Multi-level index calculation and early warning method for centralized apartment full-process data

Abstract

The application discloses a multi-level index calculation and early warning method for centralized apartment whole-process data, and the method comprises the following steps: S1, deconstructing the business stage of the whole process of apartment operation, establishing a three-layer hierarchical index structure, and forming a logical framework; S2, collecting multi-source business data through a data interface, and forming a time series basic data set after cleaning and normalization processing; S3, based on a pre-defined rule base, standardizing batch calculation of the data set, and sequentially generating index result sets of the basis, analysis and target control; S4, implementing hierarchical statistics and dynamic threshold monitoring on key indexes, and realizing abnormal early warning and root cause auxiliary analysis; and S5, visualizing the index results and monitoring signals and generating decision suggestions. The application systematically integrates multi-source whole-process operation data, fundamentally solves the problem of data model fragmentation, and enables the original data to be converted into standardized operation indexes which can be directly used for analysis and correlation.

Description

Technical Field

[0001] This invention relates to the field of apartment rental operation and management technology, specifically to a multi-level indicator calculation and early warning method for centralized apartment full-process data. Background Technology

[0002] Driven by both policy support and market demand, the centralized apartment rental industry has undergone years of large-scale development and has become a stable, well-managed, and standardized component of the housing rental market. This sector typically involves professional organizations operating entire buildings or floors of apartments, providing standardized decoration, furnishings, and management services to meet the long-term rental needs of new urban residents and young people. It has played a positive role in improving the rental housing supply system and stabilizing market rent levels.

[0003] However, the industry currently lacks a systematic, standardized, and benchmarkable set of operating indicators, mainly in the following aspects:

[0004] Lack of indicator dimensions: Traditional apartment management is mostly limited to basic indicators such as occupancy rate and rental income, and lacks unified definition and statistics for operational indicators such as vacancy day classification, membership card matching rate, sales / operation efficiency, and the proportion of corporate clients;

[0005] Inconsistent calculation logic: Different companies use different methods to calculate similar indicators (such as annualized order volume, vacancy rate, and renewal rate), which makes it difficult to effectively benchmark the data and form a standardized operation evaluation system.

[0006] As a result, existing apartment management systems, especially when dealing with high-concurrency, multi-dimensional, and highly time-varying rental business scenarios, suffer from data fragmentation and static monitoring mechanisms when processing multi-source operational data. This makes it impossible to directly use the data for indicator calculation, generate structured and interconnected operational indicators, and dynamically adapt to business fluctuations. Summary of the Invention

[0007] To address this, the present invention provides a multi-level indicator calculation and early warning method for centralized apartment full-process data, in order to solve the problems of data fragmentation and static monitoring mechanisms in the existing technology, which result in data that cannot be directly used for indicator calculation, cannot generate structured and interconnected operational indicators, and is difficult to dynamically adapt to business fluctuations.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A multi-level indicator calculation and early warning method for centralized apartment full-process data includes the following steps:

[0010] S1: Access and deconstruct the entire operation process data of apartment rental, divide the entire operation process data into five continuous business stages, and establish a three-level hierarchical indicator structure based on hierarchical indicators to form a framework for the relationship between indicator catalog and stages.

[0011] S2: Collect apartment status data in real time from the apartment management system through the application programming interface, clean and normalize the status data, and establish data time series labels to obtain the basic dataset;

[0012] S3: Based on a predefined indicator calculation rule base and relational framework, perform batch calculations on the basic dataset and generate actual indicator values ​​in the hierarchical order of basic data indicators, analytical calculation indicators, and target control indicators to obtain the indicator result set;

[0013] S4: Based on the preset hierarchical rules, the actual indicator values ​​of each level in the output indicator result set of S3 are mapped to business intervals with different reaction durations, and compared with the dynamic threshold in real time to realize anomaly warning and root cause analysis.

[0014] S5: Call the actual indicator values ​​at all levels in the indicator result set and the comparison results generated in step S4, and generate a visualization view and decision suggestions according to the preset template.

[0015] Furthermore, the five consecutive business stages include property preparation, leasing, move-in, service, lease renewal or termination.

[0016] Furthermore, the hierarchical indicator structure includes basic data indicators, analytical calculation indicators, and target control indicators; basic data indicators are used to record objective facts; analytical calculation indicators determine the business status by performing mathematical operations on the basic data indicators; and target control indicators are used to compare the actual calculation results of the analytical calculation indicators with preset management target values ​​to determine the degree of target achievement.

[0017] Furthermore, the specific steps of S3 are as follows:

[0018] S3.1: Extract basic indicators from the basic dataset based on the rules of the indicator calculation rule base;

[0019] S3.2: Calculate and analyze indicators using basic indicators and predefined formulas in the indicator calculation rule base;

[0020] S3.3: After obtaining the results of the analysis and calculation of the indicators, calculate them with the preset target values, and then enter the target control layer for calculation to obtain the indicator result set.

[0021] Furthermore, the predefined indicator calculation rule library includes indicators for property management, order transactions, customer service, personnel efficiency, revenue, and customer structure.

[0022] Furthermore, the specific steps of S4 are as follows:

[0023] S4.1: Based on the pre-configured business rules, divide each indicator in the result set of the output of S3 into multiple ordered level intervals, and count the number of rooms belonging to each interval respectively;

[0024] S4.2: Calculate dynamic thresholds based on historical data for each indicator. ;

[0025] S4.3: Compare the actual indicator values ​​in the indicator result set output by S3 with the dynamic threshold. Real-time comparison is performed to determine whether the actual value exceeds the dynamic threshold to trigger an anomaly alarm; the actual value is also determined to determine whether it reaches the target value to assess the degree of performance achievement.

[0026] Furthermore, the dynamic threshold The calculation formula is as follows:

[0027]

[0028] in, This is the average of the indicator's historical values ​​over the past complete period. The standard deviation of historical values. It is an integer multiple of the standard deviation.

[0029] Furthermore, the standard deviation The calculation formula is as follows:

[0030]

[0031] in, It is the first indicator One historical data value;

[0032] average value The calculation formula is as follows:

[0033]

[0034] in, It refers to the number of historical data points.

[0035] The present invention has the following advantages: By establishing a three-level hierarchical indicator structure and a relationship framework with five continuous business stages, and by performing layer-by-layer calculations on the basic dataset formed after cleaning and normalization based on a predefined indicator calculation rule base, the present invention generates a structured indicator result set, systematically integrates multi-source operation process data, fundamentally solves the problem of data model fragmentation, and enables the original data to be transformed into standardized operation indicators that can be directly used for analysis and correlation.

[0036] Meanwhile, the introduction of dynamic threshold early warning and hierarchical interval statistics enables the system to identify abnormal indicators in real time and provide early warnings of potential risks, helping managers to shift from passive response to proactive intervention and providing complete data intelligence support for the full-process operation and risk management of centralized apartments.

[0037] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. Attached Figure Description

[0038] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0039] Figure 1 This is a flowchart illustrating the implementation of the multi-level indicator calculation and early warning method for centralized apartment full-process data in this invention. Detailed Implementation

[0040] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Please see Figure 1 The multi-level indicator calculation and early warning method for centralized apartment full-process data includes the following steps:

[0042] S1: Access and deconstruct the entire operation process data of apartment rental, divide the entire operation process data into five continuous business stages: property preparation, leasing, occupancy, service, lease renewal or termination, and establish a three-level hierarchical indicator structure based on hierarchical indicators to form a framework for the relationship between indicator catalog and stages, thereby providing a logical framework for the construction of the operation indicator system.

[0043] The data is divided into several stages: the housing preparation stage, including data on housing receipt and decoration configuration; the leasing stage, including data on marketing and contract signing; the move-in stage, including data on tenant check-in procedures and key handover; the service stage, including data on maintenance, cleaning, consultation and other customer service provided during the tenant's tenancy; and the lease renewal or termination stage, including data on renewal negotiations or termination applications, property inspection and settlement before the contract expires, thus ensuring full-process data coverage.

[0044] The tiered indicator structure includes basic data indicators, analytical calculation indicators, and target control indicators. Basic data indicators record objective facts, such as the number of rooms in the pre-opening phase and the number of newly signed contracts. Analytical calculation indicators perform mathematical operations on basic data indicators to determine the inherent status, efficiency, or ratio relationships of the business, thus defining the business status; for example, calculating the vacancy rate based on the number of vacant and online rooms. Target control indicators compare the actual calculation results of the analytical calculation indicators with preset management target values ​​to determine the degree of target achievement, such as the vacancy rate budget achievement rate. Finally, each business stage is associated and integrated with its corresponding three-tiered indicators, forming a framework for the relationship between the indicator catalog and stages. This provides a business logic framework for determining the data collection scope and designing specific calculation formulas for subsequent steps.

[0045] S2: Collects apartment status data in real time from the apartment management system via an application programming interface (API), cleans and normalizes the status data, and establishes time-series labels to obtain the basic dataset. The apartment management system includes a property management subsystem, an order transaction subsystem, a customer service subsystem, a human resources subsystem, and a financial subsystem.

[0046] Status data includes fields collected from the property management subsystem such as room ID, physical status (e.g., vacant, rented, under repair), status update time, area, and configuration; fields collected from the order transaction subsystem such as contract ID, associated room ID, tenant ID, contract start and end dates, rent amount, contract type (new, renewed, terminated), and payment status; fields collected from the customer service subsystem such as customer complaint ticket ID, associated room ID or contract ID, complaint type, submission time, processing status, closure time, and customer rating; fields collected from the human resources subsystem such as employee ID, affiliated store, department, job title (e.g., sales, operations), and start date; and fields collected from the finance subsystem such as contract ID, amount receivable, amount received, payment date, and fee type (rent, service fee, deposit).

[0047] Cleaning and normalization processes can handle missing values ​​and filter invalid records in the collected status data. For example, for records missing the "contract end date", the data can be extrapolated based on the "contract period" field or marked as abnormal for review. Virtual data used for system testing and temporary orders with a status of "cancelled" are also deleted.

[0048] All date and time fields from data from different subsystems are uniformly converted to Coordinated Universal Time (UTC) or a specified standard time zone format; discrete values ​​used within each system that represent the same meaning (such as property status "vacant", "available for rent", and "awaiting rent" being uniformly mapped to "vacant") are mapped to a preset standard encoding table; and all fields involving currency amounts are uniformly converted to a specified base currency unit (such as RMB) based on the exchange rate.

[0049] Finally, time series labels are established for the data: each valid data record that has undergone cleaning and normalization is labeled with a corresponding statistical period based on the time of its core business occurrence. All this data can be aligned according to a unified time series framework, thus obtaining the basic dataset.

[0050] S3: Based on a predefined indicator calculation rule base and relational framework, it performs batch calculations on the basic dataset and generates actual indicator values ​​sequentially according to the hierarchical order of basic data indicators, analytical calculation indicators, and target control indicators, resulting in an indicator result set. This facilitates the transformation of basic data into business-meaning indicator values ​​through standardized calculation logic.

[0051] For example, directly count the number of online rooms and the total number of customer complaints from basic data indicators. Then, based on the results of the basic data indicators, call the corresponding standardized formulas to calculate and analyze the indicators. For example, use the obtained number of vacant rooms and number of online rooms to calculate the vacancy rate in the property management indicators.

[0052] The specific steps for S3 are as follows:

[0053] S3.1: Based on the rules in the indicator calculation rule base, extract basic indicators from the basic dataset. For example: count the number of room IDs in the rentable state to get the number of online rooms; count the number of contracts whose status changes to signed within a specified time to get the number of newly signed contracts; and summarize all customer complaint work order records to get the total number of customer complaints.

[0054] S3.2: Calculates and analyzes indicators using predefined formulas from the basic indicators and indicator calculation rule base. The calculation process follows the defined dependencies between indicators, forming a chained call. Details are as follows:

[0055] The predefined indicator calculation rule base includes indicators for property management, order transactions, customer service, staff efficiency, revenue, and customer structure.

[0056] Property management metrics include the number of rooms listed, the number of vacant rooms, the vacancy rate, the average number of vacant days, the number of rooms available for rent, the estimated number of days to be vacated, and the occupancy rate.

[0057] The vacancy rate represents the ratio of the number of vacant rooms to the number of online rooms at a given time; the formula is: Vacancy rate = Number of vacant rooms / Number of online rooms;

[0058] The estimated number of days for inventory clearance is used to predict the inventory clearance efficiency of housing resources; the formula is: Estimated number of days for inventory clearance = number of leasable rooms / (account clearance in the past 7 days / 7);

[0059] Average vacancy days are used to assess the average duration of property vacancy, enabling a more refined assessment of property vacancy duration;

[0060] The formula is: Average vacancy days = ∑ (number of days each vacant room remains vacant this time) / total number of vacant rooms.

[0061] Order transaction metrics include pre-order volume, direct contract volume, lease renewal volume, annualized transaction volume, lease renewal rate, short / long lease contract ratio, and store default rate.

[0062] The annualized transaction volume is used to convert orders from different lease periods into a unified annual caliber for comparison. The formula is: Annualized transaction volume = Σ(the number of contracts signed for the i-th order × the contract period (months) of the order) / 12.

[0063] Renewal rate: Used to measure tenant retention. The formula is: Renewal rate = (Number of rooms renewed during the statistical period / Number of rooms whose contracts expire during the same period) × 100%.

[0064] Store default rate is used to measure contract performance risk. The formula is: Store default rate = ∑ (number of successful defaults and lease terminations per day in the current month) / Σ (number of valid contracts that have been in effect in the current month).

[0065] Customer service metrics include the number of customer complaints, the complaint rate, the customer complaint handling completion rate, the response time rate, and the number of customer complaints at each level.

[0066] Among them, the customer complaint rate is used to assess service pressure in conjunction with business scale. The formula is: Customer complaint rate = (Total number of customer complaints in the statistical period / Average number of rooms rented in the same period) × 100%.

[0067] The customer complaint handling completion rate is used to quantify customer service response efficiency. The formula is: Customer Complaint Status = Ratio of Customer Complaints that do not require handling and those that have been handled to the total number of customer complaints.

[0068] Personnel efficiency metrics include the number of employees in the operations team, the number of employees in the sales team, the efficiency of employees in the sales team, the efficiency of employees in the operations team, and the percentage of transactions completed in the operations team.

[0069] Among them, the sales sequence efficiency is used to measure the per capita output of the sales team. The formula is: Sales sequence efficiency = Sales sequence employee sales volume / Total number of employees in the sales sequence.

[0070] The operational transaction ratio is used to calculate the contribution of different business series. The formula is: Operational Transaction Ratio: Operational Series Transaction Volume / Sales Volume.

[0071] Revenue metrics include contract rent, average transaction price, membership card sales revenue, membership card matching rate, and installment service fee revenue.

[0072] The membership card pairing rate is used to measure the sales penetration of value-added services. The formula is: Membership Card Pairing Rate = (Number of transactions involving membership card purchases / Total number of transactions meeting the statistical criteria) × 100%

[0073] Customer structure metrics include the number of corporate tenants, the number of individual tenants, the percentage of corporate contracts, and the cumulative percentage of corporate contracts.

[0074] The percentage of corporate contracts is used to analyze customer structure and achieve accurate classification and statistics of customer structure. The formula is: Percentage of corporate contracts = Number of corporate contracts / Number of resident contracts (the number of corporate contracts is the sum of the number of corporate contracts signed and the number of corporate individual contracts signed) × 100%.

[0075] After obtaining the results of the analysis and calculation indicators, they are compared with the preset target values, and then the calculation is carried out at the target control layer to obtain the indicator result set. The advantage is that the business analysis results of the operating indicators are transformed into subsequent measurable performance evaluation results.

[0076] For example, the calculated actual vacancy rate is compared with the preset vacancy rate target value to generate the vacancy rate budget achievement rate: Vacancy rate budget achievement rate = (vacancy rate target value / actual vacancy rate) × 100%.

[0077] S4: Based on preset hierarchical rules, the actual indicator values ​​at each level in the output indicator set of S3 are mapped to business intervals with different reaction durations, and compared in real time with dynamic thresholds to achieve anomaly warning and root cause analysis. This facilitates the determination of whether the actual indicator values ​​at each level are changing abnormally and provides early warning of potential risks. The specific steps of S4 are as follows:

[0078] S4.1: Based on the pre-configured business rules, each indicator in the result set of the output of S3 is divided into multiple ordered level intervals, and the number of rooms belonging to each interval is counted to reflect the concentration and dispersion of the vacancy period of the housing resources.

[0079] For example, the vacancy rate index is categorized into five preset ranges: within 7 days, 8 to 13 days, 14 to 29 days, 30 to 99 days, and more than 100 days.

[0080] S4.2: Calculate dynamic thresholds based on historical data for each indicator. This allows for the reasonable setting of threshold values ​​based on past data fluctuations, enabling timely detection of abnormal indicator changes and early warning of potential risks. The calculation formula is as follows:

[0081]

[0082] in, This is the average of the indicator's historical values ​​over the past complete period. The standard deviation of historical values. It is an integer multiple of the standard deviation. The calculation formula is as follows:

[0083]

[0084] in, It is the first indicator A historical data value, It refers to the number of historical data points.

[0085] average value The calculation formula is as follows:

[0086]

[0087] S4.3: Compare the actual indicator values ​​in the indicator result set output by S3 with the dynamic threshold. Real-time comparison is performed to determine whether the actual value exceeds the dynamic threshold to trigger anomaly alarms; whether the actual value reaches or exceeds the target value to assess performance achievement; and at the same time, the results of hierarchical statistics (such as a surge in the number of rooms vacant for more than 30 days) are combined to assist in attributing the causes of anomaly alarms.

[0088] S5: Call the actual indicator values ​​at all levels in the indicator result set (such as vacancy rate, membership card matching rate, store default rate) and the comparison results generated in step S4, and generate a visualization view and decision suggestions according to the preset template.

[0089] When a certain indicator triggers an abnormal alarm (such as the vacancy rate exceeding the dynamic threshold T) or the performance evaluation result is not achieved (such as the membership card matching rate being lower than the target value), an early warning is issued and the corresponding rule in the decision rule base is matched to generate a text suggestion.

[0090] This invention establishes a three-tiered hierarchical indicator structure and a framework relating it to five consecutive business stages. Based on a predefined indicator calculation rule base, it performs layer-by-layer calculations on the cleaned and normalized basic dataset, ultimately generating a structured indicator result set. This systematically integrates multi-source operational data throughout the entire process, fundamentally solving the problem of data model fragmentation and enabling the original data to be transformed into standardized operational indicators that can be directly used for analysis and correlation.

[0091] Meanwhile, the introduction of dynamic threshold early warning and hierarchical interval statistics enables the system to identify abnormal indicators in real time (such as deviations in the distribution of vacancy days) and provide early warnings of potential risks (such as rising default rates). This helps managers shift from passive response to proactive intervention, providing complete data intelligence support for the full-process operation and risk management of centralized apartments.

[0092] The following example demonstrates the implementation process and synergistic effect of this technical solution by monitoring and optimizing the vacancy rate and membership card pairing rate of a centralized apartment project in October 2023.

[0093] In the S2 phase: The system collects basic data for October 2023 from various subsystems through the application programming interface: the number of online rooms collected from the housing management subsystem is 500, of which 60 are vacant and 60 are available for rent; the total number of sales orders in the past 7 days of the month collected from the order transaction subsystem is 21, and the number of transactions for purchasing membership cards in the month is 120, with a total of 200 transactions meeting the statistical criteria.

[0094] In phase S3: The vacancy rate is calculated as 60 / 500 × 100% = 12%. The estimated number of days for inventory clearance is calculated as 60 / (21 / 7) = 20 days. The membership card pairing rate is calculated as (120 / 200) × 100% = 60%.

[0095] In phase S4, the preset target values ​​for vacancy rate are 8% and membership card pairing rate are 65%, and dynamic thresholds are calculated by using historical data from the past 12 months. The initial value was 10%. Upon comparison, the actual value of 12% exceeded the dynamic threshold. The system triggered a severe anomaly alarm when the target value was not met. The system automatically correlated the vacancy day statistics, finding that 40% of rooms were vacant for 30 to 99 days, and further attributed this to the expected occupancy period of up to 20 days. Regarding the membership card pairing rate, the actual value of 60% fell short of the preset target of 65%, and the system marked this as a failure to meet performance targets.

[0096] In the S5 phase, the visualization engine uses red alerts on the operations dashboard to indicate abnormal vacancy rates and displays a trend chart showing that membership card pairing rates have not met targets. The decision-making suggestion module automatically generates suggestions based on the linked analysis results and pushes them to the store manager's terminal.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-level indicator calculation and early warning method for centralized apartment full-process data, characterized in that, Includes the following steps: S1: Access and deconstruct the entire operation process data of apartment rental, divide the entire operation process data into five continuous business stages, and establish a three-level hierarchical indicator structure based on hierarchical indicators to form a framework for the relationship between indicator catalog and stages. S2: Collect apartment status data in real time from the apartment management system through the application programming interface, clean and normalize the status data, and establish data time series labels to obtain the basic dataset; S3: Based on a predefined indicator calculation rule base and relational framework, perform batch calculations on the basic dataset and generate actual indicator values ​​in the hierarchical order of basic data indicators, analytical calculation indicators, and target control indicators to obtain the indicator result set; S4: Based on the preset hierarchical rules, the actual indicator values ​​of each level in the output indicator result set of S3 are mapped to business intervals with different reaction durations, and compared with the dynamic threshold in real time to realize anomaly warning and root cause analysis. S5: Call the actual indicator values ​​at all levels in the indicator result set and the comparison results generated in step S4, and generate a visualization view and decision suggestions according to the preset template.

2. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 1, characterized in that, The five consecutive business phases include property preparation, leasing, move-in, service, lease renewal or termination.

3. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 1, characterized in that, The hierarchical indicator structure includes basic data indicators, analytical calculation indicators, and target control indicators. Basic data indicators are used to record objective facts. Analytical calculation indicators determine the business status by performing mathematical operations on the basic data indicators. Target control indicators are used to compare the actual calculation results of the analytical calculation indicators with the preset management target values ​​to determine the degree of target achievement.

4. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 1, characterized in that, The specific steps of S3 are as follows: S3.1: Extract basic indicators from the basic dataset based on the rules of the indicator calculation rule base; S3.2: Calculate and analyze indicators using basic indicators and predefined formulas in the indicator calculation rule base; S3.3: After obtaining the results of the analysis and calculation of the indicators, calculate them with the preset target values, and then enter the target control layer for calculation to obtain the indicator result set.

5. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 4, characterized in that, The predefined indicator calculation rule base includes indicators for property management, order transactions, customer service, personnel efficiency, revenue and income, and customer structure.

6. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 1, characterized in that, The specific steps of S4 are as follows: S4.1: Based on the pre-configured business rules, divide each indicator in the result set of the output of S3 into multiple ordered level intervals, and count the number of rooms belonging to each interval respectively; S4.2: Calculate dynamic thresholds based on historical data for each indicator. ; S4.3: Compare the actual indicator values ​​in the indicator result set output by S3 with the dynamic threshold. Real-time comparison is performed to determine whether the actual value exceeds the dynamic threshold to trigger an anomaly alarm; the actual value is also determined to determine whether it reaches the target value to assess the degree of performance achievement.

7. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 1, characterized in that, The dynamic threshold The calculation formula is as follows: in, This is the average of the indicator's historical values ​​over the past complete period. The standard deviation of historical values. It is an integer multiple of the standard deviation.

8. The multi-level indicator calculation and early warning method for centralized apartment full-process data according to claim 7, characterized in that, The standard deviation The calculation formula is as follows: in, It is the first indicator One historical data value; average value The calculation formula is as follows: in, It refers to the number of historical data points.

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