A method and system for calculating property operation costs based on Cartesian product

Abstract

This invention discloses a method and system for calculating property operation costs based on Cartesian product, belonging to the fields of property management, financial informatization, and data mining technology. This invention solves the problems of existing property cost calculation methods, which rely on manual experience, are inefficient and crude, suffer from inaccurate allocation of indirect costs leading to distorted service profitability, and are rigid, difficult to adapt to dynamic changes, and complex to implement in traditional activity-based costing models. This invention constructs a Cartesian product fully correlated matrix of standard operations and cost drivers, enabling exhaustive mapping and refined correlation configuration of cost drivers. Combined with dynamic data collection, rule engine judgment, and machine learning optimization, it forms an intelligent cost calculation system that can automatically execute and adaptively adjust, thereby significantly improving the accuracy, efficiency, and decision support capabilities of cost calculation.

Description

Technical Field

[0001] This invention relates to the fields of property management, financial informatization and data mining technology, specifically a method and system for calculating property operation costs based on the Cartesian product. Background Technology

[0002] In the property management industry, accurate and efficient cost calculation and allocation are the core foundation for improving management efficiency and achieving lean operation.

[0003] Currently, the following problems generally exist in the calculation of property management costs in residential communities: 1. Experience-based and extensive: Cost estimation relies heavily on the project manager's personal experience, employing a simplistic "historical cost + estimate" model, lacking scientific and detailed cost driver analysis. This leads to inaccurate pricing, distorted profit forecasts, and an inability to effectively support market competition and internal lean management.

[0004] 2. Confusion of cost objects: Indirect costs such as public water and electricity, management personnel, and salaries of employees in the "four guarantees and one service" system cannot be accurately allocated to specific service items (such as security, cleaning, landscaping, maintenance, etc.), making it difficult to measure the true profit and loss of a single service.

[0005] 3. Lack of dynamic adaptability: Traditional calculation models are rigid and cannot quickly respond to dynamic factors such as changes in community size (e.g., newly delivered buildings), adjustments to service standards, and price fluctuations. Each change requires a large amount of manual recalculation, which is inefficient.

[0006] 4. Data silo problem: Business data (such as the number of work orders and the area inspected) is separated from financial data, making it impossible to establish a precise link between "operations consuming resources and services consuming operations".

[0007] Activity-based costing (ABC) is an advanced cost management method, but its implementation in the property management industry is difficult. The main bottlenecks are the complexity of activity division, cost driver determination, and the calculation of massive amounts of data. However, existing technologies lack a systematic solution that can solidify, standardize, and automatically execute this methodology.

[0008] Therefore, it does not meet the existing needs. In response, we propose a property operation cost calculation method and system based on the Cartesian product. Summary of the Invention

[0009] The purpose of this invention is to provide a method and system for calculating property operation costs based on Cartesian product. By constructing a Cartesian product mapping matrix between standard operations and cost drivers, establishing a refined correlation logic between operations and drivers, and combining dynamic data collection and machine learning optimization, a standardized, automated, and intelligent cost calculation solution is formed, which solves the problems mentioned in the background technology.

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for calculating property operation costs based on the Cartesian product, comprising the following steps: Step 1: Construct a standard operation library and a cost driver library. The standard operation library includes multiple standard operation activities, each of which is associated with standard working hours and material quotas. The cost driver library includes multiple cost drivers, each of which is associated with a unit of measurement. Step 2: Establish a Cartesian product mapping matrix of tasks and drivers. By performing a Cartesian product operation on the standard task set and the cost driver set, a full task-driver association probability matrix is ​​generated, and logical judgment and weight assignment are performed on each association pair. Step 3: Collect and clean basic data, including community master data, actual values ​​of driving factors, and resource cost data; Step 4: Calculate cost allocation based on the Cartesian product mapping matrix. Select the corresponding cost calculation formula according to the job type, combine the actual value of the driver, the dynamically allocated unit price and the weight coefficient to calculate the cost of each job, and use machine learning algorithms to dynamically optimize the weights in the job-driver association mapping matrix based on the actual cost data. Step 5: Cost solidification and calculation output. Based on the calculation results, a standard cost quota library is formed, and a cost calculation report is automatically generated according to the input community parameters.

[0011] Furthermore, in step two, the process of establishing the Cartesian product mapping matrix includes: Let the standard job set be J = {j1,j2,j3,...,j...} m Let m be the total number of tasks, and let the set of cost drivers be D = {d1, d2, d3, ..., d}. n}, where n is the total number of motivations; Perform a Cartesian product operation on the standard operation set J and the cost driver set D to generate an ordered set M0 of all possible operation-cost driver pairs, where M0 = J × D = {(j i ,d j )| j i ∈J, d j ∈D}; For each association pair (j) in M0 i ,d j The process involves using a logical decision function F and a weighting function W to generate an associated state flag r. i and initial weight w i The mapping matrix M is given by the association status flags, which include strong association, configurable association, and invalid association.

[0012] Furthermore, the logical decision function F is implemented based on a configurable business rule base, and the weight function W is based on historical data fitting, expert experience assignment, or default value setting.

[0013] Furthermore, in step two, the logical judgment includes: A strong correlation indicates that the cost driver is a necessary factor driving the standard operating procedure. If a relationship is determined to be configurable, it means that the relationship requires user confirmation or configuration of specific coefficients. If deemed invalid, it means that the cost driver has no business relevance to the standard operation and is therefore excluded.

[0014] Furthermore, in step four, selecting the corresponding cost calculation formula based on the activity type includes: For internal operations, the following formula is used: Activity cost = ∑(actual value of each cost driver associated with the activity × dynamic allocation unit price of the cost driver × weight of the cost driver); For outsourced work, the following formula is used: Operating cost = Contractual unit price × Contractual quantity.

[0015] Furthermore, in step four, the machine learning algorithm is gradient descent, and the weight update formula is w. it＋1 =w it -η·( L / w i )=w it -2η·( t -y t )·x it , where w i Let η be the weight of cost driver i at period t, η be the learning rate, and L be the loss function constructed based on the deviation between predicted cost and actual cost.

[0016] Furthermore, step four also includes anomaly data processing steps, specifically including: Before cost calculation, perform anomaly detection on the actual values ​​of the drivers, including threshold detection, statistical detection, and correlation detection; Outliers are automatically replaced or truncated during the calculation process; After the calculation is completed, the output results are validated for reasonableness, including validation of individual item proportions and validation of unit cost range.

[0017] Furthermore, in step five, the standard cost quota library is extracted from cost data of multiple projects, including standard cost quotas under different business formats and service levels, supporting rapid cost calculation for new projects.

[0018] A property operation cost calculation system based on Cartesian product, used to implement a property operation cost calculation method based on Cartesian product, including: The data acquisition layer is used to build and maintain the standard operating procedure library and cost driver library, and to collect basic data of the project to be measured. The model calculation layer includes a model building engine, a Cartesian product mapping engine, a dynamic cost calculation engine, and a quota fixing engine; A model building engine for standardizing operational and motivational data; The Cartesian product mapping engine is used to perform Cartesian product operations on standard operations and cost drivers, and generates an operation-driver association mapping matrix through logical judgment and weight initialization by the rule engine. A dynamic cost calculation engine is used to calculate the cost of each standard operation based on the association mapping matrix and the basic data; The quota solidification engine is used to generate and optimize the standard cost quota library; The results presentation layer is used to generate and display cost calculation reports, manage the standard cost quota library, and provide multi-dimensional visualization analysis.

[0019] Furthermore, the Cartesian product mapping engine further includes a user interface for displaying job-driver association pairs that are determined to be configurable associations to the user, and receiving confirmation, weight adjustment, or rejection operations from the user for the association pair. The dynamic cost calculation engine has a built-in unified calculation framework, a machine learning optimization module, and an anomaly handling module. The machine learning optimization module is used to periodically and dynamically optimize weights based on actual cost data, and the anomaly handling module is used to detect, correct, and verify data before, during, and after calculation.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a standard operation library and a cost driver library, and generates a full-scale correlation probability matrix by performing a Cartesian product operation on the two. This changes the traditional extensive mode that relies on experience and manual selection of drivers. The method systematically establishes a fine mapping relationship between operations and drivers through logical judgment and weight assignment, ensuring the integrity and scientific nature of the cost allocation model. This solves the problems of confusion in the allocation of indirect costs and difficulty in measuring the profit and loss of a single service, and provides a reliable data foundation for the refined pricing and lean management of property services.

[0021] 2. This invention introduces a dynamic association determination mechanism based on a rule engine and a weight adaptive optimization mechanism based on machine learning algorithms, enabling the cost calculation model to have dynamic adaptability and self-learning ability. It can automatically identify strong associations, configurable associations, and invalid associations according to business rules, and continuously iterate and optimize the weight coefficients using historical cost data. This allows it to automatically respond to changes in cell size, service standard adjustments, and market price fluctuations, thereby overcoming the shortcomings of traditional static models that are rigid and have low adjustment efficiency, and realizing intelligent and real-time cost calculation. Attached Figure Description

[0022] Figure 1 The core table structure diagram for the property operation cost calculation based on the Cartesian product of this invention is shown below. Figure 2 This is a flowchart of the core algorithm for calculating property operation costs based on the Cartesian product in this invention. Figure 3 This is the dynamic weight optimization graph of the present invention; Figure 4 This is a diagram illustrating the overall architecture of the property operation cost calculation system based on the Cartesian product of the present invention. Figure 5 This is a data flow diagram for cost allocation calculation in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0024] To address the shortcomings of existing property cost estimation methods, such as reliance on manual experience leading to inefficiency, inaccurate allocation of indirect costs resulting in distorted service profitability, and the rigidity, inability to adapt to dynamic changes, and complex implementation of traditional activity-based costing models, please refer to [link to relevant documentation]. Figures 1-5 This embodiment provides the following technical solution: A method for calculating property operating costs based on the Cartesian product includes the following steps: Step 1: Construct a standard operation library and a cost driver library. The standard operation library includes multiple standard operation activities, each of which is associated with standard working hours and material quotas. The cost driver library includes multiple cost drivers, each of which is associated with a unit of measurement. Step 2: Establish a Cartesian product mapping matrix of tasks and drivers. By performing a Cartesian product operation on the standard task set and the cost driver set, a full task-driver association probability matrix is ​​generated, and logical judgment and weight assignment are performed on each association pair. Step 3: Collect and clean basic data, including community master data, actual values ​​of driving factors, and resource cost data; Automatically collect basic data from various data sources (such as ERP, work order systems, building automation systems, and human resources systems): Community master data: total building area, chargeable area, public area area, number of buildings, number of units, total number of households, green area, etc.

[0025] Actual values ​​of the driving factors: the specific cleaning area of ​​each region, the number of planned patrol routes, the total number of public lights, etc.

[0026] Resource cost data: personnel wages, benefits, energy consumption unit price, material procurement price, etc.; Step 4: Calculate cost allocation based on the Cartesian product mapping matrix. Select the corresponding cost calculation formula according to the job type, combine the actual value of the driver, the dynamically allocated unit price and the weight coefficient to calculate the cost of each job, and use machine learning algorithms to dynamically optimize the weights in the job-driver association mapping matrix based on the actual cost data. Step 5: Cost solidification and calculation output. Based on the calculation results, a standard cost quota library is formed, and a cost calculation report is automatically generated according to the input community parameters.

[0027] The technical effects of the above solution are as follows: By introducing Cartesian product operations to construct a full-scale operation-cost driver correlation matrix, the systematicness and completeness of cost driver identification can be ensured, avoiding omissions that may occur in traditional methods, thereby improving the refinement and accuracy of cost aggregation. Combined with the mechanism of logical judgment and weight assignment, the complex multidimensional relationship between operations and cost drivers can be effectively mapped, thereby enhancing the explanatory power and adaptability of the model. By integrating actual data cleaning, dynamic cost calculation and machine learning optimization, the continuous self-correction of weight coefficients and dynamic iteration of cost allocation models are realized, thereby significantly improving the objectivity and predictive ability of the calculation results. The resulting standard cost quota library and automated report output can quickly respond to the parameterized calculation needs of different communities, thus providing efficient, reliable and scalable data-driven support for property management companies' cost control, budget preparation and pricing decisions.

[0028] Please see Figure 1 In step one, a standard operating procedure library and a cost driver library (model foundation) are constructed. Standard Operating Procedures (SOPs) library: Defines all standard operating activities in property management. Examples include: daily lobby cleaning, hallway garbage collection, park security patrols, public lighting maintenance, and customer service reception. Each operation has a unique code and standard working hours and material quotas. Cost Driver Library: Defines the key factors that drive the cost of each activity. Examples include: cleaning area, garbage collection frequency, number of patrol points, number of public lighting fixtures, and number of residents. Each driver has a unit of measurement. Tables 1 and 2 below explain the core fields of each table: Table 1 defines the standard job library (standard_job) and is the starting point for all cost calculations.

[0029] Table 2 Cost Driver Library (cost_driver) Table 2 defines "why it changes", which are cost variables and calculation dimensions.

[0030] Table 3 Job-Driver Mapping Table is key to achieving flexible combinations, defining "which job is affected by which driver, and how they are affected".

[0031] Table 4 Property Area Table (property_area) Table 4 defines "where to do it", which is the main carrier of the motivation value and usually has a tree-like hierarchical structure.

[0032] Table 5, Area Driver Value Table, stores the specific numerical value of the driver, which is dynamic and changes over time.

[0033] In step one, the model supports the following core process for Cartesian product calculation: Basic Combinations: Perform a Cartesian product between standard_job and property_area to generate all basic combinations of "what to do where"; Related drivers: Find all cost drivers associated with each job through job_driver_mapping; Bind value: Retrieve the specific driver value from area_driver_value based on the region and driver; Cost Calculation: Ultimately, the cost calculation formula for each combination is: Total man-hours = std_man_hour + SUM(coefficient * driver_value). The system automatically adds up the impact of all relevant drivers to achieve refined calculation.

[0034] like Figure 2As shown, in step two, the process of establishing the Cartesian product mapping matrix includes: Let the standard job set be J = {j1,j2,j3,...,j...} m Let m be the total number of tasks, and let the set of cost drivers be D = {d1, d2, d3, ..., d}. n}, where n is the total number of motivations; Perform a Cartesian product operation on the standard operation set J and the cost driver set D to generate an ordered set M0 of all possible operation-cost driver pairs, where M0 = J × D = {(j i ,d j )| j i ∈J, d j ∈D}; For each association pair (j) in M0 i ,d j The process involves using a logical decision function F and a weighting function W to generate an associated state flag r. i and initial weight w i The mapping matrix M, where the association status flags include strong association, configurable association, and invalid association; Among them, the logical decision function F is implemented based on a configurable business rule base, and the weight function W is based on historical data fitting, expert experience assignment, or default value setting. The logical judgments include: A strong correlation indicates that the cost driver is a necessary factor driving the standard operating procedure. If a relationship is determined to be configurable, it means that the relationship requires user confirmation or configuration of specific coefficients. If deemed invalid, it means that the cost driver has no business relevance to the standard operation and is therefore excluded.

[0035] In this embodiment, the Cartesian product operation is performed as follows: First, perform J×D (Cartesian product) to generate the set M0 of all possible job-motivation ordered pairs: M0=J×D={(j i ,d j )| j i ∈J, d j ∈D}; In theory, M0 contains m×n elements, achieving full and complete coverage of all possible associations.

[0036] Logical judgment and weight assignment: For each association pair (j) in M0 i ,d jThe data is processed through a logical decision function F and a weight function W to form the final mapping matrix M, which has business significance.

[0037] The final mapping matrix M can be formally defined as: M={(j i ,d j ,w i j,r i j)|(j i ,d j )∈M0,w i j=W(j i ,d j ),r i j=F(j i ,d j )}; Among them, w i j (Initial Association Weight): A real number. The initial value can be automatically assigned by function W based on the rule base of the task type and driver attributes. For example, the initial weight of cleaning task with the area driver is 0.8, and the initial weight with the number of households driver may be 0.1. r i j (Association Status Flag): The value output by the logical decision function F, used to describe the business rationality of the association pair, and is usually defined as: r=1: Strong association, the system rule base determines that it is a necessary association (such as cleaning and area), and the user cannot veto it; r=0: Configurable association, the system prompts the association possibility and waits for user confirmation or configuration of the coefficient; r=-1: Invalid association. The system rule base determines that it has no business significance (such as the frequency of maintenance and garbage collection) and automatically excludes it, or it can be rejected by the user.

[0038] For example: the task set {cleaning, patrolling, maintenance} × the driver set {area, frequency, number of households} = generates 9 possible association pairs. The system will set an initial weight or logical judgment rule for each association pair.

[0039] Implementation details include: Initialization and Update: This algorithm is automatically triggered when there are increases or decreases in standard operations or cost drivers to update the mapping matrix and ensure dynamic integrity.

[0040] The implementation of the logical decision function F is based on a configurable business rule base. The rule can be: "IFjob_type='cleaning'ANDdriver_unit='square meters'THENr:= 1".

[0041] Implementation of the weight function W: It can be based on historical data fitting, expert experience assignment, or set to a default value (such as 0.5). The weights wi j will be used directly as the initial value for the coefficient field in the job_driver_mapping table.

[0042] User interface: A matrix-style management interface needs to be provided, allowing users to manage r i For associations with j=0, perform batch confirmation, configure specific coefficients, or reject them.

[0043] The technical effects of the above solution are as follows: By systematically performing Cartesian product operations, a theoretically complete set of all possible relationships between operations and drivers is constructed, ensuring the comprehensiveness and objectivity of cost driver identification from the source. Based on this, logical judgment is performed through a configurable business rule base, which can flexibly and accurately identify strong relationships, configurable relationships, and invalid relationships, realizing an organic combination of automated screening and manual intervention. This improves efficiency and ensures business adaptability. Combined with weight assignment based on historical data, expert experience, and other multi-source information, a scientific and interpretable quantitative foundation is laid for subsequent cost allocation. The final generated mapping matrix with relationship status and weights is not only clearly structured and logically rigorous, but also provides a stable and iterative initialization framework for subsequent dynamic optimization and accurate cost calculation, significantly enhancing the controllability and reliability of the entire cost calculation model.

[0044] In step four, selecting the corresponding cost calculation formula based on the type of activity includes: For internal operations, the following formula is used: Activity cost = ∑(actual value of each cost driver associated with the activity × dynamic allocation unit price of the cost driver × weight of the cost driver); For outsourced work, the following formula is used: Operating cost = Contractual unit price × Contractual quantity; In step four, the machine learning algorithm is gradient descent, and the weight update formula is w. it＋1 =w it -η·( L / w i )=w it -2η·( t -y t )·x it , where w i Let η be the weight of cost driver i at period t, η be the learning rate, and L be the loss function constructed based on the deviation between predicted cost and actual cost. Step four also includes anomaly data handling steps, specifically including: Before cost calculation, perform anomaly detection on the actual values ​​of the drivers, including threshold detection, statistical detection, and correlation detection; Outliers are automatically replaced or truncated during the calculation process; After the calculation is completed, the output results are validated for reasonableness, including validation of individual item proportions and validation of unit cost range.

[0045] In this embodiment, the total resource cost (such as the total wages of the cleaning department) is allocated to each operation based on the mapping matrix established in step two.

[0046] The cost of a certain task = ∑(actual quantity of each driver corresponding to the task × allocation unit price of the driver).

[0047] For example: Lobby cleaning operation cost = Lobby cleaning area × Cleaning cost per unit area + Daily lobby cleaning frequency × Cost per cleaning session. The allocated unit price is initialized based on historical data or standard quotas and continuously optimized through machine learning.

[0048] In this embodiment, the initial setting of the weights is as follows: In the job_driver_mapping table, the coefficient field is initially set as a statically assigned coefficient, while the newly added dynamic_weight field represents the dynamic weight. Its initial value is set based on: Historical data regression: Based on data from multiple historical projects, linear regression is used to fit the relationship coefficients between operating costs and the actual values ​​of each driver.

[0049] Expert experience assignment: Using the Delphi method, multiple cost experts scored the importance of the "activity-driver" relationship (0-1), and the average was taken.

[0050] Rule derivation: For example, if the std_man_hour of a job is linearly driven by a certain factor, then the initial weight of that factor is set to 1.0.

[0051] In this embodiment, dynamic weight optimization (machine learning mechanism) establishes an online learning feedback loop to continuously optimize the weights. Its technical framework is as follows: Figure 3 As shown.

[0052] Specific optimization algorithm (taking gradient descent as an example): Suppose a certain task has k drivers, and the period t is 1 t Forecast costs: t = w1t·x1t+ w2t·x2t + … + w k t·x k t; Actual cost: yt ; Loss function: L t =( t -y t )²; Weight update: w it＋1 =w it -η·( L / w i )=w it -2η·( t -y t )·x it ; Where η is the learning rate, which is set to 0.01 by default and can be configured.

[0053] The system runs this optimization process weekly or monthly to ensure that the weights adapt to changes in business requirements.

[0054] In this embodiment, an anomaly data handling mechanism is implemented, with three anomaly handling checkpoints before and after the calculation: 1. Input value anomaly detection: Threshold detection: A warning is triggered when the actual value of the driver exceeds 150% of the historical maximum or falls below 50%.

[0055] Statistical detection: Using the Z-Score method, data points with |Z|>3 are marked as extreme values.

[0056] Correlation detection: If "patrol frequency" and "number of households" have a strong correlation in historical data (R²>0.8), a check is triggered when new data deviates significantly from this relationship.

[0057] 2. Handling of anomalies during calculation: An automated anomaly handling mechanism is integrated into the cost calculation process. When an anomaly is detected in the actual value of the driver, a backup data cleaning strategy, as shown in the SQL logic, will be automatically triggered to ensure the stability of the calculation process and the reliability of the results. Specifically: The system performs real-time validation of the current input value: if the value is empty, it is automatically filled with the historical average of the driver to ensure data integrity; if the current value exceeds three standard deviations of historical data and is judged as an extreme outlier, the system will perform upper limit truncation, i.e., take the smaller value between the current value and "historical average plus two standard deviations", thereby effectively smoothing abnormal fluctuations and preventing individual extreme values ​​from having an unreasonable impact on the overall cost allocation; the clean driver value after this processing will be directly used for subsequent cost calculations. This mechanism, as an embedded guarantee module in the core calculation process, realizes the instant diagnosis and autonomous repair of data quality problems, significantly improving the system's robustness in dealing with complex and imperfect data environments.

[0058] 3. Output result validity check: After the calculation is completed, the system verifies: Individual percentage: The cost of any single operation should not exceed 60% of the total cost of its respective resource pool (such as the cleaning department).

[0059] Unit cost range: For example, the monthly unit cost of residential cleaning should be between 0.8 and 1.5 yuan per square meter.

[0060] If the data exceeds the specified range, the report will highlight the error and suggest reviewing the input data or initiating weight optimization.

[0061] The following is a complete calculation example for step four: Scenario: Calculate the operating cost of "Daily cleaning of office area" for "Science Park A Project" in May 2024.

[0062] Known data: Resource pool: The total cost of the cleaning department in May (wages, materials, tools) was 80,000 yuan.

[0063] Job motivation mapping (optimized dynamic weights): Historical learning rate: η = 0.02.

[0064] Based on the above, the calculation steps are as follows: Step 1: Calculate the total unit price of driver allocation First, allocate the total cost of the resource pool to each driver according to their weight: Cost allocated based on area: 80,000 yuan × 0.7 = 56,000 yuan; Frequency driver cost allocation: 80,000 yuan × 0.3 = 24,000 yuan; Then, calculate the actual allocation unit price for each driver: Unit price per square meter of area = 56,000 yuan / 20,000 square meters = 2.8 yuan / square meter; Frequency allocation unit price = 24,000 yuan / (22 days × 2 times / day) = 545.45 yuan / time (assuming 2 times per day); Step 2: Calculate the cost of this activity. Activity cost = ∑(actual value of driver × unit price of driver allocation) =(20,000 square meters × 2.8 yuan / square meter) +(22 days × 545.45 yuan / time × 2 times / day) = 56,000 yuan + 24,000 yuan =80,000 yuan Step 3: Weight Optimization Feedback Assuming the actual cost of this operation in May was 78,500 yuan (from financial data), then the forecast deviation Δ = 80,000 - 78,500 = 1,500 yuan.

[0065] System startup weight optimization: Loss L = (1,500)² = 2,250,000; Gradient of area weight w1: L / w1= 2×1,500×20,000=60,000,000 Update: w 1＋ = 0.7 - 0.02 × (60,000,000 / 10¹) 0 )≈0.688 After normalization, the new weights are: w1=0.688, w2=0.312; The updated weights will be used for cost forecasting in June, enabling continuous self-optimization.

[0066] The technical effects of the above solution are as follows: By clearly distinguishing between internal and outsourced operations and adopting differentiated cost calculation formulas that are tailored to the specific business needs, the accuracy and practicality of cost estimation are ensured. It can accurately reflect the complex cost structure driven by multiple factors in internal operations while directly connecting to the fixed pricing model of outsourced contracts, thus enhancing the comprehensiveness and applicability of the model. The introduction of a machine learning dynamic optimization mechanism based on gradient descent allows the weight coefficients to be automatically and iteratively corrected according to actual cost deviations, effectively reducing the subjectivity and static limitations of manually setting weights. This enables the cost allocation model to have continuous learning and self-optimization capabilities, significantly improving the accuracy and adaptability of long-term calculations. Furthermore, the multi-level abnormal data processing and result verification steps throughout the pre-, during, and post-calculation stages constitute a complete quality control closed loop, effectively identifying and handling data anomalies and ensuring the reliability of input data and the rationality of output results.

[0067] In step five, the standard cost quota library is extracted from cost data of multiple projects, including standard cost quotas under different business formats and service levels, supporting rapid cost calculation for new projects.

[0068] In this embodiment, through repeated calculations and verifications in step four, a standard cost quota library for different business formats and service levels will be formed (e.g., under the first-level service standard, the monthly cleaning cost per square meter is X yuan). This quota library is the "solidified" result.

[0069] When it is necessary to calculate the cost of a new residential community, users only need to input the core parameters of the community (such as total area, number of households, and expected service level) to automatically call up the fixed cost quota library and generate a complete community operation cost calculation report instantly through the preset algorithm model.

[0070] The technical effects of the above solution are as follows: By refining and solidifying the accurate cost data accumulated during historical calculation and verification into a standard cost quota library covering different business formats and service levels, the transformation of activity cost knowledge from dynamic calculation to stable standards is effectively realized, forming a reusable core asset. When faced with the cost calculation needs of new projects, users only need to input a few key parameters such as area and service level, and can instantly match and calculate based on this quota library through a pre-set model. This completely changes the traditional time-consuming and cumbersome model that relies on manual experience to estimate one by one, greatly improving the efficiency, consistency and standardization of cost forecasting. This not only provides a powerful data engine and decision support for property management companies to quickly bid, accurately budget and scale up, but also ensures the comparability and management standardization of cost benchmarks between different projects, thereby significantly enhancing the company's market response speed and core competitiveness.

[0071] In summary, this method achieves precise linkage between costs and operations, enabling clear calculation of the true cost of each property service and providing a basis for differentiated pricing and profit and loss analysis. This method reduces the manual calculation work that previously required several days to minutes of automated completion, significantly improving the efficiency of property management companies' market expansion and budget preparation. The standard operating procedure library and cost quota library formed by this method ensure that cost calculations across different communities are based on unified standards and have strong replicability, facilitating group-level management. The model in this method can automatically update the calculation results according to changes in basic data (such as prices and personnel costs), possessing dynamic predictive capabilities and supporting "What-If" analysis. This method IT-izes and normalizes management accounting concepts, shifting corporate decision-making from "experience-based" to "data-based."

[0072] Specifically, such as Figure 4As shown, this embodiment also proposes a property operation cost calculation system based on Cartesian product, used to implement a property operation cost calculation method based on Cartesian product, including: The data acquisition layer is used to build and maintain the standard operating procedure library and cost driver library, and to collect basic data of the project to be measured. The model calculation layer includes a model building engine, a Cartesian product mapping engine, a dynamic cost calculation engine, and a quota fixing engine; Model building engine: used to standardize operational and motivational data; Corresponding to step one, the collected raw data is standardized into two core objects: "task" and "motivation"; Cartesian product mapping engine: used to perform Cartesian product operations on standard tasks and cost drivers, and generate task-driver association mapping matrix through logical judgment and weight initialization by the rule engine; Corresponding to step two, the full combination operation is automatically performed to generate a full correlation matrix of job-motivation, and intelligent filtering is performed through the rule engine; Dynamic cost calculation engine: used to calculate the cost of each standard operation based on the aforementioned correlation mapping matrix and basic data; Corresponding to steps three / four, the internal allocation or outsourcing pricing formula is automatically selected based on the cost type for accurate rolling calculation; Standard cost quota engine: used to generate and optimize the standard cost quota library; Corresponding to step five, the verified project cost data is mined, averaged, and attributed to extract reusable standard cost quotas. The results presentation layer is used to generate and display cost calculation reports, manage the standard cost quota library, and provide multi-dimensional visualization analysis. Specifically: Calculation report generation: Automatically generates structured professional calculation reports, supporting one-click export; Quota library management: Provides functions for querying, comparing versions, and managing the release of fixed quotas; Multidimensional analysis dashboard: Provides visual analysis of cost structure, fluctuation trends, sensitivity simulation, etc., to support management decision-making; The Cartesian product mapping engine further includes a user interface for displaying job-driver association pairs that are determined to be configurable associations to the user, and receiving confirmation, weight adjustment, or rejection operations from the user for the association pair. The dynamic cost calculation engine has a built-in unified calculation framework, a machine learning optimization module, and an anomaly handling module. The machine learning optimization module is used to dynamically optimize weights periodically based on actual cost data, and the anomaly handling module is used to detect, correct, and verify data before, during, and after calculation.

[0073] The technical effects of the above solution are as follows: The data acquisition layer effectively ensures the unified maintenance and high-quality acquisition of standard operating procedures, driver databases, and basic project data, providing a solid foundation for accurate calculation. The model calculation layer, as the core, has four built-in engines that realize a complete closed loop from model building, full Cartesian product mapping, dynamic cost calculation to quota solidification. The model building engine ensures data standardization, the Cartesian product mapping engine constructs a comprehensive and flexibly configurable association matrix through full combination operations and rule judgment, combined with the user interface. The dynamic cost calculation engine, with its unified calculation framework, automatically selects and executes accurate cost allocation or outsourcing pricing, while continuously optimizing weights through the machine learning module and ensuring the robustness and reliability of the calculation process through the exception handling module. The quota solidification engine extracts verified cost data into a reusable standard quota database. Finally, the results display layer transforms complex calculation results into intuitive and operable decision support information by automatically generating professional reports, providing quota database management, and multi-dimensional visualization analysis dashboards, thereby comprehensively improving the efficiency, accuracy, and intelligence level of property management companies' cost management.

[0074] Working Principle: By constructing a standard operation library and a cost driver library, and establishing a Cartesian product mapping matrix between them, this method systematically covers all possible associations between operations and cost drivers. Then, through logical judgment and weight assignment, it filters out valid association pairs, thereby constructing a comprehensive and configurable cost allocation model. In the cost calculation stage, this method selects different formulas based on the operation type (internal or outsourced) and combines actual driver data, dynamic unit prices, and weights for accurate calculation. Simultaneously, machine learning algorithms are introduced to dynamically optimize the weights, enabling the model to have self-learning and adaptability. Furthermore, by integrating abnormal data processing and rationality verification mechanisms, data quality and result reliability are ensured. Finally, the calculation results are solidified into a standard cost quota library, supporting the rapid generation of cost calculation reports based on input parameters. This invention, through structured, automated, and intelligent means, solves the problems of one-sided association relationships, reliance on experience, insufficient accuracy, and poor adaptability in traditional property cost calculation. It achieves comprehensive coverage, accurate quantification, and dynamic optimization of the calculation process, improving the standardization, refinement, and intelligence level of cost management.

[0075] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0076] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for calculating property operation costs based on the Cartesian product, characterized in that, Includes the following steps: Step 1: Construct a standard operation library and a cost driver library. The standard operation library includes multiple standard operation activities, each of which is associated with standard working hours and material quotas. The cost driver library includes multiple cost drivers, each of which is associated with a unit of measurement. Step 2: Establish a Cartesian product mapping matrix of tasks and drivers. By performing a Cartesian product operation on the standard task set and the cost driver set, a full task-driver association probability matrix is ​​generated, and logical judgment and weight assignment are performed on each association pair. Step 3: Collect and clean basic data, including community master data, actual values ​​of driving factors, and resource cost data; Step 4: Calculate cost allocation based on the Cartesian product mapping matrix. Select the corresponding cost calculation formula according to the job type, combine the actual value of the driver, the dynamically allocated unit price and the weight coefficient to calculate the cost of each job, and use machine learning algorithms to dynamically optimize the weights in the job-driver association mapping matrix based on the actual cost data. Step 5: Cost solidification and calculation output. Based on the calculation results, a standard cost quota library is formed, and a cost calculation report is automatically generated according to the input community parameters.

2. The method for calculating property operation costs based on the Cartesian product according to claim 1, characterized in that, Step two involves establishing the Cartesian product mapping matrix, which includes: Let the standard job set be J = {j1,j2,j3,...,j...} m Let m be the total number of tasks, and let the set of cost drivers be D = {d1, d2, d3, ..., d}. n }, where n is the total number of motivations; Perform a Cartesian product operation on the standard operation set J and the cost driver set D to generate an ordered set M0 of all possible operation-cost driver pairs, where M0 = J × D = {(j i ,d j )| j i ∈J, d j ∈D}; For each association pair (j) in M0 i ,d j The process involves using a logical decision function F and a weighting function W to generate an associated state flag r. i and initial weight w i The mapping matrix M is given by the association status flags, which include strong association, configurable association, and invalid association.

3. The method for calculating property operation costs based on the Cartesian product according to claim 2, characterized in that, The logical decision function F is implemented based on a configurable business rule base, and the weight function W is based on historical data fitting, expert experience assignment, or default value setting.

4. The method for calculating property operation costs based on the Cartesian product according to claim 1, characterized in that, In step two, the logical judgment includes: A strong correlation indicates that the cost driver is a necessary factor driving the standard operating procedure. If a relationship is determined to be configurable, it means that the relationship requires user confirmation or configuration of specific coefficients. If deemed invalid, it means that the cost driver has no business relevance to the standard operation and is therefore excluded.

5. The method for calculating property operation costs based on the Cartesian product according to claim 1, characterized in that, In step four, selecting the corresponding cost calculation formula based on the type of activity includes: For internal operations, the following formula is used: Activity cost = ∑(actual value of each cost driver associated with the activity × dynamic allocation unit price of the cost driver × weight of the cost driver); For outsourced work, the following formula is used: Operating cost = Contractual unit price × Contractual quantity.

6. The method for calculating property operation costs based on the Cartesian product according to claim 1, characterized in that, In step four, the machine learning algorithm is gradient descent, and the weight update formula is w. it＋1 =w it -η·( L / w i )=w it -2η·( t -y t )·x it , where w i Let η be the weight of cost driver i at period t, η be the learning rate, and L be the loss function constructed based on the deviation between predicted cost and actual cost.

7. The method for calculating property operation costs based on the Cartesian product according to claim 1, characterized in that, Step four also includes anomaly data handling steps, specifically including: Before cost calculation, perform anomaly detection on the actual values ​​of the drivers, including threshold detection, statistical detection, and correlation detection; Outliers are automatically replaced or truncated during the calculation process; After the calculation is completed, the output results are validated for reasonableness, including validation of individual item proportions and validation of unit cost range.

8. The method for calculating property operation costs based on the Cartesian product according to claim 1, characterized in that, In step five, the standard cost quota library is extracted from cost data of multiple projects, including standard cost quotas under different business formats and service levels, supporting rapid cost calculation for new projects.

9. A property operation cost calculation system based on Cartesian product, used to implement the property operation cost calculation method based on Cartesian product as described in any one of claims 1-8, characterized in that, include: The data acquisition layer is used to build and maintain the standard operating procedure library and cost driver library, and to collect basic data of the project to be measured. The model calculation layer includes a model building engine, a Cartesian product mapping engine, a dynamic cost calculation engine, and a quota fixing engine; A model building engine for standardizing operational and motivational data; The Cartesian product mapping engine is used to perform Cartesian product operations on standard operations and cost drivers, and generates an operation-driver association mapping matrix through logical judgment and weight initialization by the rule engine. A dynamic cost calculation engine is used to calculate the cost of each standard operation based on the association mapping matrix and the basic data; The quota solidification engine is used to generate and optimize the standard cost quota library; The results presentation layer is used to generate and display cost calculation reports, manage the standard cost quota library, and provide multi-dimensional visualization analysis.

10. A property operation cost calculation system based on Cartesian product according to claim 9, characterized in that, The Cartesian product mapping engine further includes a user interface for displaying job-driver association pairs that are determined to be configurable associations to the user, and receiving confirmation, weight adjustment, or rejection operations from the user for the association pair. The dynamic cost calculation engine has a built-in unified calculation framework, a machine learning optimization module, and an anomaly handling module. The machine learning optimization module is used to dynamically optimize weights periodically based on actual cost data, and the anomaly handling module is used to detect, correct, and verify data before, during, and after calculation.

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