Apartment operation and maintenance data management system and method based on Internet of Things

Abstract

The invention belongs to the technical field of apartment operation and maintenance, and particularly relates to an apartment operation and maintenance data management system and method based on the Internet of Things. According to the invention, through real-time monitoring of energy and equipment states, visualized large-screen display of the overall operation condition of the apartment is realized, fault discovery and response time is shortened, operation and maintenance efficiency is improved, the apportionment amount is automatically adjusted based on actual energy consumption and reference, market and seasonal changes can be reflected, and operation and maintenance efficiency is improved. Meanwhile, public energy consumption is finely allocated, automatic detection of unpublic allocation, instantaneous energy abnormality and equipment fault signals and priority work order generation are completely eradicated, the manual monitoring burden is greatly reduced, potential risks are handled in the first time, the maintenance cost and potential safety hazards are reduced, and the economic benefit is improved. Based on the comprehensive analysis of the payment weight, the historical operation and maintenance data and the current house resource state, accurate lease renewal, lease expansion or preferential strategy suggestions are provided for managers, and revenue maximization and lease rate improvement are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of apartment operation and maintenance, and in particular relates to an apartment operation and maintenance data management system and method based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization and the advancement of the "smart city" concept, new housing models such as centralized long-term rental apartments and shared apartments are becoming increasingly popular. Intelligent apartment management has become a key component for property operators to improve management efficiency and service quality. Especially in multi-tenant environments, tiered energy consumption uses a fixed proportion of energy consumption, which cannot accurately match tenants' actual usage. This leads to questions about the fairness of "pay more for less" or "pay less for more." Furthermore, energy metering, equipment maintenance, contract management, and rent settlement involve multiple parties, resulting in complex data and frequent updates. Traditional manual processing models cannot meet the needs of high-frequency, real-time, and refined management.

[0003] At present, some apartment management systems have initially introduced Internet of Things technology, realizing the remote collection of energy usage data such as water, electricity, and gas, as well as the status monitoring of some smart devices. However, most of these systems are functionally separated and lack the ability to integrate and process data and make decisions. Especially for collective apartments or shared apartments, although each resident is equipped with an Internet of Things device, they will be treated as a whole to divide energy consumption into tiered levels when calculating energy. However, for some users, their own energy consumption accounts for a very small proportion of the overall energy consumption. Therefore, it is actually unfair to use a common area to distribute it. In other words, in the current apartment management process, in key links such as rent adjustment, energy sharing, operation and maintenance scheduling, and risk prevention and control, there are still cases of untimely response and frequent accounting disputes, which makes it difficult to implement. The current large-scale operation, and for the apartment management, when the user's energy usage is abnormal, since the user is responsible for the energy usage fee, generally do not pay too much attention, but there are many reasons for energy anomalies, such as abnormalities in the energy equipment in the apartment, such as water pipe leakage, which will cause a sudden increase in water consumption, and serious aging of electrical equipment will greatly increase electricity consumption. For the lessee, its actual housing cost increases, but this part of the problem is actually not caused by the user, but by the maintenance of the apartment management. Therefore, it is unfair to the user, and may even cause the user to question the service of the apartment management, which in turn leads to an increase in the rent termination rate. Summary of the Invention

[0004] The purpose of the present invention is to provide an apartment operation and maintenance data management system and method based on the Internet of Things, which can achieve unified management and intelligent processing of energy usage, tenant behavior, contract data and operation and maintenance events.

[0005] The technical solutions adopted by the present invention are as follows: An apartment operation and maintenance data management method based on the Internet of Things, comprising: Collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the device operating status. Combined with energy usage data and corresponding device operating status, determine energy consumption and usage, including normal and abnormal energy consumption. Dynamically link tenant contracts with energy usage data, determine allocation weights based on energy usage data, and dynamically adjust allocation amounts based on usage; When abnormal energy consumption or equipment failure signals appear in the equipment status data, a priority maintenance work order is automatically generated and assigned to the operation and maintenance personnel to confirm the abnormal factors, which include equipment aging, equipment damage, and human damage; Generate a comprehensive rent bill based on the contract period and the adjusted amortized amount, and calculate the payment weight value through the payment interface; Combining the components of the operation and maintenance work order, the property status, the payment weight value and the contract information, the leasing strategy and leasing recommendations are output, and the tenant contract information is encrypted using a dynamic encryption strategy based on the contract cycle.

[0006] In a preferred embodiment, energy usage data and device status data of IoT devices in an apartment are collected, wherein the energy usage data includes electricity meter readings, water meter readings, and gas meter readings, and the device status data includes the number of items in the room and the operating status of the devices. The energy consumption and usage are determined based on the energy usage data and the corresponding device operating status, and the usage includes normal energy consumption and abnormal energy consumption. The steps include: Collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the operating status of the devices. Obtaining corresponding multiple energy consumption values ​​according to energy usage data; Get the corresponding device status value according to the device operation status; Obtaining a usage status value based on multiple energy consumption values ​​and device status values; Obtaining a usage status threshold and determining whether the usage status value exceeds the usage status threshold; If the usage status value does not exceed the usage status threshold, the energy consumption is determined to be normal; If the usage status value exceeds the usage status threshold, the energy consumption is determined to be abnormal energy consumption.

[0007] In a preferred embodiment, the steps of dynamically associating tenant contracts with energy usage data, determining allocation weights based on the energy usage data, and dynamically adjusting allocation amounts based on usage include: Obtain tenant contract information, including contract period, rent amount, payment method, and tenant identity information; Dynamically associate and store energy usage data with tenant identity information; Get the current season information and obtain the corresponding season weight based on the season information; Obtaining a plurality of corresponding energy usage values ​​according to the energy usage data, and obtaining a corresponding energy usage reference based on the plurality of energy usage values ​​and seasonal weights; Obtaining an allocation table, wherein the allocation table includes a plurality of energy usage reference intervals and an allocation weight corresponding to each energy usage reference interval; Obtain the corresponding allocation weight from the allocation table according to the energy usage reference interval corresponding to the energy usage reference; Adjust the allocation amount based on allocation weight and usage.

[0008] In a preferred embodiment, the step of adjusting the apportionment amount based on the apportionment weight and usage includes: Obtain the total cost of energy use; Obtain the total energy usage value and tenant energy value of each tenant based on the energy usage data; Obtain the corresponding energy consumption weight according to usage; The apportionment amount is obtained based on the total energy usage value, the tenant energy value of each tenant, the apportionment weight, the energy consumption weight, and the total energy usage cost.

[0009] In a preferred solution, when abnormal energy consumption or equipment status data indicates an equipment failure, a priority maintenance work order is automatically generated and assigned to an operation and maintenance person to identify the abnormal factors, which include equipment aging, equipment damage, and human damage. The steps include: When abnormal energy consumption or equipment status data is obtained and equipment failure signals appear, the corresponding energy instantaneous abnormal fluctuations and equipment failure signals are obtained based on the energy usage data and equipment status data; Obtain abnormal factors of transient energy fluctuations and equipment failure signals, including equipment aging, equipment damage, and human damage; Obtain the corresponding abnormal weight according to the abnormal factors; Obtain corresponding energy instantaneous abnormal value and equipment failure value according to energy instantaneous abnormal fluctuation and equipment failure signal respectively; Obtain the corresponding abnormal value based on the energy instantaneous abnormal value, equipment failure value and abnormal weight; Obtain a work order priority table, where the work order priority table includes multiple abnormal value intervals and priority operation and maintenance work orders corresponding to each abnormal value interval; Obtain the corresponding priority operation and maintenance work order from the work order priority table based on the abnormal value interval corresponding to the abnormal value; Assign operation and maintenance personnel based on the contract cycle and priority of the operation and maintenance work orders.

[0010] In a preferred solution, the steps of assigning operation and maintenance personnel based on the contract period and priority of the operation and maintenance work orders include: Obtaining a work list of an operation and maintenance processing personnel, wherein the work list includes a schedule for the operation and maintenance processing personnel to process work orders; Obtain the remaining contract period based on the contract period, and obtain the preliminarily screened operation and maintenance personnel based on the remaining contract period and the worksheet; An operation and maintenance personnel scoring table is established based on the initially screened operation and maintenance personnel, wherein the operation and maintenance personnel table includes the work order comprehensive value range corresponding to each operation and maintenance personnel; Obtain the payment weight and energy usage reference of the corresponding tenant based on the priority operation and maintenance work order, and obtain the initial value of the work order based on the priority operation and maintenance work order; Obtain the comprehensive value of the work order based on the work order initial value, payment weight value and energy usage reference; According to the work order comprehensive value interval corresponding to the work order comprehensive value, the corresponding operation and maintenance processing personnel are obtained from the operation and maintenance personnel score table.

[0011] In a preferred embodiment, the steps of generating a comprehensive rent bill based on the contract period and the adjusted apportionment amount, and calculating the payment weight value through the payment interface include: Generate a comprehensive rent bill based on the contract period and adjusted amortization amount; Trigger automated deductions and overdue warnings based on the rental comprehensive bill through the preset payment interface; Obtain automated deduction data and overdue data, and obtain the corresponding automated deduction success rate based on the automated deduction data, and obtain the corresponding overdue duration, overdue amount, and overdue bill type based on the overdue data; Obtain the corresponding overdue bill weight value according to the overdue bill type; Obtain the payment weight value based on the automatic deduction success rate, overdue duration, overdue amount, and overdue bill weight value.

[0012] In a preferred solution, the steps of outputting a leasing strategy and leasing recommendations based on the components of the operation and maintenance work order, the property status, the payment weight value, and the contract information, and encrypting the tenant contract information using a dynamic encryption strategy based on the contract period include: Obtain the abnormal factors and property status of the priority operation and maintenance work order. If the abnormal factors are human-caused, obtain the corresponding human factor weight based on the abnormal factors, and obtain the corresponding property residual weight based on the property status. Obtain a period weight based on the contract period of the tenant contract information, and obtain a tenant violation score based on the contract terms of the tenant contract information; Obtain tenant rental scores based on human factor weights, remaining housing weights, cycle weights, and tenant violation scores; Obtain a lease table, where the lease table includes multiple tenant lease score intervals and the lease policy corresponding to each tenant lease score interval; Obtain the corresponding leasing strategy from the leasing table based on the tenant leasing score interval corresponding to the tenant leasing score; Provide leasing advice based on leasing strategies; Tenant contract information is encrypted using a dynamic encryption strategy based on the contract cycle.

[0013] In a preferred embodiment, the steps of encrypting the tenant contract information using a dynamic encryption strategy based on the contract period include: Divide the encryption phases into stages based on the contract lifecycle, and define encryption policies for the contract validity period, renewal period, and termination period. The first encryption policy is used during the contract validity period, the second encryption policy is used during the contract renewal period, and the third encryption policy is activated during the contract termination period, triggering historical data migration. During the effective period of the contract, the tenant's identity information and contract terms are encrypted using the first encryption strategy to obtain the contract identifier of the tenant's contract information, and an initial key is generated based on the contract identifier; During the contract renewal period, the rental amount and payment method are encrypted twice using a second encryption strategy, and the encryption key is dynamically updated based on the remaining duration of the contract period; When the contract period reaches its end, the sensitive data in the tenant contract information will be migrated to an independent storage partition, and the migrated data will be reinforced using the third encryption strategy; During decryption access, the corresponding decryption algorithm is matched according to the current contract stage, and the decryption authorization is completed through a two-factor authentication mechanism of the tenant's identity biometrics and dynamic verification code.

[0014] An apartment operation and maintenance data management system based on the Internet of Things, used in the above-mentioned apartment operation and maintenance data management method based on the Internet of Things, comprising: The device management module is used to collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the operating status of the devices. Energy consumption is determined by combining energy usage data and corresponding device operating status. Usage includes normal energy consumption and abnormal energy consumption. The tenant module is used to dynamically associate tenant contracts with energy usage data, determine the allocation weight based on energy usage data, and dynamically adjust the allocation amount based on usage; The operation and maintenance work order module is used to automatically generate priority maintenance work orders when abnormal energy consumption or equipment status data indicates equipment failure, and assign the operation and maintenance work orders to operation and maintenance personnel to identify abnormal factors, such as equipment aging, equipment damage, and human damage; The payment weight module is used to generate a comprehensive rent bill based on the contract period and the adjusted amortization amount, and calculate the payment weight value through the payment interface; The leasing strategy module is used to combine the components of the operation and maintenance work order, the property status, the payment weight value and the contract information to output the leasing strategy and leasing suggestions, and encrypt the tenant contract information using a dynamic encryption strategy based on the contract cycle.

[0015] The technical effects achieved by the present invention are: The present invention realizes a large-screen visualization of the overall operating status of the apartment through real-time monitoring of energy and equipment status, shortens fault discovery and response time, improves operation and maintenance efficiency, and automatically adjusts the apportionment amount based on actual energy consumption and reference, which can not only reflect market and seasonal changes, but also ensure the fairness of tenants paying for what they use. At the same time, public energy consumption is finely apportioned to eliminate unfair apportionment. The automatic detection of energy transient anomalies and equipment failure signals and the generation of priority work orders greatly reduce the burden of manual monitoring, so that potential risks can be dealt with in the first place, reducing maintenance costs and safety hazards. Based on a comprehensive analysis of payment weights, historical operation and maintenance data and current housing status, managers are provided with accurate renewal, rent increase or preferential strategy suggestions to maximize profits and increase occupancy rate. The dynamic encryption strategy ensures the security of contracts and tenants' privacy information, complies with information security and data protection regulations, enhances tenant trust, and reduces legal risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the method provided by the present invention; Figure 2 This is a system module diagram provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0019] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive of other embodiments.

[0020] Secondly, the present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention in detail, for the sake of convenience, the schematic diagrams are only examples and should not limit the scope of protection of the present invention.

[0021] Please see the attached Figure 1 As shown, a method for apartment operation and maintenance data management based on the Internet of Things is provided, including: S1. Collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and device operating status. Combine the energy usage data and the corresponding device operating status to determine energy consumption, including normal and abnormal energy consumption. S2. Dynamically link tenant contracts with energy usage data, determine allocation weights based on energy usage data, and dynamically adjust allocation amounts based on usage; S3: When abnormal energy consumption occurs or equipment status data indicates equipment failure, a priority maintenance work order is automatically generated and assigned to the operation and maintenance personnel to identify the abnormal factors, which include equipment aging, equipment damage, and human damage. S4. Generate a comprehensive rent bill based on the contract period and the adjusted apportionment amount, and calculate the payment weight value through the payment interface; S5. Combine the components of the operation and maintenance work order, the property status, the payment weight value, and the contract information to output the leasing strategy and leasing suggestions, and encrypt the tenant contract information using a dynamic encryption strategy based on the contract cycle.

[0022] As in steps S1 to S5 above, electricity meters, water meters, gas meters, and smart sensors are installed in each tenant's room to collect real-time data on energy (electricity, water, and gas) usage and equipment operating status (such as air conditioners, refrigerators, door locks, etc.). Together with environmental information such as the number of items, these data are uploaded to the cloud. The raw data is cleaned (noise is filtered out and missing values ​​are filled) and formatted through the gateway to form a standardized data set that can be used for subsequent analysis. The platform accesses and manages the tenant contract data of each room, including contract terms, lease period, base rent, payment method, and tenant identity. Energy usage data is dynamically associated with contract information, and allocation weights are generated in real time. The allocation amount for the current period is automatically adjusted based on usage. A dynamic billing model based on "how much you use, how much you pay" is implemented. The operating status data is combined with the instantaneous energy consumption curve. Through threshold detection, mutation detection or machine learning anomaly detection models, abnormal energy fluctuations (such as sudden increases in electricity consumption) and equipment failure signals (such as equipment aging, equipment damage, and human damage) are identified. According to the contract period and the adjusted apportionment amount, a comprehensive bill is formed. After the comprehensive bill is processed by the payment interface (third-party payment gateway or platform), a payment weight value is generated to measure the tenant's credit or the impact on the subsequent operation and maintenance priority. For different levels of warnings, operation and maintenance work orders with priorities are automatically generated and intelligently allocated to appropriate maintenance personnel based on the contract period, rent payment status and payment weight. After the operation and maintenance work order is executed, personnel or outsourced teams can further obtain information such as human factors (such as misoperation, moving out / moving in) and current housing status (vacancy rate, maintenance time), and combine it with the tenant's payment record and contract information to output personalized leasing strategies to improve renewal rate and leasing efficiency. For sensitive information such as tenant contracts, dynamic key management and encryption solutions based on contract cycles (such as regularly rotating symmetric keys and attribute-based encryption) are adopted to ensure the confidentiality and integrity of data during storage and transmission, and support secure decryption and auditing when needed. Through real-time monitoring of energy and equipment status, a large-screen visual display of the overall operation status of the apartment is achieved, shortening fault detection and response time and improving operation efficiency. Maintenance efficiency, automatically adjusting the shared amount based on actual energy consumption and reference, can not only reflect market and seasonal changes, but also ensure the fairness of tenants paying for what they use. Automatic detection of energy transient anomalies and equipment failure signals and generation of priority work orders greatly reduce the burden of manual monitoring, so that potential risks can be dealt with in the first place, reducing maintenance costs and safety hazards. Based on a comprehensive analysis of payment weights, historical operation and maintenance data and current property status, it provides managers with accurate renewal, rent adjustment or preferential strategy suggestions to maximize profits and increase occupancy rate. The dynamic encryption strategy ensures the security of contracts and tenants' privacy information, complies with information security and data protection regulations, enhances tenant trust and reduces legal risks.

[0023] In a preferred embodiment, energy usage data and device status data of IoT devices in an apartment are collected, wherein the energy usage data includes electricity meter readings, water meter readings, and gas meter readings, and the device status data includes the number of items in the room and the operating status of the devices. The energy consumption and usage are determined based on the energy usage data and the corresponding device operating status, and the usage includes normal energy consumption and abnormal energy consumption, including the following steps: S101. Collect energy usage data and device status data of IoT devices in the apartment. The energy usage data includes electricity meter readings, water meter readings, and gas meter readings. The device status data includes the number of items in the room and the operating status of the devices. S102, obtaining corresponding multiple energy consumption values ​​according to the energy usage data; S103, obtaining a corresponding device status value according to the device operation status; S104, obtaining a usage status value based on multiple energy consumption values ​​and device status values; S105, obtaining a usage status threshold, and determining whether the usage status value exceeds the usage status threshold; If the usage status value does not exceed the usage status threshold, the energy consumption is determined to be normal; If the usage status value exceeds the usage status threshold, the energy consumption is determined to be abnormal energy consumption.

[0024] As in steps S101 to S105 above, smart electricity meters, water meters, gas meters, and environmental and equipment status sensors are installed in each room to continuously collect three types of energy usage data (electricity meter readings, water meter readings, and gas meter readings) and the number of items in the room, the operating status of the equipment (air conditioners / refrigerators, etc.), and the equipment status data. After each sensor is pre-processed (filtered, denoised, and padded) by the local gateway, the standardized data is uploaded to the cloud or edge time series database. The energy readings of multiple consecutive moments are read from the time series database, and the difference between adjacent readings or the cumulative reading change is calculated to obtain the energy consumption values ​​in multiple time periods (such as hourly electricity consumption and daily water consumption. Here, electricity consumption can be obtained through a smart plug-in board with electricity consumption statistics, while water consumption can be obtained through an IoT water meter). To obtain), the operating information of each device in the room at different times (such as switch status, working mode, and operating frequency) is mapped into a quantitative "device status value", which is obtained by matching according to the preset status mapping table. The status mapping table should at least include parameters such as device type, device status, and status value. For example: the air conditioner "cooling mode + high wind speed" is mapped to a status value of 2, and the standby mode is mapped to a status value of 0.5. The specific settings can be made according to the device nameplate and its actual usage in the past. I will not over-describe it here. Then, multiple energy consumption values ​​are associated with the device status values ​​of the corresponding time periods, and the total collection time of multiple energy consumption value data is collected. The usage status value of the device is calculated to reflect the rationality of energy consumption in different states. The calculation formula of the usage status value is: , where H represents the usage status value, j represents the number of energy consumption values ​​in multiple time periods, j=1,2,3…m, It is represented as the j-th energy consumption value, G is represented as the equipment status value, t is represented as the total collection time of multiple energy consumption value data, the usage status threshold range is pre-defined (can be set based on historical statistics or standard specifications), and the current usage status value is monitored in real time. If the usage status value exceeds the threshold, among which, when the current usage status value exceeds the threshold instantaneously, it may not be immediately identified, but the collection period interval may be shortened to further verify whether it is a sustainable abnormality. If the abnormality persists or there are multiple abnormalities within the interval, it can be identified as abnormal energy consumption, avoiding the direct identification of instantaneous abnormalities as abnormal energy consumption. In addition, when shortening the collection period interval, the collection can be stopped when the current state exceeds the threshold. The collection interval is greatly reduced to increase the collection frequency, which is used to eliminate instantaneous anomalies and confirm the anomalies in the shortest possible time. After confirming the anomaly, it triggers the subsequent operation and maintenance warning or work order generation. The dual combination of multi-period energy consumption and equipment status avoids misjudgment based on a single reading and improves the accuracy of energy consumption analysis. By comparing the status value with the threshold, abnormal energy consumption (such as forgetting to turn off the lights, abnormally high equipment consumption) can be immediately discovered, and it can be verified whether it is abnormal energy consumption. It guides operation and maintenance personnel or tenants to correct unreasonable operations in time, reduce waste, and reduce energy consumption costs. The abnormal identification results are linked with the operation and maintenance work order module to achieve a closed loop from monitoring, alarm, and disposal, which significantly improves the operation and maintenance efficiency of the apartment.

[0025] In a preferred embodiment, the steps of dynamically associating tenant contracts with energy usage data, determining allocation weights based on the energy usage data, and dynamically adjusting allocation amounts based on usage include: S201. Obtain tenant contract information, wherein the tenant contract information includes the contract period, rent amount, payment method, and tenant identity information; S202, dynamically associating and storing energy usage data with tenant identity information; S203, obtaining current season information, and obtaining corresponding season weights based on the season information; S204. Obtaining a plurality of corresponding energy usage values ​​according to the energy usage data, and obtaining a corresponding energy usage reference based on the plurality of energy usage values ​​and seasonal weights; S205: Obtain an allocation table, wherein the allocation table includes multiple energy usage reference intervals and an allocation weight corresponding to each energy usage reference interval; S206 , obtaining a corresponding allocation weight from an allocation table according to an energy usage reference interval corresponding to the energy usage reference; S207. Adjust the apportionment amount based on the apportionment weight and usage.

[0026] As in steps S201 to S207 above, the contract record of the specified room or tenant is read from the contract management database, and the contract period (start and end dates), basic rent amount, payment method (such as prepayment, monthly settlement, one deposit and three payments), and tenant identity information (name, contact information, check-in number, etc.) are extracted and loaded. Whenever new energy usage data (electricity meter, water meter, gas meter readings) is collected, the record is automatically associated with the corresponding contract through the tenant identity information (such as room number or tenant ID). After the association and merging, it is uniformly stored in the time series database to form a continuous energy usage sequence grouped by tenant dimension. The energy usage values ​​in multiple time periods in the recent period (configurable window period, such as the past 7 days, 30 days) are extracted from the time series database, and combined with the current seasonal information, the corresponding seasonal weight is extracted from the current seasonal information (for example, summer + high temperature, the corresponding seasonal weight is 1.5), and the energy usage reference for each type of energy is obtained. The calculation formula for the energy usage reference is: , where N represents the energy usage reference, i represents the number of energy usage values ​​in multiple time periods, i=2,3,4…n, Expressed as the energy usage value in the i-th time period, Expressed as the energy usage value in the i-1th time period, It is expressed as seasonal weight, and the reference tag is written into the database. The "allocation table" is queried. The table is pre-set based on the historical experience of recent years and the data corresponding to the current season. It can also be modified through manual revision. The table defines several energy reference intervals (such as electricity energy + summer, energy reference intervals include [0-10), [10-20), [20-30)) and corresponding allocation weights (such as 0.2, 0.4, 0.3). When the energy usage reference is 15 calculated under summer conditions, the allocation table corresponding to summer is queried, and the allocation weight is matched to 0.4. The specific values ​​of the various tables involved are set according to the usage requirements. This is an example and does not represent the actual intervals and weights. The allocation table can be dynamically maintained by the operator through the background interface , no system upgrade is required. According to the currently calculated energy usage reference, the matching interval is located in the allocation table, the corresponding "allocation weight" is extracted, and the extracted allocation weight is applied to the original unit price components in the contract (such as basic allocation amount, electricity unit price, etc.). After reaching the tiered energy usage level (such as first-level electricity price, second-level electricity price, and third-level electricity price), the new allocation unit price is calculated in real time according to the energy usage reference adjustment, and the parameters of the bill generation engine are updated to ensure that the adjusted allocation amount is used in the current stage bill. Based on the objective energy usage reference, the automatic matching adjustment strategy is implemented to eliminate the arbitrariness of manual pricing and enhance tenants' trust. Through monitoring and early warning of tenants' energy usage reference, abnormal and large energy consumption can be identified in time, which helps to prevent cost losses caused by malicious or abnormal behavior.

[0027] In a preferred embodiment, the step of adjusting the apportionment amount based on the apportionment weight and usage includes: S2061. Obtain the total cost of energy use; S2062. Obtaining a total energy usage value and a tenant energy value for each tenant based on the energy usage data; S2063. Obtain corresponding energy consumption weight according to usage; S2064. Obtain the apportioned amount based on the total energy usage value, the tenant energy value of each tenant, the apportionment weight, the energy consumption weight, and the total energy usage cost.

[0028] As in steps S2061 to S2064 above, the total energy usage cost of the entire apartment during the period (the cost of each of the three items of electricity, water, and gas) is pulled from the energy cost settlement module or the third-party energy platform interface. If the cost has not been settled, it can be estimated and updated in real time based on the local rate and total usage. From the time series database, according to the same period, the total energy usage value (the total usage of each of the three items of electricity, water, and gas for the entire apartment during the period) and the tenant energy value (the usage of each of the three items of electricity, water, and gas for each tenant's room or account) are summarized. The corresponding energy consumption weight is obtained based on the usage situation and combined with the energy consumption weight. For example, when the usage situation is normal energy consumption, the corresponding energy consumption weight is 1. For abnormal energy consumption, although it can also be based on the usage between each user, The energy consumption is apportioned based on the energy consumption ratio, but in fact, there are still big problems. There are many reasons for abnormal energy consumption, such as equipment abnormality, equipment aging, etc. In the above cases, energy consumption will increase significantly. In this case, if the abnormal reason is not determined and the apportionment is made directly, it will be obviously unfair to most tenants. Therefore, it is necessary to first determine the specific cause of the abnormal energy and match the corresponding weight according to the cause. For example, the abnormal energy consumption caused by equipment aging has a corresponding energy consumption weight of 0.8. The abnormal energy consumption caused by human damage to the equipment has a corresponding energy consumption weight of 1.2. The apportionment amount can be distinguished more finely according to the specific reasons for the use, and the apportionment amount of the energy cost that each tenant should bear can be calculated respectively. The calculation formula of the apportionment amount is: , where Expressed as the apportioned amount, Expressed as the total energy use, Expressed as the tenant energy value of each tenant, Expressed as the total energy cost, Expressed as the apportioned weight, Expressed as energy consumption weight, the tiered energy price can be linked to the actual usage ratio. Based on the usage ratio, the energy cost after reaching the tiered unit price can be regulated. The energy fee is allocated according to the actual usage, which effectively avoids the "big pot" sharing, encourages energy conservation, and reduces public management disputes.

[0029] In a preferred embodiment, when abnormal energy consumption or equipment status data indicates an equipment failure, a priority maintenance work order is automatically generated and assigned to an operation and maintenance person to identify the abnormal factors, which include equipment aging, equipment damage, and human damage. The steps include: S301. When abnormal energy consumption or device status data indicates a device failure signal, corresponding energy transient abnormal fluctuations and device failure signals are obtained based on the energy usage data and device status data. S302, obtaining abnormal factors of energy transient abnormal fluctuations and equipment failure signals, wherein the abnormal factors include equipment aging, equipment damage, and human damage; S303. Obtain corresponding abnormal weights according to abnormal factors; S304, obtaining corresponding energy instantaneous abnormal value and equipment fault value respectively according to energy instantaneous abnormal fluctuation and equipment fault signal; S305. Obtain corresponding abnormal values ​​according to the energy instantaneous abnormal value, the equipment fault value, and the abnormal weight; S306: Obtain a work order priority table, wherein the work order priority table includes multiple abnormal value intervals and priority operation and maintenance work orders corresponding to each abnormal value interval; S307. Obtain the corresponding priority operation and maintenance work order from the work order priority table according to the abnormal value interval corresponding to the abnormal value; S308. Allocate operation and maintenance personnel according to the contract period and priority of the operation and maintenance work order.

[0030] As in the above steps S301 to S308, when abnormal energy consumption or device status data is obtained and a device fault signal appears, the energy usage data (such as instantaneous power, current, voltage, water flow, etc.) and device status data (such as switch status, operating frequency, temperature, etc.) uploaded by the IoT gateway are read in real time. Through threshold comparison, mutation detection or machine learning model, the instantaneous abnormal energy fluctuation (such as power surge / sudden drop) and device operation abnormality (such as equipment shutdown, excessive vibration, excessive temperature, etc.) are identified. For each abnormal signal detected, the log or sensor data is used to identify the abnormal fluctuation. Obtain the cause and classify it as "human damage" (such as misoperation, illegal intrusion, abnormal power consumption) or "equipment damage" (such as pipe leakage, motor damage), "equipment aging" (such as component aging), and query the "abnormal weight table". This table is pre-set based on historical experience or abnormal factors in recent years, and can also be modified and maintained through manual revision. The abnormal weight table includes multiple different abnormal factors (misoperation, pipe leakage, component aging), and the abnormal weight corresponding to each abnormal factor (0.2, 0.5, 0.8). If the abnormal factor is component aging, match The weight obtained by matching the abnormal weight table is 0.8. The specific value is set according to the specific usage requirements. This is an example and does not represent the actual interval and weight. The initial weight is assigned according to the type of abnormal factor. The weight reflects the relative impact of different types of abnormalities on safety and operation and maintenance costs. Each energy abnormal fluctuation signal is represented by the energy abnormal fluctuation value, and the equipment failure signal is represented by the equipment failure value. The assignment of each value is based on the preset energy abnormality comparison table (including multiple energy abnormal fluctuation signal intervals and the energy abnormal fluctuation values ​​corresponding to each interval. The specific reading of the IoT gateway is the energy abnormal fluctuation signal. For example, the instantaneous power is 260 watts, and the corresponding energy abnormal fluctuation signal interval is 255-265, and the energy abnormality value corresponding to this interval is 1.2) and the equipment failure comparison table (including multiple equipment states and the equipment failure value corresponding to each state. For example, the equipment state of the current instantaneous power abnormality is detected to be overtemperature, and the equipment failure value corresponding to overtemperature is 1.5). The energy abnormality value, equipment failure value and corresponding abnormality weight are combined to calculate the "abnormal value" of a single event. The calculation formula of the abnormal value is: , where Z represents the abnormal value, C represents the energy instantaneous abnormal value, and B represents the equipment failure value. Expressed as anomaly weight, the anomaly value quantifies the severity of the event. Read the "Work Order Priority Table", which defines multiple anomaly value intervals and their corresponding priority categories. According to the calculated anomaly value interval, automatically select (for example: high / medium / low) priority operation and maintenance work order templates, and fill in event details, anomaly values, location, timestamp and other information. Combined with the contract period (such as trial period, first month of lease, renewal period, etc.) and the tenant's "payment weight value", the available operation and maintenance personnel pool can be screened: for high-priority work orders, priority is given to dispatching those with fast response speed and matching skills. For medium and low priority tasks, technicians will dispatch work orders based on rotation or geographical location, and ultimately automatically push the work orders to the corresponding personnel's operation and maintenance platform or mobile terminal, completing a closed-loop dispatch, monitoring and quantifying anomalies in real time, and automatically generating high-priority work orders to ensure that critical faults or large energy fluctuations can be responded to and handled immediately, minimizing equipment downtime. Operation and maintenance dispatch combines contract cycles and tenant credit, and can flexibly adjust response strategies, providing faster responses for new contracts or high-credit tenants, and reasonably allocating resources for ordinary work orders to optimize labor costs.

[0031] It is worth mentioning that the steps to obtain abnormal factors of transient energy fluctuations and equipment failure signals include: S3021. Obtain energy usage data and device status data in the apartment. The energy usage data includes readings of electricity meters, water meters, and gas meters. The device status data includes the operating status of devices in the room. S3022. Based on a preset data anomaly identification model, perform time series processing on energy usage data to identify abnormal instantaneous energy fluctuations; S3023. Determine whether the device has a fault based on the device operating status and generate a device fault signal; S3024. Correlate the identified abnormal transient energy fluctuation with the equipment fault signal to obtain the triggering time of the abnormal event and the corresponding equipment identifier; S3025. Based on preset abnormal factor identification rules, obtain abnormal factors that cause transient abnormal energy fluctuations and equipment failure signals, where abnormal factors include equipment aging, equipment damage, and human damage.

[0032] As in steps S3021 to S3025 above, energy usage data and device status data are collected by IoT sensor devices deployed in the apartment. Energy usage data includes readings of electricity meters, water meters, gas meters, etc., and device status data includes current operating status information of various devices in the apartment. Energy usage data is processed by a preset data anomaly recognition model (such as time series analysis based on sliding windows, mutation detection algorithm, etc.), and energy instantaneous abnormal fluctuations such as sudden increases and decreases in energy consumption are identified in real time, thereby achieving early anomaly detection. Combined with the collected device operating status data, the device operating status is processed (such as temperature, voltage, working hours, etc.) to determine whether there are any equipment failures, overloads, long-term shutdowns, etc. Fault conditions, generate equipment fault signals, match and process the instantaneous abnormal fluctuations of energy with equipment fault signals, extract the time nodes of occurrence, identification codes of the equipment involved and other information, so as to establish the time correspondence between abnormal events and equipment, based on the abnormal factor identification rules preset by the system (equipment aging factor identification logic: the service life of the equipment exceeds the set threshold, the frequency of failures increases periodically, and the operating efficiency (such as motor power factor, energy consumption ratio, etc.) gradually decreases. For example, the energy consumption of a water heater continues to rise and the heating time is prolonged under normal use; equipment damage factor identification logic, energy data mutates (suddenly increase or drop) in a very short time, and power outages, overloads, shutdowns and other abnormalities occur at the same time as the equipment status signal, and are compared with historical abnormalities of the same type of equipment). The similarity between the state before the damage is high, such as a sudden change in the air conditioner current and the equipment status showing shutdown, is determined to be damaged; the human damage factor identification logic, abnormal energy consumption during abnormal time periods (such as the equipment suddenly running at high power late at night), inconsistent energy readings with equipment status (such as the equipment showing off, but the meter is still beating), multiple forced power outages, repeated restarts, illegal dismantling or signal interruption, such as tenants frequently plugging and unplugging the power supply to evade energy consumption monitoring, or maliciously damaging sensor equipment), attribute abnormal events, identify the root cause of energy anomalies or equipment failures, and the identification results divide the abnormal factors into three categories: equipment aging, equipment damage and human damage, providing a basis for subsequent maintenance, responsibility division and management decision-making. Through real-time monitoring and factor identification of energy anomalies and equipment failures, the anomaly response time can be greatly shortened, the source of the fault can be efficiently located, and the operation and maintenance efficiency can be improved. The introduction of identification rules based on data models can intelligently determine whether the anomaly is a problem with the equipment itself or caused by human intervention, reducing the cost of manual investigation. By identifying and classifying abnormal factors, it helps to clarify the boundaries of responsibility, such as distinguishing between human damage and equipment aging, facilitating contract management and dispute resolution. The identified equipment aging or damage information can provide data support for equipment replacement and maintenance plans, realizing the transition from passive maintenance to active maintenance, and timely discovering abnormal energy usage and equipment operation, which helps to ensure the stable operation of the apartment energy system and prevent energy waste and potential safety risks.

[0033] In a preferred embodiment, the step of assigning operation and maintenance personnel according to the contract period and priority of the operation and maintenance work order includes: S3081. Obtain a worksheet for the operation and maintenance personnel, wherein the worksheet includes a schedule for the operation and maintenance personnel to process work orders; S3082. Obtain the remaining contract period according to the contract period, and obtain the preliminarily screened operation and maintenance personnel based on the remaining contract period and the worksheet; S3083. Create an operation and maintenance personnel score table based on the initially screened operation and maintenance personnel, where the operation and maintenance personnel table includes the work order comprehensive value range corresponding to each operation and maintenance personnel; S3084. Obtain the payment weight value and energy usage reference of the corresponding tenant based on the priority operation and maintenance work order, and obtain the work order initial value based on the priority operation and maintenance work order; S3085. Obtain a comprehensive value of the work order based on the work order initial value, payment weight value, and energy usage reference; S3086. Obtain the corresponding operation and maintenance processing personnel from the operation and maintenance personnel score table according to the work order comprehensive value interval corresponding to the work order comprehensive value.

[0034] As in steps S3081 to S3086 above, the "work sheet" of all operation and maintenance processing personnel is extracted from the human resources or operation and maintenance management system. This sheet records the availability, schedule, and time blocks of assigned work orders for each operation and maintenance personnel in each future period. Based on the remaining period of the room contract to which the current work order belongs (for example, there are still 3 months until the end of the lease), it is compared with the operation and maintenance personnel work sheet to screen out personnel who have no major vacation or scheduling conflicts within this period, forming a "preliminary candidate operation and maintenance personnel" list. For each candidate, the comprehensive value range for the work orders they can undertake is pre-defined in the "operation and maintenance personnel score table". For example, the comprehensive value range for the work orders that operation and maintenance personnel Zhang San can accept is [0-30]. These ranges can be dynamically configured based on factors such as personnel experience, skill level, and historical performance. The "payment weight value" and "energy usage reference" corresponding to the tenant of the priority operation and maintenance work order are read and combined with the "initial value" of the work order itself (such as basic urgency, abnormal value, etc.). Based on the work order initial value, payment weight value, and energy usage reference, the comprehensive value of the work order is calculated. The calculation formula for the work order comprehensive value is: In the formula, H represents the comprehensive value of the work order, F represents the initial value of the work order, Q represents the payment weight value, and N represents the energy usage reference. The output is the "comprehensive value of the work order" that reflects the urgency of the work order, potential risks, and customer value. The calculated comprehensive value of the work order is matched with the available interval of each person in the "Operation and Maintenance Personnel Score Table". The operation and maintenance personnel in the corresponding interval are found, and the dispatch object is finally determined according to the rotation or load balancing principle. The work order is automatically issued to the operation and maintenance end of the person, completing the closed-loop dispatch. Available personnel are prioritized based on the remaining contract period to avoid response delays caused by conflicts between personnel scheduling and contract periods, ensuring the quality of contract services. Through the two-way alignment of the "comprehensive value of the work order" and the "personnel score interval", high-value or high-risk work orders are handled by more experienced or higher-skilled personnel, improving the success rate and efficiency of maintenance.

[0035] In a preferred embodiment, the steps of generating a comprehensive rent bill based on the contract period and the adjusted apportionment amount, and calculating the payment weight value through the payment interface include: S401. Generate a comprehensive rent bill based on the contract period and the adjusted amortized amount; S402. Triggering automated deductions and overdue warnings through a preset payment interface based on the comprehensive rental bill; S403. Obtain automated deduction data and overdue data, and obtain a corresponding automated deduction success rate based on the automated deduction data, and obtain a corresponding overdue duration, overdue amount, and overdue bill type based on the overdue data; S404. Obtain the corresponding overdue bill weight value according to the overdue bill type; S405. Obtain a payment weight value based on the automatic deduction success rate, overdue duration, overdue amount, and overdue bill weight value.

[0036] As in the above steps S401 to S405, based on the contract period (such as monthly, quarterly) and the adjusted apportionment amount of each energy item, the apportionment amount of each tenant is inserted into the current stage of the rental bill to form a complete bill of "rent + energy fee", which includes the start and end dates of the lease, apportionment amount, basic rent payable, subtotal and other fields, and is saved to the bill database. When the comprehensive bill is generated and reaches the due date, the preset payment interface (such as bank deduction, third-party payment gateway) is automatically called to initiate a deduction request. At the same time, the overdue monitoring module is started to mark the bills that have not been successfully deducted in real time, and push warning notifications (SMS, email or App push) according to the set overdue threshold (such as 1 day, 3 days, 7 days overdue) to remind the tenant to pay in time. After the deduction result is called back, the automated deduction data is collected, including: number of requests, number of successes, reasons for failure, etc., and the automated deduction success rate is calculated. The overdue monitoring module records the overdue duration (actual number of days), overdue amount and corresponding bill type (basic rent, energy fee or comprehensive bill) of the overdue bill, and archives it to the overdue log library. According to the overdue bill type, the corresponding weight value is read from the "overdue bill weight table", for example: the basic rent overdue option is reset to 0.9, and the energy fee overdue option is reset to 0.85. The weight can be dynamically adjusted by the operator according to the risk preference. According to the key indicators, the automated deduction success rate, overdue duration, overdue amount, and overdue bill weight value, the payment weight value is calculated. The calculation formula for the payment weight value is: , where Q represents the payment weight value, K represents the success rate of automatic deduction, T represents the overdue time, and J represents the overdue amount. It is expressed as the weight value of overdue bills, and the calculation result is a comprehensive score used to measure the tenant's "payment health" and "credit priority". Automated deductions can significantly reduce the cost of manual collection and increase the speed of payment recovery. Overdue warnings provide timely reminders to help tenants quickly correct their behavior and reduce overdue losses. Through quantitative analysis of deduction success rates and overdue behaviors, a scientific payment weight value is formed to provide data support for subsequent operation and maintenance work order allocation, renewal approval, deposit adjustment, etc. Based on the payment weight, stricter deposit or guarantee measures can be taken for high-risk tenants, and more flexible payment methods or discounts can be provided to high-credit tenants to reduce bad debt risks.

[0037] In a preferred embodiment, the steps of outputting a leasing strategy and leasing recommendations based on the components of the operation and maintenance work order, the property status, the payment weight value, and the contract information, and encrypting the tenant contract information using a dynamic encryption strategy based on the contract period include: S501. Obtain the abnormal factors and property status of the priority operation and maintenance work order. If the abnormal factors are human-caused, obtain the corresponding human factor weight based on the abnormal factors, and obtain the corresponding property residual weight based on the property status. S502: Obtain a period weight based on the contract period of the tenant contract information, and obtain a tenant violation score based on the contract terms of the tenant contract information; S503. Obtaining a tenant rental score based on the human factor weight, the remaining housing weight, the cycle weight, and the tenant violation score; S504: Obtain a lease table, where the lease table includes multiple tenant lease scoring intervals and a lease policy corresponding to each tenant lease scoring interval; S505. Obtain the corresponding leasing strategy from the leasing table according to the tenant leasing score interval corresponding to the tenant leasing score; S506. Provide leasing suggestions based on leasing strategies; S507: Encrypt the tenant contract information using a dynamic encryption strategy based on the contract period.

[0038] As in steps S501 to S507 above, the abnormal factors (equipment aging, human damage, and equipment damage) and the current "property status" (such as vacant, partially available, and pending repair) are read from the priority operation and maintenance work order. If the extracted abnormal factor is human damage, a weight value is assigned according to the pre-defined "human factor weight table". If the extracted abnormal factor is equipment damage and equipment aging, the weight is 1, and a weight value is assigned according to the pre-defined "property status weight table" (for example, the weight of human error is 0.9, the weight of malicious damage is 0.8, and the property status is 60). The weight for vacancy is 0.96, and the weight for 80% vacancy is 0.98. The "contract period" (number of remaining months) is read from the tenant's contract information and mapped to a period weight through the "period weight table" (for example, a remaining period greater than 6 months has a weight of 1.0, and a remaining period less than 1 month has a weight of 0.9). The contract terms are extracted (for example, whether the rent is paid on time, whether the prohibition on keeping pets is violated), and the tenant violation score is obtained (range 0-100). The tenant rental score is calculated based on the human factor weight, the remaining weight of the property, the period weight, and the tenant violation score. The calculation formula for the tenant rental score is: In the formula, P represents the tenant rental score, r represents the human factor weight, f represents the remaining weight of the house, q represents the cycle weight, and L represents the tenant violation score. This ensures that the relative importance of different indicators can be flexibly configured according to the operation strategy. The "rental strategy table" is loaded. The table divides the rental score into several intervals and corresponds to a set of rental strategies for each interval. For example: 80 to 100, priority renewal, provide rent discounts, 50 to 79, normal renewal, no additional discounts. According to the calculated rental score, the corresponding interval is located and the corresponding strategy is extracted. According to the selected rental strategy, specific suggestion texts are generated for operators and tenants, such as "recommended 8-month renewal and 5% rent discount" or "recommended to sign a short-term contract of no more than 3 months" "Term contracts and strengthen property inspections", it is recommended that notifications can be pushed to relevant persons in charge and tenants through the management background, APP or email to achieve a closed-loop decision-making process. For sensitive information such as tenant contracts, a dynamic key management and encryption scheme based on the contract cycle (such as regularly rotating symmetric keys and attribute-based encryption) is adopted to ensure the confidentiality and integrity of data during storage and transmission, and support secure decryption and auditing when needed. Based on multi-dimensional quantitative indicators, differentiated strategies can be provided for each tenant and property status, which not only protects the interests of landlords but also improves tenant satisfaction. Man-made damage, property maintenance and performance risks are included in the same scoring system to help managers identify properties with high renewal risks or high potential costs, and intervene or avoid risks in advance.

[0039] In a preferred embodiment, the steps of encrypting the tenant contract information using a dynamic encryption strategy based on the contract period include: S5071. Divide the encryption phases according to the contract period and define encryption policies for the contract validity period, renewal period, and termination period. The first encryption policy is used during the contract validity period, the second encryption policy is used during the contract renewal period, and the third encryption policy is used during the contract termination period to trigger historical data migration. S5072. During the effective period of the contract, encrypt the tenant's identity information and the contract terms using the first encryption strategy, obtain the contract identifier of the tenant's contract information, and generate an initial key based on the contract identifier; S5073. During the contract renewal period, the rental amount and payment method are re-encrypted using the second encryption strategy, and the encryption key is dynamically updated based on the remaining duration of the contract period. S5074. When the contract period reaches its end, the sensitive data in the tenant contract information is migrated to an independent storage partition, and the migrated data is reinforced using the third encryption strategy; S5075. When decrypting access, the corresponding decryption algorithm is matched according to the current contract stage, and the decryption authorization is completed through the two-factor authentication mechanism of the tenant's identity biometrics and dynamic verification code. As in steps S5071 to S5075 above, the entire life cycle is divided into three stages according to the contract period: Effective period: the period from the date the contract officially takes effect to the first expiration date; Renewal period: the transition period between when the tenant submits a renewal application and when the contract is updated; Termination period: After the contract expires, the historical data will be archived and cleaned up; Different encryption strategies are specified for each stage: the first, second, and third encryption strategies correspond to different security strengths and key management requirements. During the validity period of the contract, the first encryption strategy (such as AES-128-CBC+Random IV) is applied to sensitive information (tenant identity information, contract terms), and a unique contract identifier is generated after encryption. The initial symmetric key of this contract is derived based on this identifier and the system master key, which is used for subsequent data encryption and decryption. After entering the renewal period, the variable fields such as "rent amount" and "payment method" in the contract are encrypted again using the second encryption strategy (such as AES-256-GCM+authentication tag) to improve data integrity protection. At the same time, the key dynamic update is triggered according to the strategy based on the remaining length of the contract (such as monthly decrease), and the symmetric key is rotated regularly (or according to the renewal node) to prevent the risk of long-term key leakage. When the contract enters the termination period, all historical sensitive data of the contract will be migrated to an independent archive partition and automatically cleaned up in the production library. To detect plaintext or weak encryption traces, apply a third encryption strategy (such as key encapsulation + segmented encryption based on hardware security modules) to archived data and generate audit logs to ensure that it can be decrypted only when required by legal audits or legal affairs. When a user or system needs to access contract data, first detect which stage the current contract is in and select the matching decryption algorithm and key management process. During the effective period or renewal period, after verification through two-factor authentication (tenant identity biometrics + dynamic verification code), call the corresponding key to decrypt the required fields; during the termination period, only in the compliance audit process, the authorized administrator submits a decryption application and decrypts after going through the approval chain. Different encryption strategies and key management methods of different strengths are used in different contract stages to protect active data and archived data in a targeted manner, reduce the risk of full leakage caused by the compromise of a single key, match the decryption algorithm in stages, and combine with two-factor authentication to ensure that sensitive content can only be accessed in legal scenarios and after multiple verifications, thereby improving overall security and tenant trust.

[0040] Please see the attached Figure 2 As shown, the present invention also provides an apartment operation and maintenance data management system based on the Internet of Things, which is used in the above-mentioned apartment operation and maintenance data management method based on the Internet of Things, including: The device management module is used to collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the operating status of the devices. Energy consumption is determined by combining energy usage data and corresponding device operating status. Usage includes normal energy consumption and abnormal energy consumption. The tenant module is used to dynamically associate tenant contracts with energy usage data, determine the allocation weight based on energy usage data, and dynamically adjust the allocation amount based on usage; The operation and maintenance work order module is used to automatically generate priority maintenance work orders when abnormal energy consumption or equipment status data indicates equipment failure, and assign the operation and maintenance work orders to operation and maintenance personnel to identify abnormal factors, such as equipment aging, equipment damage, and human damage; The payment weight module is used to generate a comprehensive rent bill based on the contract period and the adjusted amortization amount, and calculate the payment weight value through the payment interface; The leasing strategy module is used to combine the components of the operation and maintenance work order, the property status, the payment weight value and the contract information to output the leasing strategy and leasing suggestions, and encrypt the tenant contract information using a dynamic encryption strategy based on the contract cycle.

[0041] The device management module collects energy usage data (electricity meter readings, water meter readings, gas meter readings) and device status data (number of items in the room, operating status of air conditioners / refrigerators, etc.) in real time through smart electricity meters, water meters, gas meters, and environmental and device sensors deployed in each room. The gateway filters, removes noise, and fills in missing values ​​on the raw data before uploading it to the cloud-based time series database, generating cleaned and standardized time series data. The tenant module obtains each tenant's contract terms, contract period, rent amount, payment method, and tenant identity information from the contract management system, automatically associates the real-time collected energy usage data with the tenant's identity information, and writes it into the time series database. The associated energy data is then processed through time series processing to generate allocation weights and automatically adjust the current allocation amount based on usage. The operation and maintenance work order module monitors energy usage and device status signals in real time, identifies instantaneous energy fluctuations and equipment failures, determines abnormal events using thresholds or models, extracts abnormal factors such as human operation or equipment failure, assigns corresponding weights, and calculates a comprehensive abnormal value.The system automatically matches work order priorities (high / medium / low) based on the outlier range. It reads the remaining contract period and the operation and maintenance personnel schedule, preliminarily screens available personnel, matches the most suitable handler based on the work order's overall value and the operation and maintenance personnel's score range, and issues work orders in a closed loop. The payment weight module calculates the total energy usage of the entire apartment and each tenant based on the contract period and adjusted shared amount, calculates the respective shared costs proportionally, and combines them into the rental bill to form a comprehensive "rent + energy fee" bill. It initiates automatic deductions and overdue warnings through the preset payment interface, collects data such as the deduction success rate, overdue duration, and amount, and calculates the tenant's "payment weight" based on the overdue bill type and weight table. The rental strategy module obtains the "human factor weight" and "property status weight" generated in the work order, the tenant's contract period weight, and the tenant's violation score, and calculates the tenant's "rental score" according to the weighted model. In the rental strategy table, it extracts the corresponding strategy (such as preferential renewal, short-term contract, or termination suggestion) based on the score range. It generates personalized rental suggestions and pushes them to the management backend or tenant end. Data is divided into three phases based on the contract lifecycle: effective period, renewal period, and termination period. Primary, secondary, and tertiary encryption strategies are applied, respectively. During the effective period, identity and terms fields are encrypted with AES-128 and a contract-specific key is generated. During the renewal period, variable fields are encrypted twice and the key is dynamically rotated. During the termination period, historical data is migrated to an archive partition. Hardware security modules are used to reinforce encryption. During access, the decryption algorithm is matched according to the contract phase, and decryption is authorized through two-factor authentication (biometrics + dynamic verification code). The device management module enables real-time collection and large-screen visualization of operational data for the entire apartment, facilitating on-site operations and management monitoring. Energy consumption is dynamically adjusted based on actual usage and meticulously allocated, allowing tenants to "pay as they go" and enhance trust. Multi-dimensional anomaly detection and automated prioritized work order dispatch shorten response times and improve maintenance efficiency. Renewal or termination recommendations are automatically generated based on operational performance and tenant credit, optimizing occupancy rates and customer satisfaction. Phased dynamic encryption and two-factor decryption mechanisms meet data confidentiality and regulatory requirements, minimizing the risk of data leakage.

[0042] And, an apartment operation and maintenance data management terminal based on the Internet of Things, comprising: one or more processors; a storage device having one or more programs stored thereon; When one or more programs are executed by one or more processors, the one or more processors implement an apartment operation and maintenance data management method based on the Internet of Things.

[0043] The foregoing is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, and such improvements and modifications are also within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained herein shall, unless otherwise specified or limited, be implemented in accordance with conventional means in the art.

Claims

1. A method for apartment operation and maintenance data management based on the Internet of Things, characterized in that: include: Collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the device operating status. Combined with energy usage data and corresponding device operating status, determine energy consumption and usage, including normal and abnormal energy consumption. Dynamically link tenant contracts with energy usage data, determine allocation weights based on energy usage data, and dynamically adjust allocation amounts based on usage; When abnormal energy consumption or equipment failure signals appear in the equipment status data, a priority maintenance work order is automatically generated and assigned to the operation and maintenance personnel to confirm the abnormal factors, which include equipment aging, equipment damage, and human damage; Generate a comprehensive rent bill based on the contract period and the adjusted amortized amount, and calculate the payment weight value through the payment interface; Combining the components of the operation and maintenance work order, the property status, the payment weight value and the contract information, the leasing strategy and leasing recommendations are output, and the tenant contract information is encrypted using a dynamic encryption strategy based on the contract cycle.

2. The apartment operation and maintenance data management method based on the Internet of Things according to claim 1 is characterized in that: Collecting energy usage data and device status data of IoT devices in the apartment, where the energy usage data includes electricity meter readings, water meter readings, and gas meter readings, and the device status data includes the number of items in the room and the operating status of the devices. Combining the energy usage data and the corresponding device operating status to confirm energy consumption and usage, including normal energy consumption and abnormal energy consumption, includes the following steps: Collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the operating status of the devices. Obtaining corresponding multiple energy consumption values ​​according to energy usage data; Get the corresponding device status value according to the device operation status; Obtaining a usage status value based on multiple energy consumption values ​​and device status values; Obtaining a usage status threshold and determining whether the usage status value exceeds the usage status threshold; If the usage status value does not exceed the usage status threshold, the energy consumption is determined to be normal; If the usage status value exceeds the usage status threshold, the energy consumption is determined to be abnormal energy consumption.

3. The apartment operation and maintenance data management method based on the Internet of Things according to claim 1 is characterized in that: The steps for dynamically associating tenant contracts with energy usage data, determining allocation weights based on energy usage data, and dynamically adjusting allocation amounts based on usage include: Obtain tenant contract information, including contract period, rent amount, payment method, and tenant identity information; Dynamically associate and store energy usage data with tenant identity information; Get the current season information and obtain the corresponding season weight based on the season information; Obtaining a plurality of corresponding energy usage values ​​according to the energy usage data, and obtaining a corresponding energy usage reference based on the plurality of energy usage values ​​and seasonal weights; Obtaining an allocation table, wherein the allocation table includes a plurality of energy usage reference intervals and an allocation weight corresponding to each energy usage reference interval; Obtain the corresponding allocation weight from the allocation table according to the energy usage reference interval corresponding to the energy usage reference; Adjust the allocation amount based on allocation weight and usage.

4. The apartment operation and maintenance data management method based on the Internet of Things according to claim 3 is characterized in that: The steps to adjust the allocation amount based on allocation weights and usage include: Obtain the total cost of energy use; Obtain the total energy usage value and tenant energy value of each tenant based on the energy usage data; Obtain the corresponding energy consumption weight according to usage; The apportionment amount is obtained based on the total energy usage value, the tenant energy value of each tenant, the apportionment weight, the energy consumption weight, and the total energy usage cost.

5. The apartment operation and maintenance data management method based on the Internet of Things according to claim 1 is characterized in that: When abnormal energy consumption or equipment status data indicates equipment failure, a priority maintenance work order is automatically generated and assigned to the operation and maintenance personnel to confirm the abnormal factors, which include equipment aging, equipment damage, and human damage. The steps include: When abnormal energy consumption or equipment status data is obtained and equipment failure signals appear, the corresponding energy instantaneous abnormal fluctuations and equipment failure signals are obtained based on the energy usage data and equipment status data; Obtain abnormal factors of transient energy fluctuations and equipment failure signals, including equipment aging, equipment damage, and human damage; Obtain the corresponding abnormal weight according to the abnormal factors; Obtain corresponding energy instantaneous abnormal value and equipment failure value according to energy instantaneous abnormal fluctuation and equipment failure signal respectively; Obtain the corresponding abnormal value based on the energy instantaneous abnormal value, equipment failure value and abnormal weight; Obtain a work order priority table, where the work order priority table includes multiple abnormal value intervals and priority operation and maintenance work orders corresponding to each abnormal value interval; Obtain the corresponding priority operation and maintenance work order from the work order priority table based on the abnormal value interval corresponding to the abnormal value; Assign operation and maintenance personnel based on the contract cycle and priority of the operation and maintenance work orders.

6. The apartment operation and maintenance data management method based on the Internet of Things according to claim 5 is characterized in that: The steps for assigning maintenance personnel based on the contract period and priority of maintenance tickets include: Obtaining a work list of an operation and maintenance processing personnel, wherein the work list includes a schedule for the operation and maintenance processing personnel to process work orders; Obtain the remaining contract period based on the contract period, and obtain the preliminarily screened operation and maintenance personnel based on the remaining contract period and the worksheet; An operation and maintenance personnel scoring table is established based on the initially screened operation and maintenance personnel, wherein the operation and maintenance personnel table includes the work order comprehensive value range corresponding to each operation and maintenance personnel; Obtain the payment weight and energy usage reference of the corresponding tenant based on the priority operation and maintenance work order, and obtain the initial value of the work order based on the priority operation and maintenance work order; Obtain the comprehensive value of the work order based on the work order initial value, payment weight value and energy usage reference; According to the work order comprehensive value interval corresponding to the work order comprehensive value, the corresponding operation and maintenance processing personnel are obtained from the operation and maintenance personnel score table.

7. The apartment operation and maintenance data management method based on the Internet of Things according to claim 1 is characterized in that: The steps for generating a comprehensive rent bill based on the contract period and the adjusted amortized amount, and calculating the payment weight value through the payment interface include: Generate a comprehensive rent bill based on the contract period and adjusted amortization amount; Trigger automated deductions and overdue warnings based on the rental comprehensive bill through the preset payment interface; Obtain automated deduction data and overdue data, and obtain the corresponding automated deduction success rate based on the automated deduction data, and obtain the corresponding overdue duration, overdue amount, and overdue bill type based on the overdue data; Obtain the corresponding overdue bill weight value according to the overdue bill type; Obtain the payment weight value based on the automatic deduction success rate, overdue duration, overdue amount, and overdue bill weight value.

8. The apartment operation and maintenance data management method based on the Internet of Things according to claim 1 is characterized in that: The steps of outputting a leasing strategy and leasing recommendations based on the components of the operation and maintenance work order, the property status, the payment weight, and the contract information, and encrypting the tenant contract information using a dynamic encryption strategy based on the contract cycle include: Obtain the abnormal factors and property status of the priority operation and maintenance work order. If the abnormal factors are human-caused, obtain the corresponding human factor weight based on the abnormal factors, and obtain the corresponding property residual weight based on the property status. Obtain a period weight based on the contract period of the tenant contract information, and obtain a tenant violation score based on the contract terms of the tenant contract information; Obtain tenant rental scores based on human factor weights, remaining housing weights, cycle weights, and tenant violation scores; Obtain a lease table, where the lease table includes multiple tenant lease score intervals and the lease policy corresponding to each tenant lease score interval; Obtain the corresponding leasing strategy from the leasing table based on the tenant leasing score interval corresponding to the tenant leasing score; Provide leasing advice based on leasing strategies; Tenant contract information is encrypted using a dynamic encryption strategy based on the contract cycle.

9. The apartment operation and maintenance data management method based on the Internet of Things according to claim 8 is characterized in that: The steps for encrypting tenant contract information using a dynamic encryption strategy based on the contract period include: Divide the encryption phases into stages based on the contract lifecycle, and define encryption policies for the contract validity period, renewal period, and termination period. The first encryption policy is used during the contract validity period, the second encryption policy is used during the contract renewal period, and the third encryption policy is activated during the contract termination period, triggering historical data migration. During the effective period of the contract, the tenant's identity information and contract terms are encrypted using the first encryption strategy to obtain the contract identifier of the tenant's contract information, and an initial key is generated based on the contract identifier; During the contract renewal period, the rental amount and payment method are encrypted twice using a second encryption strategy, and the encryption key is dynamically updated based on the remaining duration of the contract period; When the contract period reaches its end, the sensitive data in the tenant contract information will be migrated to an independent storage partition, and the migrated data will be reinforced using the third encryption strategy; During decryption access, the corresponding decryption algorithm is matched according to the current contract stage, and the decryption authorization is completed through a two-factor authentication mechanism of the tenant's identity biometrics and dynamic verification code.

10. An apartment operation and maintenance data management system based on the Internet of Things, applied to the apartment operation and maintenance data management method based on the Internet of Things according to any one of claims 1 to 9, characterized in that: include: The device management module is used to collect energy usage data and device status data from IoT devices in the apartment. Energy usage data includes electricity meter readings, water meter readings, and gas meter readings. Device status data includes the number of items in the room and the operating status of the devices. Energy consumption is determined by combining energy usage data and corresponding device operating status. Usage includes normal energy consumption and abnormal energy consumption. The tenant module is used to dynamically associate tenant contracts with energy usage data, determine the allocation weight based on energy usage data, and dynamically adjust the allocation amount based on usage; The operation and maintenance work order module is used to automatically generate priority maintenance work orders when abnormal energy consumption or equipment status data indicates equipment failure, and assign the operation and maintenance work orders to operation and maintenance personnel to identify abnormal factors, such as equipment aging, equipment damage, and human damage; The payment weight module is used to generate a comprehensive rent bill based on the contract period and the adjusted amortization amount, and calculate the payment weight value through the payment interface; The leasing strategy module is used to combine the components of the operation and maintenance work order, the property status, the payment weight value and the contract information to output the leasing strategy and leasing suggestions, and encrypt the tenant contract information using a dynamic encryption strategy based on the contract cycle.

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