total downtime of a maintenance. Step S1-2: Select the unique identifier code of the equipment to be repaired as the key, and select the unique repair number, total downtime, fault detection time, waiting time and repair time of the corresponding single repair as the value. Store the historical repair time of each equipment to be repaired according to the key-value correspondence. Different repairs of the same equipment to be repaired are distinguished by the unique repair number in the value field, and a safety risk identification and analysis set is constructed.

3. The safety risk identification method based on equipment maintenance according to claim 1, characterized in that, The specific steps of step S2 are as follows: Step S2-1: Extract the total downtime corresponding to each maintenance from the safety risk identification and analysis set based on the unique maintenance number. Sum all the extracted total downtimes and divide by the total number of downtimes. The result is used as the total downtime baseline value of the equipment to be maintained. Step S2-2: From the safety risk identification and analysis set, extract the total downtime corresponding to each maintenance based on the unique maintenance number. Compare all the extracted total downtimes, select the total downtime with the smallest value as the total downtime optimistic value, and select the total downtime with the largest value as the total downtime lazy value.

4. The safety risk identification method based on equipment maintenance according to claim 1, characterized in that, The specific steps of step S3 are as follows: Step S3-1: Set the window size of the total shutdown analysis sliding window to the preset number of consecutive maintenance times, and the sliding step size to 1 historical maintenance record; the initial position of the window corresponds to the consecutive N historical maintenance records traced back from the most recent maintenance, where N is the preset value of the window size. Step S3-2: Extract the total downtime and fault detection time corresponding to each maintenance operation covered by the current position of the total downtime analysis sliding window from the safety risk identification and analysis set; Step S3-3: Compare the total downtime of each maintenance in the total downtime analysis sliding window with the total downtime baseline value. Mark the total downtime less than the total downtime baseline value as optimistic attribute, and mark the total downtime not less than the total downtime baseline value as lazy attribute. Step S3-4: Count the total downtime marked as optimistic attribute in the total downtime analysis sliding window, and record it as the first optimistic attribute count; count the total downtime marked as lazy attribute, and record it as the first lazy attribute count.

5. The safety risk identification method based on equipment maintenance according to claim 4, characterized in that, Step S3 further includes: Step S3-5: Sum the fault detection times of each maintenance within the total downtime analysis sliding window and divide the sum by the number of fault detection times within the total downtime analysis sliding window. The result is used as the fault detection baseline value. Step S3-6: Compare the fault detection time of each maintenance in the total downtime analysis sliding window with the fault detection benchmark value. Count the number of fault detection times that do not exceed the fault detection benchmark value and record them as the number of second optimistic attributes. Count the number of fault detection times that exceed the fault detection benchmark value and record them as the number of second lazy attributes.

6. The safety risk identification method based on equipment maintenance according to claim 5, characterized in that, In step S4, the total number of lazy attributes is calculated by weighted fusion based on the number of first lazy attributes and the number of second lazy attributes, and the total number of optimistic attributes is calculated by weighted fusion based on the number of first optimistic attributes and the number of second optimistic attributes; when the total number of optimistic attributes exceeds the total number of lazy attributes, the total downtime optimism value is selected as the current total downtime of the equipment to be repaired. When the number of overall optimistic attributes does not exceed the number of overall lazy attributes, the total downtime lazy value is selected as the current total downtime of the equipment to be repaired, and the selected total downtime is recorded as the estimated total downtime.

7. The safety risk identification method based on equipment maintenance according to claim 1, characterized in that, The specific steps of step S5 are as follows: Step S5-1: Extract the maintenance start time corresponding to each historical maintenance of the equipment to be maintained from the safety risk identification and analysis set. Sort all the extracted maintenance start times and select the median value after sorting as the maintenance start time of the equipment to be maintained in the current group wheel change time. Step S5-2: Add the maintenance start time determined in step S5-1 to the estimated total downtime obtained in step S4, and use the result as the maintenance deadline for the equipment to be maintained.

8. The safety risk identification method based on equipment maintenance according to claim 1, characterized in that, The specific steps of step S6 are as follows: Step S6-1: Through steps S1 to S5, each piece of equipment to be repaired is processed synchronously to obtain the repair start time and repair end time of each piece of equipment in the current group wheel replacement time. Step S6-2: Calculate the time difference between the maintenance deadline and the maintenance start time for each piece of equipment to be maintained, and obtain the individual time required for each piece of equipment to complete maintenance. Step S6-3: Add up the individual times of all the equipment to be repaired, and use the result as the total cumulative time of all the equipment to be repaired.

9. The safety risk identification method based on equipment maintenance according to claim 1, characterized in that, The specific steps of step S7 are as follows: Step S7-1: Extract the number of parallel maintenance dispatches and the group rotation time set by the maintenance group from the preset parameter library of the equipment maintenance management system; the number of parallel maintenance dispatches represents the number of parallel work groups participating in maintenance within the same time period; the group rotation time is preset by the system and is a uniformly stipulated completion time applicable to the rotation maintenance of all equipment to be maintained within the group. Step S7-2: Calculate the average maintenance rate based on the historical maintenance rates. The maintenance rate is expressed as the quotient of the total cumulative time corresponding to the unique maintenance number divided by the number of parallel maintenance dispatches. The average maintenance rate is the sum of the maintenance rates in each historical maintenance divided by the total number of historical maintenance operations involved in the calculation of the average maintenance rate.

10. The safety risk identification method based on equipment maintenance according to claim 9, characterized in that, Step S7 further includes: Step S7-3: Divide the total cumulative time of all equipment to be repaired by the average repair rate to obtain the theoretical total repair time; then divide the theoretical total repair time by the number of parallel repair dispatches to obtain the time required to complete the repair after considering the collaboration of parallel work groups. Step S7-4: Compare the time required to complete the maintenance with the group wheel replacement time. If the time required to complete the maintenance exceeds the group wheel replacement time, it is identified as a safety risk and it is determined that the maintenance of all equipment to be maintained cannot be completed within the specified wheel replacement cycle. A maintenance safety risk warning signal is issued. If the time required to complete the maintenance does not exceed the group wheel replacement time, it is identified as no safety risk at present and the maintenance of all equipment to be maintained can be completed within the specified wheel replacement cycle.

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