Elevator parameter-based retrofit project quotation scheme configuration method and management system

Abstract

The present application relates to the technical field of data processing, in particular to a retrofit project quotation scheme configuration method and management system based on elevator parameters, comprising the following steps: calculating the running time and integrating the standard load by obtaining the start-stop time, load ratio and speed record, combining the multi-time window statistical average load and running frequency to form the degradation driving relationship, screening abnormal data by density deviation and correcting the degradation result, then mapping the component cost and installation cost according to the degradation grade weight to generate the quotation configuration result.In the present application, continuous data is formed by integrating the running time and load ratio, combined with multi-time window statistics, interval frequency correlation and abnormal data correction, the fitting degree of degradation evaluation to the real working condition is enhanced, the degradation degree is mapped and calculated with the component cost, the response ability of the quotation result to the equipment state change is improved, the experience interference is reduced, and the quotation accuracy, consistency and adaptability are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and management system for configuring quotation schemes for elevator renovation projects based on elevator parameters. Background Technology

[0002] The field of data processing technology mainly involves the process of using computers to collect, store, calculate, analyze, and output various types of data. It covers core matters such as business data modeling, parameter matching, rule calculation, and result generation. It is widely used in scenarios such as enterprise operation and management, project evaluation, cost accounting, and quotation formulation. By structuring the input data and processing it according to established rules, it forms result data that can be used for decision-making or other purposes.

[0003] The traditional renovation project quotation scheme configuration method and management system refers to the process in which, during the engineering renovation or equipment upgrade, technicians manually enter parameters through a pre-set quotation form based on basic equipment parameters such as model, specifications, service life, component configuration, and on-site survey records, and then combine experience to calculate and form a quotation scheme. At the same time, data is entered, calculated, and results are output through spreadsheets or simple software. It mainly involves manually organizing parameters, establishing calculation tables, setting price correspondences, and gradually calculating multiple costs to complete the quotation configuration.

[0004] Existing technologies rely on manual parameter input and experience-based matching of price coefficients. The data processing is discrete, and the operational information lacks a continuous correlation with the actual operating conditions of the equipment. This makes it difficult to reflect the impact of changes in operating load and time on component wear. Furthermore, the calculation process is based on static tables and lacks the ability to comprehensively analyze the operating characteristics at multiple time scales. This leads to subjective biases in parameter selection, insufficient stability in the quotation results, and the inability to effectively identify and correct abnormal operating conditions. Consequently, cost assessments may deviate from the actual situation, thus affecting the accuracy of quotations and the reliability of project decisions. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention provides a method for configuring a quotation scheme for a retrofit project based on elevator parameters, comprising the following steps: S1: Obtain elevator start-up and stop times, load ratio and speed change records; calculate the running duration for elevator start-up and stop times; convert the load ratio to a standard load ratio and merge it with the running duration to construct a running dataset. S2: Window the runtime of the running dataset and calculate the average load, accumulate the number of runs within multiple time windows and associate it with the average load to build a segmented index set; S3: Obtain the time length of multiple time windows and perform dimensionality reduction filtering on the segmented index set; calculate the interval running frequency by combining the accumulated running value with the time length and multiply it with the average load to generate a degradation driver set; S4: Obtain speed change records and calculate the deviation degree of all data points according to the distribution density. Combine the preset density threshold to filter the abnormal point set, convert it into a dimensionless correction coefficient and correct the degradation driving set to generate a corrected degradation set. S5: Obtain the preset component cost unit price, perform mapping analysis on the modified degradation set to determine the degradation level range, and extract the weight multiplier. Calculate the individual component price using the weight multiplier and the component cost unit price, and sum this with the preset labor installation cost to generate the project quotation configuration result.

[0006] As a further embodiment of the present invention, the running dataset includes a running duration field, a standard load ratio field, and a start time identifier; the segmented index set includes a time window number, an average load identifier, and a cumulative running count value; the degradation driver set includes interval running frequency, average load weight, and time length parameters; the corrected degradation set includes anomaly deviation, density filtering identifier, and anomaly point number; and the project quotation configuration result includes component quotation parameters, manual installation cost item, and component number mapping.

[0007] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain elevator start-up and stop times, load ratio and speed change records through the data communication interface, calculate the running duration by the difference between elevator start-up and stop times, and arrange multiple running durations in chronological order to generate a running duration sequence; S102: Based on the runtime sequence, obtain the load ratio record, perform percentage conversion on the multiple load ratio values, divide the original ratio value by one hundred to unify the scale, rearrange the converted values ​​according to the time index, and generate a standard load ratio sequence. S103: Based on the runtime sequence and the standard load ratio sequence, align the two sequences in terms of time dimension, perform data splicing operation according to a unified time index, combine the runtime value and load ratio value at the corresponding time and integrate them in order to generate a runtime dataset.

[0008] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the timestamp order, the running duration of the running dataset is windowed, the arithmetic mean of the standard load ratio sequence within multiple time windows is calculated, the values ​​of multiple sampling points in the sequence are accumulated and divided by the total number of sampling points, and then integrated according to the time window number to obtain the window average load value sequence. S202: Retrieve the running count records within multiple windows according to the time window number, perform item-by-item summation on the multiple running count values ​​within the same window, and map and arrange them according to the time window number order to obtain a sequence of accumulated running count values; S203: Align the window average load value sequence with the cumulative value sequence of the number of runs, combine and pair the two types of values ​​under the same time window number, and uniformly associate all window numbers to obtain a segmented index set.

[0009] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Obtain the time length corresponding to multiple time windows, perform dimensionality reduction filtering on the accumulated values ​​and average load of the segmented index set, remove the time window number identifier dimension, extract the numerical sequences of accumulated values ​​and average load and align and reorganize them according to the time index to obtain the dimensionality reduction feature data group. S302: Based on the dimensionality reduction feature data group, perform item-by-item division operations on multiple numerical elements in the running cumulative value sequence and corresponding position elements in the time length sequence to obtain the running frequency values ​​of multiple time windows. Map and arrange the values ​​of all division operations to obtain the interval running frequency sequence. S303: Call the dimensionality reduction feature data group and the interval operation frequency sequence, perform item-by-item multiplication of the average load multiple data items and the corresponding items of the interval operation frequency sequence to obtain the initial driving feature value, filter the driving feature values ​​that exceed the preset loss benchmark threshold and integrate them to generate the degradation driving set.

[0010] As a further aspect of the present invention, the loss reference threshold is determined by obtaining the rated load value and the extreme operating frequency value of the equipment at the factory, multiplying the rated load value and the extreme operating frequency value to obtain the extreme drive reference parameter, and multiplying the extreme drive reference parameter by a preset fatigue tolerance coefficient to perform numerical attenuation calculation.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Obtain all data points of the elevator speed change record, perform adjacent time index difference operation on each data point to obtain the speed gradient, analyze the deviation of the speed gradient from the mean of the whole sequence, sum the squares of the deviation and divide by the number of data points to obtain the distribution density characterization quantity, and establish the density deviation quantity sequence. S402: Extract the deviation values ​​item by item based on the density deviation sequence, perform point-by-point comparison with the preset density threshold, mark the data indexes whose deviation values ​​exceed the density threshold, and aggregate the original velocity data corresponding to all marked indices to obtain the abnormal velocity index set. S403: Calculate the corresponding speed difference sequence based on the abnormal speed index set, normalize the speed difference sequence, perform a weighted summation operation with the degradation driving set using preset weights, and rearrange the superposition results to generate a unified data structure to obtain the corrected degradation set.

[0012] As a further embodiment of the present invention, the density threshold is obtained by acquiring a sample set of velocity change density under the calibration conditions of the equipment, calculating the arithmetic mean of all sample values ​​to obtain the standard density mean, extracting the standard deviation of the sample set and multiplying it by the tolerance coefficient to obtain the fluctuation compensation amount, and then performing an addition operation between the standard density mean and the fluctuation compensation amount to determine the density threshold.

[0013] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Obtain the preset component cost unit price, map the modified degradation set to construct degradation level intervals, call the degradation value sequence and cost unit price sequence in the modified degradation set to divide the intervals, segment the degradation values ​​according to the distribution boundaries and associate them with the corresponding cost unit prices, and generate a degradation interval mapping list. S502: Based on the degradation interval mapping list, obtain the degradation level interval associated weight multiplier, call the interval index sequence and weight coefficient sequence in the list to perform item-by-item matching operation, perform numerical multiplication calculation with the weight multiplier corresponding to multiple intervals and the preset component cost unit price, and aggregate to obtain the component quotation value set; S503: Summing the component price set with the preset labor installation cost, calling multiple values ​​in the price set and performing item-by-item addition with the labor installation cost value, reorganizing the calculation results by component dimension, and generating the project price configuration result.

[0014] The elevator parameter-based retrofit project quotation scheme configuration management system includes: The data analysis module acquires records of elevator start-up and stop times, load ratios, and speed changes. It calculates the running duration based on elevator start-up and stop times, converts the load ratio to a standard load ratio, merges it with the running duration, constructs the running dataset, and transmits it to the segmented statistics module. The segmented statistics module truncates the runtime of the running dataset into windows and calculates the average load. It accumulates the number of runs within multiple time windows and associates them with the average load to construct a segmented index set and pass it to the degradation calculation module. The degradation calculation module obtains the time length of multiple time windows and performs dimensionality reduction and filtering on the segmented index set. It calculates the interval running frequency by combining the accumulated running value with the time length and multiplies it with the average load to generate a degradation driving set and pass it to the anomaly correction module. The anomaly correction module acquires speed change records and calculates the degree of deviation for all data points based on distribution density. It then filters out anomaly point sets by combining preset density thresholds, converts them into dimensionless correction coefficients, corrects the degradation driving set, generates a corrected degradation set, and transmits it to the quotation generation module. The quotation generation module obtains the preset component cost unit price, performs mapping analysis on the modified degradation set to determine the degradation level range, extracts the weight multiplier, calculates the individual component quotation with the component cost unit price, and sums it with the preset labor installation cost to generate the project quotation configuration result.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by standardizing and integrating the operating duration and load ratio to construct continuous operating data, and combining multi-time window statistics and segmented correlation processing, dynamic characterization of operating behavior and load characteristics is achieved. At the same time, based on interval frequency and load coupling calculation, a degradation driving relationship is formed, and density distribution analysis is introduced to filter and superimpose corrections on abnormal data, making the degradation assessment closer to the real working conditions. On this basis, the degree of degradation is mapped to the component cost and weighting coefficients are introduced to participate in the calculation, so that the quotation results can reflect the actual operating status changes of the equipment, reduce reliance on human experience, and improve the consistency and adaptability of the quotation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0019] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0020] Please see Figure 1 This invention provides a method for configuring a quotation scheme for an elevator renovation project based on elevator parameters, including the following steps: S1: Obtain elevator start-up and stop times, load ratio and speed change records; calculate the running duration for elevator start-up and stop times; convert the load ratio to a standard load ratio and merge it with the running duration to construct a running dataset. S2: Window the duration of the running dataset and calculate the average load. Accumulate the number of runs within multiple time windows and associate them with the average load to build a segmented index set. S3: Obtain the time length of multiple time windows and perform dimensionality reduction filtering on the segmented index set. Calculate the interval running frequency by combining the accumulated running value with the time length and multiply it with the average load to generate the degradation driver set. S4: Obtain speed change records and calculate the deviation degree of all data points based on distribution density. Combine with preset density threshold to filter out the abnormal point set, convert it into a dimensionless correction coefficient and correct the degradation driving set to generate the corrected degradation set. S5: Obtain the preset component cost unit price, perform mapping analysis on the modified degradation set to determine the degradation level range, and extract the weight multiplier. Calculate the individual component price using the weight multiplier and the component cost unit price, and sum this with the preset labor installation cost to generate the project quotation configuration result.

[0021] The running dataset includes the running duration field, the standard load ratio field, and the start time identifier. The segmented index set includes the time window number, the average load identifier, and the cumulative value of the number of runs. The degradation driver set includes the interval running frequency, the average load weight, and the time length parameter. The corrected degradation set includes the abnormal deviation degree, the density filtering identifier, and the abnormal point number. The project quotation configuration result includes the component quotation parameters, the labor installation cost item, and the component number mapping.

[0022] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain elevator start-up and stop times, load ratio and speed change records through the data communication interface, calculate the running duration by the difference between elevator start-up and stop times, and arrange multiple running durations in chronological order to generate a running duration sequence; The elevator car's IoT data communication interface at the top acquires real-time elevator start timestamps, stop timestamps, load ratio values, and speed change curves at a fixed sampling period of 100 milliseconds. The data is transmitted via wired network using a common text data format. After acquiring the raw data, data cleaning is performed to remove null records and anomalies caused by network communication delays and jump noise. A difference calculation is then performed on the cleaned elevator start and stop times. The absolute values ​​of the stop and start times for a single operation cycle are obtained, and the difference is calculated to determine the preliminary runtime of a single elevator run. In actual calculations, for example, if the start time of the first run is 08:15:20 AM daily, and the corresponding stop time is 08:15:45 AM, the difference between the absolute seconds for 08:15:45 AM and 08:15:20 AM yields a preliminary runtime of 25 seconds. When setting the system error compensation benchmark, the average processing delay and network transmission delay were calculated to be 1 second by continuously collecting elevator no-load operation data for 50 consecutive times. The difference between the initial running time of 25 seconds and the system error compensation benchmark of 1 second was calculated to obtain the actual running duration of 24 seconds. The multiple actual running duration values ​​obtained from a single calculation were strictly sorted in ascending order according to the chronological order of their corresponding start times. If there were abnormal records with completely identical start times, their stop times were further compared and sorted in ascending order again. After the sorting operation was completed, all sorted actual running duration values ​​were integrated into a continuous one-dimensional data set, thereby generating a running duration sequence. The advantage of this operation logic is that by directly performing a difference operation on high-precision start and end timestamps and introducing system error compensation, the data accumulation error during intermediate state transition processes is avoided. The preset reasonable duration judgment range for a single run is 5 to 120 seconds. The actual run duration of 24 seconds is compared with the upper and lower limits of this judgment range. If 24 seconds is within this range, it is judged as valid and real run data. This valid value constitutes the basic feature element of the subsequent duration sequence.

[0023] S102: Obtain load ratio records based on runtime sequence, perform percentage conversion on multiple load ratio values, perform scale unification by dividing the original ratio value by the percentage conversion benchmark value of one hundred, rearrange the converted values ​​according to time index, and generate a standard load ratio sequence. Based on the precise timestamp information in the generated runtime sequence, the multi-load ratio numerical records synchronously collected by the weighing sensors at the bottom of the elevator car are retrieved. The initial data format of the multi-load ratio numerical records is a raw continuous ratio value within the range of 0 to 150. A percentage conversion operation is performed on the multi-load ratio values. A percentage conversion baseline value of 100 is introduced as the absolute reference denominator for scale unification. A load over-limit alarm threshold is set. By averaging the historical 30 days of full-load operation data, a baseline full-load value of 90 is obtained. The baseline full-load value of 90 is multiplied by a safety redundancy coefficient of 1.2, resulting in a load over-limit alarm threshold of 108. The original ratio value of the sensor is obtained, and a division operation is performed between the original ratio value and the percentage conversion baseline value of 100. The quotient obtained from the division operation is directly used as the standard load ratio value, thus completing the percentage scale unification for different range values. For example, the original ratio value actually measured by the weighing sensor at a specific moment is 85, and the known percentage conversion baseline value is 100. The original ratio value of 85 is divided by the conversion benchmark value of 100 to obtain a quotient of 0.85. This quotient of 0.85 is the standard load ratio value after scaling. This division scaling operation is repeated iteratively for multiple dynamic load records during continuous operation. All converted standard load ratio values ​​are then rearranged in ascending order strictly according to their corresponding time index attributes. The rearranged standard load ratio values ​​are then sequentially concatenated to form a structured continuous data set, thereby generating a standard load ratio sequence. The advantage of this operation logic is that by using a unified fixed benchmark value for the division scaling operation, the impact of data heterogeneity caused by differences in the physical range of different batches of weighing sensors is eliminated. The actual acquired original load ratio value of 85 is compared with the previously set load over-limit alarm threshold of 108. Since 85 is significantly less than 108, the real-time load data is determined to be within the safe and compliant operating range, indicating that the conversion result can be safely and effectively used for subsequent deep-dimensional data concatenation.

[0024] S103: Based on the runtime sequence and the standard load ratio sequence, the two types of sequences are aligned in time dimension, and data splicing is performed according to a unified time index. The runtime values ​​and load ratio values ​​at the corresponding time are combined and sequentially integrated to generate a runtime dataset. Obtain the generated continuous runtime sequence and the scaled standard load ratio sequence. Perform strict time dimension alignment on both sequences. Extract the runtime sequence timestamp features from the runtime sequence and the load sequence timestamp features from the standard load ratio sequence. Perform a step-by-step comparison of the runtime sequence timestamps and the load sequence timestamps. Set a time alignment tolerance threshold. Calculate the basic communication transmission period as 20 milliseconds by taking the reciprocal of the bottom sensor communication frequency. Multiply the basic communication transmission period of 20 milliseconds by the time alignment relaxation factor 2 to obtain a time alignment tolerance threshold of 40 milliseconds. When the absolute difference between the corresponding timestamps of the two sequences is less than the time alignment tolerance threshold of 40 milliseconds, it is determined that the two independently collected data points physically belong to the same occurrence time. Based on the unified time index dimension determined to be the same time, perform data concatenation operations on the corresponding feature values ​​in the two sequences. Combine the calculated actual running duration of 24 seconds at this unified time with the converted standard load ratio value of 0.85 using physical concatenation of consecutive memory addresses. The combined feature data blocks are strictly integrated and sorted according to the absolute chronological order of the unified time index. To clearly demonstrate the specific implementation data of the multi-dimensional time alignment and combination operations, a corresponding data table is constructed to record the relevant status integration details.

[0025] Table 1 Sample Data Table for Running the Dataset

[0026] Table 1 shows some sample operational data after time alignment and feature concatenation. Through the above continuous feature integration and sorting operations, a structured operational dataset with multi-dimensional feature attributes is generated.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the timestamp order, the running duration of the running dataset is windowed, the arithmetic mean of the standard load ratio sequence within multiple time windows is calculated, the values ​​of multiple sampling points in the sequence are accumulated and divided by the total number of sampling points, and then integrated according to the time window number to obtain the window average load value sequence. Obtain the generated structured runtime dataset and read its continuous time indices. Set the time window length constant to 3600 seconds. Perform time-division slicing on the runtime dataset's time indices at fixed 3600-second intervals to generate multiple consecutive, non-overlapping time windows. Assign each time window an incrementing integer as its time window number, following absolute chronological order; for example, assign time window number 1 to the first time window of each day. Extract the standard load ratio sequence corresponding to time window number 1 and obtain the values ​​of multiple sampling points contained in this sequence. Substitute the previously calculated standard load ratio value of 85 into the sequence and obtain the other two standard load ratio values ​​of 42 and 15 within the same time window number 1. Perform an arithmetic summation on these three values ​​to obtain a cumulative window load value of 142. Obtain the total number of sampling points within time window number 1, which is 3. Divide the cumulative window load value of 142 by the total number of sampling points of 3 to obtain a window average load value of 47.33. A load assessment benchmark threshold is set, and the upper limit of the ratio corresponding to the rated load specified on the elevator's nameplate (100) is obtained. This upper limit of 100 is multiplied by the normal operating coefficient of 0.5, resulting in a load assessment benchmark threshold of 50. The calculated window average load value of 47.33 is compared with the load assessment benchmark threshold of 50. Since 47.33 is less than 50, the elevator operation within time window number 1 is determined to be under light load, reflecting the rationality of the effective calculation data. The calculated window average load value of 47.33 is mapped, associated, and stored with its corresponding time window number 1. The above slicing, accumulation, division, and integration operations are repeated iteratively for all time window numbers, finally splicing together to generate a one-dimensional continuous sequence of window average load values. The advantage of this calculation logic is that the arithmetic average calculation eliminates the interference of single extreme load fluctuations on the overall operating load assessment.

[0028] S202: Retrieve the run count records within multiple windows based on the time window number, perform item-by-item summation on the multiple run count values ​​within the same window, and map and arrange them according to the time window number order to obtain the run count summation value sequence; Based on the assigned set of time window numbers, the independent operation count records within the time period corresponding to each time window number are retrieved from the underlying operation log of the elevator control main board. A complete operation is defined as a closed loop of complete mechanical motion from elevator start-up acceleration to deceleration and stop, with each complete motion recorded as a count value of 1. The count values ​​of multiple independent operation events within time window number 1 are extracted. The multiple operation count values ​​within the same time window number 1 are then continuously accumulated. Within time window number 1, the first operation event containing an actual operating duration of 24 seconds is detected, along with 11 subsequent independent operation events. These 12 independent count values ​​of 1 are added one by one to obtain a cumulative operation count value of 12 within time window number 1. A peak frequency judgment benchmark is set. By retrieving historical operation count records for the same time period over the past 30 days, the arithmetic mean is calculated by summing and dividing by the number of days, resulting in 8 times. The historical arithmetic mean of 8 times is multiplied by the congestion relief coefficient of 1.5 to obtain a peak frequency judgment benchmark value of 12 times. The calculated cumulative run count value 12 is compared with the peak frequency judgment benchmark value 12. If the two values ​​are equal, it is determined that the elevator operating frequency within time window number 1 has reached the peak load critical state and exhibits high-frequency operation characteristics. Following the ascending order of time window numbers starting from 1, time window number 1 is mapped and bound to the calculated cumulative run count value 12. The process continues to retrieve and process subsequent time window number 2. If the accumulated run count value is 8, it is mapped and arranged immediately after the record of time window number 1. After completing the item-by-item accumulation and sequential mapping operation for all time window numbers, a continuously arranged sequence of cumulative run count values ​​is generated. The advantage of this operation logic is that by accumulating the absolute operating frequency item by item, the start-stop wear frequency of elevator mechanical components within a specific time period is quantified.

[0029] S203: Align the numbering based on the window average load value sequence and the cumulative value sequence of running times, combine and pair the two types of values ​​under the same time window number, and uniformly associate all window numbers to obtain a segmented index set; The generated window average load value sequence and the cumulative run count value sequence are retrieved synchronously. A strict number alignment operation is performed on the numbering attributes contained within these two sequences. The first index feature, i.e., the time window number, is extracted from the window average load value sequence, and the corresponding index feature, i.e., the time window number, is extracted from the cumulative run count value sequence. The extracted time window numbers from the two sequences are compared one by one. When the time window numbers in the two sequences are determined to be completely equal, for example, both are time window number 1, the corresponding physical quantity values ​​under that same number are extracted. The window average load value of 47.33 under time window number 1, calculated in the previous steps, is extracted and substituted into the cumulative run count value of 12 under time window number 1. The window average load value of 47.33 and the cumulative run count value of 12 are physically combined at the memory level to construct a two-dimensional paired data pair containing the two numerical features. A threshold set for identifying abnormal operating conditions was established. The average load (40) and average number of runs (10) for 24 time windows throughout the day were obtained. The average load (40) was multiplied by a deviation coefficient of 1.2 to obtain a load deviation from the baseline (48), and the average number of runs (10) was multiplied by the deviation coefficient of 1.2 to obtain a frequency deviation from the baseline (12). The average load value (47.33) of the combined and paired window was compared with the load deviation from the baseline (48), and the cumulative number of runs (12) was compared with the frequency deviation from the baseline (12). Since 47.33 is less than 48 and 12 equals 12, the combined and paired data pair was determined to be in a normal high-frequency operating state. This two-dimensional data combination accurately represents the basic operating condition within the time window. The above equivalent comparison and combination pairing operations were performed iteratively on all time window numbers. All generated combined and paired data pairs were uniformly associated and spliced ​​according to the ascending order of the time window numbers. After integration, a multi-dimensional segmented index set was generated. The advantage of this operational logic is that it achieves deep fusion of multi-dimensional features of elevator load and operating frequency on a time scale through unified numbering alignment and two-dimensional combination pairing.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Obtain the time length corresponding to multiple time windows, perform dimensionality reduction filtering on the accumulated values ​​and average load of the segmented index, remove the dimension of time window number identifier, extract the numerical sequences of accumulated values ​​and average load and align and reorganize them according to the time index to obtain the dimensionality reduction feature data group. The generated segmented index set is retrieved, and the two-dimensional combined paired data pairs and their corresponding time window numbers are read. The previously set time window length constant of 3600 seconds is extracted as the corresponding time length for multiple time windows. Dimensionality reduction filtering is performed on the data structure within the segmented index set, traversing the multi-dimensional segmented index set to identify and locate the time window number identifier dimension as the associated primary key. Field removal is performed to physically delete the time window number from the data record, breaking the original segmented independence state based on the number. After removal, the remaining pure numerical feature data, namely the cumulative running value and average load value sequence, are extracted. The window average load value of 47.33 and the cumulative running count value of 12 under time window number 1 obtained from the previous steps are substituted into the data. After removing time window number 1, these two physical attribute values ​​are retained. The absolute time sequence inherent in the underlying data acquisition is used as the new time index, and strict time series alignment and recombination are performed on the cumulative running value and average load value after removing the number. Pure numerical items extracted from multiple time windows are sequentially concatenated according to the direction of time flow to obtain a continuous dimensionality-reduced feature data set after removing discrete labels. The dimensionality reduction validation baseline dimension is set to 2. The number of feature fields contained in the current dimensionality-reduced feature data set is calculated to obtain an actual dimension of 2. The actual dimension 2 is compared with the dimensionality reduction validation baseline dimension 2. If the two values ​​are equal, the dimensionality reduction filtering operation is considered successful in removing redundant structured noise labels. This two-dimensional continuous feature array constitutes a pure physical state time series.

[0031] S302: Based on the dimensionality reduction feature data group, perform item-by-item division operations between multiple numerical elements in the running cumulative value sequence and the corresponding elements in the time length sequence to obtain the running frequency values ​​of multiple time windows. Map and arrange the values ​​of all division operations to obtain the interval running frequency sequence. Based on the generated dimensionality-reduced feature data set, the sequence of accumulated running values ​​and the corresponding time length sequence are extracted. A step-by-step division operation is performed between multiple numerical elements in the accumulated running value sequence and the corresponding elements in the time length sequence. The accumulated running count value of 12 corresponding to the first sorting position (substituted in the previous step) is obtained, and the corresponding time length of 3600 seconds for the multiple time windows is simultaneously obtained. The accumulated running count value of 12 is divided by the time length of 3600 seconds, yielding a running frequency value of 0.0033 for the first time window. The accumulated running value of the next sorting position is then extracted. If the obtained value is 9, the same division operation is performed with the corresponding time length of 3600 seconds, yielding a running frequency value of 0.0025 for the second time window. This step-by-step division operation is repeated for all numerical pairs within the dimensionality-reduced feature data set to obtain standardized frequency values ​​at all time window scales. The series of values ​​obtained from all division operations are then sequentially mapped and combined according to the time occurrence order determined by the aforementioned dimensionality reduction and recombination, thus obtaining a continuous one-dimensional interval running frequency sequence. To clearly demonstrate the specific mapping of the interval operation frequency calculation process, a corresponding statistical table is constructed to record the relevant calculation values.

[0032] Table 2. Integrated Table of Inter-regional Operation Frequency Data

[0033] Table 2 lists the actual numerical correspondences of frequency sequences generated by division and mapping within some consecutive time periods. Setting the lower limit for active operation frequency judgment to 0.0020, the first calculated operation frequency value of 0.0033 is compared with the lower limit of 0.0020. Since 0.0033 is greater than 0.0020, the equipment is determined to be in a high-frequency start-up workload mode within this interval. This value effectively characterizes the operational density of this interval. The advantage of this calculation logic is that by introducing a unified time length benchmark for division standardization, it eliminates the interference of potential time sampling truncation bias on frequency evaluation.

[0034] S303: Call the dimensionality reduction feature data group and the interval operation frequency sequence, perform item-by-item multiplication of the average load multiple data items and the corresponding items of the interval operation frequency sequence to obtain the initial driving feature value, filter the driving feature values ​​that exceed the preset loss benchmark threshold and integrate them to generate the degradation driving set. The generated dimensionality-reduced feature data set and interval operation frequency sequence are invoked. The retained average load multi-data item sequence is extracted from the dimensionality-reduced feature data set, and the frequency corresponding items arranged in the same time order are extracted from the interval operation frequency sequence. Item-by-item multiplication is performed on the corresponding sequence elements of these two sets of synchronized data. The window average load value of 47.33, retained from the previous step, and the interval operation frequency of 0.0033 calculated from the previous step corresponding to the first sorting position are substituted into the equation. The window average load value of 47.33 and the interval operation frequency of 0.0033 are multiplied to obtain the initial driving feature value of 0.1561 for this time interval. The multiplication operation is performed iteratively on all corresponding sequence element pairs to generate the complete initial driving feature value sequence. A preset wear threshold is set. The average value of the baseline driving characteristics (0.1000) of the tested equipment, after 2000 hours of continuous operation in a factory-exposed, wear-free state, is retrieved. This average value is multiplied by the material fatigue tolerance coefficient (1.20) to obtain a preset wear threshold of 0.1200. The generated sequence of initial driving characteristic values ​​is iterated, and each value is compared to the preset wear threshold of 0.1200. The calculated initial driving characteristic value (0.1561) is compared with the preset wear threshold. Since 0.1561 is greater than 0.1200, the physical operating stage corresponding to this value is determined to have exceeded the normal material fatigue tolerance range. All driving characteristic values ​​greater than 0.1200, such as 0.1561, are extracted and uniformly spliced ​​together according to their original time sequence to generate a degradation driving set characterizing the mechanical deterioration state of the equipment. The advantage of this operational logic is that it couples static stress with dynamic start-stop fatigue by multiplying the average load with the operating frequency, thereby quantifying the degree of composite mechanical loss.

[0035] Please see Figure 5 The specific steps of S4 are as follows: S401: Obtain all data points of the elevator speed change record, perform adjacent time index difference operation on each data point to obtain the speed gradient, analyze the deviation of the speed gradient from the mean of the whole sequence, sum the squares of the deviation and divide by the number of data points to obtain the distribution density characterization quantity, and establish the density deviation quantity sequence. The system retrieves the full data of elevator speed changes recorded in real time by the photoelectric encoder on the traction machine spindle, and reads the time index and corresponding transient speed value generated according to a specific sampling frequency. It extracts the transient speed value of 1.50 m / s corresponding to the first sampling time index and the transient speed value of 1.70 m / s corresponding to the adjacent second sampling time index. A subtraction difference operation is performed on the data at these two adjacent time points, subtracting the 1.50 from the 1.70 of the first time point to obtain the speed change of 0.20 m / s. A division operation is performed at a fixed sampling time interval of 0.10 seconds, dividing the speed change of 0.20 by the time interval to obtain the corresponding speed gradient value of 2.00 m / s². The same difference and division operations are performed on all data points of the elevator speed change records to generate a complete speed gradient sequence. The arithmetic mean of the speed gradient samples from all normal and stable operation phases over the past 24 hours is calculated to obtain the mean of the entire sequence, 1.20 m / s². The calculated velocity gradient value of 2.00 is subtracted from the mean of the entire sequence of 1.20, resulting in an offset value of 0.80. This offset value of 0.80 is then squared to obtain a discrete value of 0.64 for a single data point. The second and third groups of data points are then processed with the same method, yielding squared discrete values ​​of 0.36 and 0.50. These are then summed by adding 0.64, 0.36, and 0.50 to obtain a total sum of 1.50 within the continuous segment. The total number of data points involved in the current summation is 3. Dividing the sum of 1.50 by the number 3 yields a distribution density representation of 0.50. All generated representations are arranged and integrated according to their absolute chronological order to establish a density deviation sequence. This representation of 0.50 is compared to a preset normal distribution baseline value of 0.30. The higher value indicates a higher degree of velocity oscillation within a local time period.

[0036] S402: Extract the deviation values ​​item by item based on the density deviation sequence, perform point-by-point comparison with the preset density threshold, mark the data indexes whose deviation values ​​exceed the density threshold, and aggregate the original velocity data corresponding to all marked indices to obtain the abnormal velocity index set; The established density deviation sequence is invoked, and the distribution density characteristics contained therein are extracted item by item according to the timestamp order. A historical density deviation benchmark sample set under healthy elevator operation conditions is obtained, and the average deviation of the first 1000 normal operation cycles in this sample set is calculated to be 0.20. This average value of 0.20 is multiplied by the dynamic fault tolerance multiplier of 1.50 to obtain a preset density threshold of 0.30. The deviation value of the first distribution density characteristic calculated in the previous process, 0.50, is compared point by point with the preset density threshold of 0.30. If the current deviation value of 0.50 is greater than the preset density threshold of 0.30, it indicates that there is a non-negligible mechanical jerking phenomenon in the traction machine speed at the corresponding time point. A specific numerical label 1 is generated as an anomaly marker for the sequence data index positions that meet this condition, and a numerical label 0 is generated as a normal marker for positions where the deviation value is less than or equal to the preset density threshold of 0.30. All data index positions marked with numerical label 1 are extracted and traced back to the original elevator speed change log. Based on the tagged data index, extract its corresponding raw transient velocity data and substitute it with the aforementioned raw transient velocity value of 1.70 m / s that caused the abnormal fluctuation. Perform splicing and centralized storage operations on all indices marked with 1 and their associated raw velocity data within the entire evaluation time window. Integrate the discretely distributed abnormal velocity points into a continuous data block according to their original chronological order of occurrence, and output the abnormal velocity index set.

[0037] S403: Calculate the corresponding speed difference sequence based on the abnormal speed index set, normalize the speed difference sequence, perform a weighted summation operation with the degradation driving set using preset weights, and rearrange the superposition results to generate a unified data structure to obtain the corrected degradation set. Read the generated abnormal speed index set and extract the corresponding original abnormal speed values ​​contained therein. Retrieve the ideal operating reference speed values ​​for the corresponding floor interval preset by the elevator inverter's internal controller, and extract the reference speed value for the rated constant speed stage of the current section as 1.50 m / s. Subtract the extracted original abnormal speed value of 1.70 m / s from the ideal operating reference speed value of 1.50 m / s to obtain an absolute speed difference of 0.20 m / s. Divide the absolute speed difference of 0.20 by the reference speed value of 1.50 to obtain a dimensionless relative speed difference ratio of 0.1333. Repeat this subtraction and division operation for all original speed values ​​in the abnormal speed index set to generate a speed difference sequence mapped from the original time series. Retrieve the degraded drive set generated and retained in the aforementioned process, extract the drive feature values ​​at the same time dimension, and substitute them into the initial drive feature value of 0.1561 calculated in the aforementioned process for this time node. The calculated relative velocity difference ratio of 0.1333 is added to the initial driving characteristic value of 0.1561, resulting in a superposition of 0.2894. Comparing this superposition result of 0.2894 with the initial characteristic value of 0.1561 before single superposition, the significant increase indicates that the additional dynamic stress damage component caused by abnormal velocity fluctuations has been effectively compensated for in the mechanical wear quantification evaluation at this node. All superposition results generated across the entire sequence are then re-compared and rearranged according to descending order, placing high-risk severe degradation quantification indicators at the beginning of the sequence to generate a uniformly decreasing data structure. After the overall fusion and rearrangement, the corrected degradation set is output.

[0038] Please see Figure 6 The specific steps of S5 are as follows: S501: Obtain the preset component cost unit price, map the modified degradation set to construct degradation level intervals, call the degradation value sequence and cost unit price sequence in the modified degradation set to divide the intervals, segment the degradation values ​​according to the distribution boundaries and associate them with the corresponding cost unit prices, and generate a degradation interval mapping list. Retrieve the preset component cost unit price recorded in the materials management database, and obtain the basic material unit price of the traction sheave component as 4500 yuan. Read the unified descending rearranged data structure generated in the previous process, i.e., the corrected degradation set, and extract the degradation value sequence. Substitute the first degradation value of 0.2894 output by the previous process. Obtain the extreme value of the maximum degradation state in the same batch of historical maintenance records as the maximum distribution boundary value of 0.4500, and retrieve the preset loss benchmark threshold of 0.1200 generated above as the minimum distribution boundary value. Perform a subtraction difference operation on these two boundary parameters, subtracting the minimum distribution boundary value of 0.1200 from the maximum distribution boundary value of 0.4500 to obtain the total value span of 0.3300. Divide this total value span of 0.3300 by the preset grading constant of 3 to obtain an equal grading step span value of 0.1100. The minimum distribution boundary value of 0.1200 and the graded step span value of 0.1100 are numerically added together to obtain the first-level boundary parameter of 0.2300. Further addition is performed on this first-level boundary parameter of 0.2300 and the graded step span value of 0.1100 to obtain the second-level boundary parameter of 0.3400. Based on these two key boundary parameters, three degradation level intervals are established: a low-risk operating area, a medium-risk monitoring area, and a high-risk replacement area. The first degradation value of 0.2894 in the modified degradation set is called, and this value is compared one by one with the first-level boundary parameter of 0.2300 and the second-level boundary parameter of 0.3400. Since the degradation value of 0.2894 is greater than 0.2300 and less than 0.3400, this comparison result indicates that the current physical state of the component falls into the medium-risk monitoring area. Based on the classification results for this region, a field binding and association operation is performed on physical memory with the corresponding preset component cost unit price of 4500 yuan to generate a degradation interval mapping list. The advantage of this operation logic is that by performing equidistant segmented calculations based on extreme value spans on continuous degradation state values ​​and performing physical association, a structured mapping dictionary between the degree of physical degradation and the basic material benchmark is established.

[0039] S502: Based on the degradation interval mapping list, obtain the degradation level interval associated weight multiplier, call the interval index sequence and weight coefficient sequence in the list to perform item-by-item matching operation, perform numerical multiplication calculation with the weight multiplier corresponding to multiple intervals and the preset component cost unit price, and aggregate to obtain the component quotation numerical set; Read the generated degradation interval mapping list and extract the degradation level interval identifier for the medium-risk monitoring area. Retrieve the weight configuration information from the maintenance cost experience knowledge base, extracting a weight multiplier of 0.20 for low-risk operating areas, 0.60 for medium-risk monitoring areas, and 1.00 for high-risk replacement areas, thus constructing a weight coefficient sequence. Based on the interval index sequence stored in the degradation interval mapping list, extract the medium-risk monitoring area index corresponding to the first item. Call the aforementioned weight coefficient sequence and perform a string-by-string matching operation between the extracted interval index and the identifier in the weight coefficient sequence. If a match is successful, retrieve the target weight multiplier of 0.60 corresponding to the medium-risk monitoring area. Extract the preset component cost unit price of 4500 yuan bound to the degradation interval mapping list. Perform a numerical multiplication calculation between the obtained target weight multiplier of 0.60 and the preset component cost unit price of 4500 yuan. The resulting preliminary price adjustment value for the current traction wheel component is 2700 yuan. Compile a detailed table of numerical values ​​to illustrate the batch matching calculation process.

[0040] Table 3 Multi-interval weighted multiplier matching calculation table

[0041] Table 3 lists the correlation results of extracting corresponding weights and performing multiplication to obtain quotation values ​​under three different degradation risk ranges. The preliminary quotation correction values ​​obtained from multiple iterations of matching and multiplication calculations are aggregated according to the unique identification code of each component, centralizing discrete amount values ​​belonging to the same maintenance project to obtain a complete set of component quotation values. The generated first quotation correction value of 2700 yuan is compared with the basic maintenance reserve fund benchmark amount of 2000 yuan. Since 2700 yuan is greater than 2000 yuan, it indicates that the current moderate degradation state of the component has triggered the condition for calling additional risk maintenance reserve funds. The advantage of this calculation logic is that by introducing weight multipliers to perform multiplication modulation calculations, the probability factor of mechanical failure is quantified and converted into the actual expected cost of spare parts.

[0042] S503: Summing the component quotation value set with the preset labor installation cost, calling multiple values ​​in the quotation value set and performing item-by-item addition with the labor installation cost value, reorganizing the calculation results according to the component dimension, and generating the project quotation configuration result; The generated component quotation set is retrieved, and the first corrected quotation value stored according to the component identification code is extracted. This value is then substituted into the previously calculated value of 2700 yuan obtained through multiplication. Simultaneously, a request is sent to the labor settlement database to obtain the preset labor installation cost benchmark for the traction sheave component, and the standard installation hourly rate is read as 1200 yuan. Multiple discrete monetary values ​​contained in the component quotation set are retrieved, and the corresponding preset labor installation cost value of 1200 yuan is extracted. A step-by-step addition operation is performed on these two sets of data elements with the same component identification tag. The previously substituted component quotation correction value of 2700 yuan is added to the corresponding labor installation cost of 1200 yuan, calculating the total comprehensive maintenance cost for this specific traction sheave component to be 3900 yuan. For multiple different types of underlying equipment components included in this maintenance task, the above process of extracting quotation values ​​and labor installation costs and performing addition operations is repeated iteratively to obtain a series of sequences of total individual costs including material and labor costs. The total amount of all individual items is calculated and then hierarchically restructured according to the original physical structure of the mechanical equipment system. After the tree-structured reorganization of all discrete data records, the project quotation configuration result covering all elements is summarized and output. The calculated total amount of 3900 yuan is compared with the customer's pre-approved upper limit of 3500 yuan for individual maintenance budget. Since 3900 yuan is greater than the upper limit of 3500 yuan, this comparison result prompts the maintenance specialist to immediately initiate the special fund supplement approval process. The advantage of this calculation logic is that, through the direct addition and superposition of hard component costs and flexible labor costs, as well as structured reorganization, it achieves comprehensive and unrestricted statistical correlation of all underlying maintenance cost elements.

[0043] Please see Figure 7 A configuration management system for elevator parameter-based retrofit project pricing schemes, including: The data analysis module acquires records of elevator start-up and stop times, load ratios, and speed changes. It calculates the running duration based on elevator start-up and stop times, converts the load ratio to a standard load ratio, merges it with the running duration, constructs the running dataset, and transmits it to the segmented statistics module. The segmented statistics module extracts a window of the running duration of the running dataset and calculates the average load. It accumulates the number of runs within multiple time windows and associates them with the average load, constructs a segmented index set, and passes it to the degradation calculation module. The degradation calculation module obtains the time length of multiple time windows and performs dimensionality reduction filtering on the segmented index set. It calculates the interval running frequency by combining the accumulated running value with the time length and multiplies it with the average load to generate a degradation driving set and pass it to the anomaly correction module. The anomaly correction module acquires speed change records and calculates the degree of deviation for all data points based on distribution density. It then filters out the set of anomalies by combining them with a preset density threshold, converts them into dimensionless correction coefficients, corrects the degradation driver set, generates a corrected degradation set, and passes it to the quotation generation module. The quotation generation module obtains the preset component cost unit price, performs mapping analysis on the modified degradation set to determine the degradation level range, extracts the weight multiplier, calculates the individual component quotation with the component cost unit price, and sums it with the preset labor installation cost to generate the project quotation configuration result.

[0044] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for configuring a quotation scheme for a renovation project based on elevator parameters, characterized in that, Includes the following steps: S1: Obtain elevator start-up and stop times, load ratio and speed change records; calculate the running duration for elevator start-up and stop times; convert the load ratio to a standard load ratio and merge it with the running duration to construct a running dataset. S2: Window the runtime of the running dataset and calculate the average load, accumulate the number of runs within multiple time windows and associate it with the average load to build a segmented index set; S3: Obtain the time length of multiple time windows and perform dimensionality reduction filtering on the segmented index set; calculate the interval running frequency by combining the accumulated running value with the time length and multiply it with the average load to generate a degradation driver set; S4: Obtain speed change records and calculate the deviation degree of all data points according to the distribution density. Combine the preset density threshold to filter the abnormal point set, convert it into a dimensionless correction coefficient and correct the degradation driving set to generate a corrected degradation set. S5: Obtain the preset component cost unit price, perform mapping analysis on the modified degradation set to determine the degradation level range, and extract the weight multiplier. Calculate the individual component price using the weight multiplier and the component cost unit price, and sum this with the preset labor installation cost to generate the project quotation configuration result.

2. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 1, characterized in that, The running dataset includes a running duration field, a standard load ratio field, and a start time identifier. The segmented index set includes a time window number, an average load identifier, and a cumulative running count value. The degradation driver set includes interval running frequency, average load weight, and time length parameters. The corrected degradation set includes anomaly deviation, density filtering identifier, and anomaly point number. The project quotation configuration result includes component quotation parameters, manual installation cost item, and component number mapping.

3. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain elevator start-up and stop times, load ratio and speed change records through the data communication interface, calculate the running duration by the difference between elevator start-up and stop times, and arrange multiple running durations in chronological order to generate a running duration sequence; S102: Based on the runtime sequence, obtain the load ratio record, perform percentage conversion on the multiple load ratio values, divide the original ratio value by one hundred to unify the scale, rearrange the converted values ​​according to the time index, and generate a standard load ratio sequence. S103: Based on the runtime sequence and the standard load ratio sequence, align the two sequences in terms of time dimension, perform data splicing operation according to a unified time index, combine the runtime value and load ratio value at the corresponding time and integrate them in order to generate a runtime dataset.

4. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the timestamp order, the running duration of the running dataset is windowed, the arithmetic mean of the standard load ratio sequence within multiple time windows is calculated, the values ​​of multiple sampling points in the sequence are accumulated and divided by the total number of sampling points, and then integrated according to the time window number to obtain the window average load value sequence. S202: Retrieve the running count records within multiple windows according to the time window number, perform item-by-item summation on the multiple running count values ​​within the same window, and map and arrange them according to the time window number order to obtain a sequence of accumulated running count values; S203: Align the window average load value sequence with the cumulative value sequence of the number of runs, combine and pair the two types of values ​​under the same time window number, and uniformly associate all window numbers to obtain a segmented index set.

5. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Obtain the time length corresponding to multiple time windows, perform dimensionality reduction filtering on the accumulated values ​​and average load of the segmented index set, remove the time window number identifier dimension, extract the numerical sequences of accumulated values ​​and average load and align and reorganize them according to the time index to obtain the dimensionality reduction feature data group. S302: Based on the dimensionality reduction feature data group, perform item-by-item division operations on multiple numerical elements in the running cumulative value sequence and corresponding position elements in the time length sequence to obtain the running frequency values ​​of multiple time windows. Map and arrange the values ​​of all division operations to obtain the interval running frequency sequence. S303: Call the dimensionality reduction feature data group and the interval operation frequency sequence, perform item-by-item multiplication of the average load multiple data items and the corresponding items of the interval operation frequency sequence to obtain the initial driving feature value, filter the driving feature values ​​that exceed the preset loss benchmark threshold and integrate them to generate the degradation driving set.

6. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 5, characterized in that, The loss reference threshold is determined by obtaining the rated load value and the extreme operating frequency value of the equipment at the factory, multiplying the rated load value and the extreme operating frequency value to obtain the extreme drive reference parameter, and multiplying the extreme drive reference parameter by a preset fatigue tolerance coefficient to perform numerical attenuation calculation.

7. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Obtain all data points of the elevator speed change record, perform adjacent time index difference operation on each data point to obtain the speed gradient, analyze the deviation of the speed gradient from the mean of the whole sequence, sum the squares of the deviation and divide by the number of data points to obtain the distribution density characterization quantity, and establish the density deviation quantity sequence. S402: Extract the deviation values ​​item by item based on the density deviation sequence, perform point-by-point comparison with the preset density threshold, mark the data indexes whose deviation values ​​exceed the density threshold, and aggregate the original velocity data corresponding to all marked indices to obtain the abnormal velocity index set. S403: Calculate the corresponding speed difference sequence based on the abnormal speed index set, normalize the speed difference sequence, perform a weighted summation operation with the degradation driving set using preset weights, and rearrange the superposition results to generate a unified data structure to obtain the corrected degradation set.

8. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 7, characterized in that, The density threshold is determined by acquiring a sample set of velocity variation density under the equipment calibration conditions, calculating the arithmetic mean of all sample values ​​to obtain the standard density mean, extracting the standard deviation of the sample set and multiplying it by the tolerance coefficient to obtain the fluctuation compensation amount, and then adding the standard density mean and the fluctuation compensation amount to determine the density threshold.

9. The method for configuring a quotation scheme for a renovation project based on elevator parameters according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Obtain the preset component cost unit price, map the modified degradation set to construct degradation level intervals, call the degradation value sequence and cost unit price sequence in the modified degradation set to divide the intervals, segment the degradation values ​​according to the distribution boundaries and associate them with the corresponding cost unit prices, and generate a degradation interval mapping list. S502: Based on the degradation interval mapping list, obtain the degradation level interval associated weight multiplier, call the interval index sequence and weight coefficient sequence in the list to perform item-by-item matching operation, perform numerical multiplication calculation with the weight multiplier corresponding to multiple intervals and the preset component cost unit price, and aggregate to obtain the component quotation value set; S503: Summing the component price set with the preset labor installation cost, calling multiple values ​​in the price set and performing item-by-item addition with the labor installation cost value, reorganizing the calculation results by component dimension, and generating the project price configuration result.

10. A configuration management system for quotation schemes of elevator retrofit projects based on elevator parameters, characterized in that, The system is used to implement the method for configuring a retrofit project quotation scheme based on elevator parameters as described in any one of claims 1-9, and the system includes: The data analysis module acquires records of elevator start-up and stop times, load ratios, and speed changes. It calculates the running duration based on elevator start-up and stop times, converts the load ratio to a standard load ratio, merges it with the running duration, constructs the running dataset, and transmits it to the segmented statistics module. The segmented statistics module truncates the runtime of the running dataset into windows and calculates the average load. It accumulates the number of runs within multiple time windows and associates them with the average load to construct a segmented index set and pass it to the degradation calculation module. The degradation calculation module obtains the time length of multiple time windows and performs dimensionality reduction and filtering on the segmented index set. It calculates the interval running frequency by combining the accumulated running value with the time length and multiplies it with the average load to generate a degradation driving set and pass it to the anomaly correction module. The anomaly correction module acquires speed change records and calculates the degree of deviation for all data points based on distribution density. It then filters out anomaly point sets by combining preset density thresholds, converts them into dimensionless correction coefficients, corrects the degradation driving set, generates a corrected degradation set, and transmits it to the quotation generation module. The quotation generation module obtains the preset component cost unit price, performs mapping analysis on the modified degradation set to determine the degradation level range, extracts the weight multiplier, calculates the individual component quotation with the component cost unit price, and sums it with the preset labor installation cost to generate the project quotation configuration result.

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