CN-122022530-A - Park asset operation management system

CN122022530ACN 122022530 ACN122022530 ACN 122022530ACN-122022530-A

Abstract

The invention relates to the technical field of park asset management, in particular to a park asset operation management system which comprises a unified asset sensing module, a multidimensional digital twin engine, a predictive simulation module and an autonomous programming engine, wherein the unified asset sensing module is used for acquiring real-time running states, real-time economic factors and real-time environmental factors of assets, the multidimensional digital twin engine is used for calculating real-time economic dimensions and real-time ESG dimensions of the assets based on the data, the predictive simulation module is used for deducing comprehensive quantitative influences of operation strategies on economy, ESG and service quality, and the autonomous programming engine is used for autonomously selecting optimal strategies and generating control instructions according to deduction results and preset decision rules.

Inventors

LI XIAOQING
XIONG JIANBIN
LIU ZHIKAI
ZHOU XIN

Assignees

深圳市润信数据技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260326

Claims (9)

1. A campus asset operation management system, comprising: The asset sensing module is used for acquiring real-time running state data of the asset, real-time economic factor data related to asset operation and real-time environment factor data; The multidimensional digital twin module is connected with the asset perception module, and the inside of the module is provided with: the economic dimension operation model is used for executing a preset economic operation rule by taking the real-time running state data and the real-time economic factor data as inputs so as to generate real-time economic dimension data; the ESG dimension operation model is used for executing a preset ESG operation rule by taking the real-time running state data and the real-time environment factor data as inputs so as to generate real-time ESG dimension data; Constructing or updating a multi-dimensional digital twin body through data integration based on the generated real-time economic dimension data, real-time ESG dimension data and the real-time running state data; The prediction simulation module is connected with the multi-dimensional digital twin module and is used for: invoking the multi-dimensional digital twin in response to one or more operational policies to be selected; Deducing one or more operation strategies to be selected, wherein the one or more operation strategies respectively have comprehensive quantitative influences on the future state of the asset, and the comprehensive quantitative influences at least comprise influences on the economic dimension, the ESG dimension and the service quality parameter; The autonomous arrangement module is connected with the prediction simulation module and is used for: receiving and based on the comprehensive quantitative influence, and automatically selecting an optimal operation strategy from the one or more operation strategies to be selected according to a preset decision rule; Based on the optimal operating strategy, control instructions for controlling the asset are generated.
2. The campus asset operation management system of claim 1, wherein the asset awareness module is configured to obtain, via an application programming interface, a dynamic carbon emission factor from a regional grid data server in real time, wherein the dynamic carbon emission factor is a time-varying factor value characterizing a real-time power generation portfolio of the regional grid data server.
3. The campus asset operation management system of claim 2 wherein the asset awareness module is configured to automatically rollback to a locally stored static carbon factor representing a historical average for use in the generation of the real-time ESG dimension data upon detection of a timeout of the application program interface or a response to return an error code.
4. The campus asset operation management system of claim 1, wherein the real-time economic factor data comprises a time-of-use electricity price table, the real-time operational status data comprises real-time energy consumption data, and the step of the multidimensional digital twin module executing the economic dimension operational model comprises: Multiplying the real-time energy consumption data by the current time period electricity price in the time-sharing electricity price table to generate the real-time economic dimension data.
5. The campus asset operation management system of claim 1, wherein the quality of service parameters are calculated based on a service level agreement defining at least a tolerance range for asset status; the step of deducting the influence of the service quality parameter by the prediction simulation module comprises the following steps: Determining a difference between the simulated asset status and the tolerance range as a degree of deviation; And inquiring a preset nonlinear punishment rule base storing the mapping relation between the deviation interval and the punishment score so as to generate the service quality parameter based on the deviation.
6. The campus asset operation management system of claim 1, wherein said autonomous orchestration module selecting said optimal operation strategy according to said preset decision rules comprises: Filtering and eliminating all the operation strategies to be selected, which lead to the quality of service parameters being lower than the service level rigidity constraint threshold set by the user; For the remaining candidate operation policies, reading configurable decision weight values for the economic dimension and the ESG dimension configured and stored by a user interface; Before the weighted summation operation is performed, normalization processing is further performed on the economic dimension influence and the ESG dimension influence of the operation strategy to be selected; Based on the decision weight value, carrying out weighted summation operation on the influence subjected to normalization processing to calculate a comprehensive score; and selecting one to-be-selected operation strategy with the highest comprehensive score as the optimal operation strategy.
7. The campus asset operation management system of claim 6, wherein said autonomous orchestration module is configured to: And in the response that all the operation strategies to be selected lead to the service quality parameters to be lower than the service level rigidity constraint threshold, stopping the autonomous selection and triggering an alarm for manual decision upgrading.
8. The campus asset operation management system of claim 1, wherein the system further comprises: an asset logic encapsulation module for: Creating a logical service object representing an asset service; establishing a one-to-many or many-to-many mapping relationship between the logical service object and one or more physical asset twins in the multi-dimensional digital twins; wherein the real-time economic dimension and the real-time ESG dimension of the logical service object are configured to aggregate corresponding dimension data of the one or more physical asset twins through a summation operation.
9. The campus asset operation management system of claim 1, wherein the multi-dimensional digital twinning module is implemented in a federal architecture, wherein: the physical twinning layer and the state twinning layer of the multi-dimensional digital twinning body are deployed on an edge computing device; the economic dimension operational model and the ESG dimension operational model are deployed on a cloud platform for execution; And, the edge computing device is configured to periodically synchronize data of the state twinning layer to the cloud platform.

Description

Park asset operation management system Technical Field The invention relates to the technical field of park asset management, in particular to a park asset operation management system. Background In order to improve the operation efficiency and reduce the cost, various intelligent management systems are commonly deployed in modern large parks and buildings. The system mainly comprises a building automatic control system (BAS/BMS) for monitoring and controlling equipment such as Heating Ventilation Air Conditioning (HVAC), lighting and the like, an energy consumption management system (EMS) for collecting and counting energy consumption data such as water, electricity, gas and the like and generating a report form, and a Computer Auxiliary Facility Management (CAFM) or a work order system (CMMS) for managing asset accounts, lease contracts and maintenance flows. In recent years, with the development of BIM (building information model) and IoT (internet of things) technologies, a common prior art solution has emerged, which attempts to combine a static 3D model of BIM with "real-time running states" of IoT sensor acquisition. The intelligent park operation and maintenance system based on BIM and IoT can display state information such as "the A span 5 layer temperature is 25 ℃ and" the B water pump is running "on the 3D model in real time. The operator can monitor the campus status more visually through this visual twins and locate the device more quickly in the event of a failure. Although the above visual digital twin scheme based on BIM and IoT is an improvement over the traditional BAS, EMS systems in "state presentation", it still has fundamental drawbacks at the "operational decision" level: First, the "twinning" dimension is missing, resulting in the decision "data blindness". The existing twins are a "physical state twins". It can only answer "what is? but cannot answer in real time" what is the cost. The operator cannot see from this twin in real time the "current 25 ℃ operating state" and the corresponding "real time energy costs" and "real time carbon footprint" behind. Second, the lack of predictive simulation results in an operation that is "passive". Such visual twinning can only be "seen" and cannot be "calculated". Before an operator makes a decision (such as "raise air conditioner by 1 ℃), the twin cannot be utilized to" deduce "what kind of quantitative impact the decision will have on both" electricity cost "," carbon emission "and" tenant SLA ". All decisions still rely on "experience", a passive mode of "execute first, look at report later". Finally, multi-objective conflicts, failing to "autonomously optimize". The essence of park operation is to find a balance among the three conflicting objectives of "cost", "ESG" and "SLA". The prior art is split, EMS only saves energy, BAS only SLAs, they lack a unified computational model and decision engine to answer "how to find the comprehensive optimal solution of cost and ESG on the premise of satisfying SLAs. In view of this, a campus asset operation management system is presented. Disclosure of Invention The invention aims to provide a park asset operation management system which aims to solve the technical problems that in the prior art, asset operation decisions lack of real-time quantification on economic cost and ESG influence, predictive deduction cannot be carried out, and a plurality of operation targets are difficult to cooperatively optimize. In order to solve the above technical problems, the present invention provides a system for managing operations of assets in a campus, including: The asset sensing module is used for acquiring real-time running state data of the asset, real-time economic factor data related to asset operation and real-time environment factor data; The multidimensional digital twin module is connected with the asset perception module, and the inside of the module is provided with: the economic dimension operation model is used for executing a preset economic operation rule by taking the real-time running state data and the real-time economic factor data as inputs so as to generate real-time economic dimension data; the ESG dimension operation model is used for executing a preset ESG operation rule by taking the real-time running state data and the real-time environment factor data as inputs so as to generate real-time ESG dimension data; Constructing or updating a multi-dimensional digital twin body through data integration based on the generated real-time economic dimension data, real-time ESG dimension data and the real-time running state data; The prediction simulation module is connected with the multi-dimensional digital twin module and is used for: invoking the multi-dimensional digital twin in response to one or more operational policies to be selected; Deducing one or more operation strategies to be selected, wherein the one or more operation strategies respectively have comprehensive quantitative influenc