A multi-dimensional KPI index quantification and performance evaluation method and system

Abstract

The application discloses a kind of multi-dimension KPI index quantification and performance evaluation method and system, it is related to performance evaluation technical field, including: according to the condensation reflux tooth of target performance object, the domain data of target performance object is handled with domain mapping, and domain account record is obtained;According to the normalized tooth front area humidity in domain account record, the normalized tooth gap area temperature, the normalized tooth gap area humidity, the normalized tooth gap area ventilation intensity and the normalized tooth rear area humidity, determine the thin film hanging bridge continuation wet driving amount;According to the energy consumption account data of business account data, security account data and thin film hanging bridge continuation wet driving amount, determine the structural coupling proportion;The application improves the analysis accuracy and consistency of multi-dimension data.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation technology, and in particular to a method and system for quantifying and evaluating multi-dimensional KPI indicators. Background Technology

[0002] With the continuous expansion of energy storage power stations, new energy power plants, and supporting energy storage equipment, operation and maintenance assessments are gradually shifting from single-indicator evaluation to multi-dimensional comprehensive evaluation. Typically, a multi-dimensional KPI system is constructed using energy consumption indicators, safety indicators, and management indicators. Energy consumption indicators reflect the auxiliary energy consumption levels generated by the operation of air conditioning, dehumidification, fans, and power conversion equipment; safety indicators reflect insulation status, humidity changes, and thermal management alarms; and management indicators reflect inspection execution, work order processing, and defect closure quality. In actual operating environments, issues commonly exist inside energy storage battery compartments or converter equipment cabinets. In complex geometric channels formed by guide edges, folded edges, and drainage structures, when hot and humid airflow circulates between the supply and return air paths and is accompanied by temperature changes, condensation is easily generated on the local structural surface and forms a continuous liquid film along the structural boundary. This film can cross adjacent gaps to form a connecting channel under the influence of airflow and humidity difference, thereby allowing moisture to continuously migrate and expand inside the structure. This process will simultaneously cause an increase in local ambient humidity and an increase in the load on auxiliary equipment, which will cause energy consumption indicators and safety indicators to fluctuate synchronously within the same time interval, resulting in a superimposed impact on multi-dimensional KPI data.

[0003] Existing technologies for quantifying and evaluating multi-dimensional KPI indicators typically involve directly weighting and summarizing energy consumption, safety, and management indicators or performing simple correlation analysis. This fails to distinguish between synchronous changes in indicators caused by internal humidity migration paths and changes in indicators resulting from actual operational and maintenance activities. Consequently, increases in energy consumption and safety alarms caused by structural environmental factors are misjudged as fluctuations in operational and maintenance management levels. This leads to performance evaluation results deviating from actual management effectiveness, making it difficult to accurately reflect the true operational level of the target performance object. As a result, there are problems such as distorted evaluation results, unclear attribution of responsibility, and inaccurate assessment criteria. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies that make it difficult to accurately reflect the true operational level of target performance objects, and to propose a multi-dimensional KPI indicator quantification and performance evaluation method and system.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution: A multi-dimensional KPI indicator quantification and performance evaluation method includes: Based on the condensation reflux tooth of the target performance object, perform structural domain mapping processing on the structural domain data of the target performance object to obtain structural domain ledger records; Based on the normalized humidity in the pre-tooth region, temperature in the inter-tooth region, humidity in the inter-tooth region, ventilation intensity in the inter-tooth region, and humidity in the post-tooth region recorded in the structural domain ledger, the continuous humidification drive of the thin-film bridge is determined. By combining the energy consumption data, safety data, and continuous moisture drive of the thin-film hanging bridge from the business account data, the structural coupling ratio is generated; Based on the energy consumption account identifier corresponding to the energy consumption account data, the safety account identifier corresponding to the safety account data, and the management account identifier corresponding to the management account data, multiple account reference sequences are generated, and the candidate bridge stripping and fusion rules corresponding to the account reference sequences are stored in the rule library to obtain the bridge stripping and fusion rule library. Based on the bridge stripping and fusion rule base, the candidate bridge stripping and fusion rules are mapped to obtain particle identifiers, and the particle swarm set is obtained based on the particle identifiers. Based on the structural coupling ratio, the particle swarm is iteratively updated to obtain the bridge stripping and fusion rules for the target performance object, and the performance of the target performance object is evaluated.

[0006] Preferably, the structure domain ledger records are obtained, including: Obtain the raw data of the target performance object; Define the condensation reflux tooth of the target performance object; The raw data is divided into business account data and structural domain data. The business account data includes energy consumption account data, safety account data and management account data, while the structural domain data includes data in the front area, back area and rear area of ​​the tooth arranged around the condensation return tooth. Determine the supply air path, return air path, and drainage path for the target performance object; Based on the spatial relationship of the condensate return teeth in the supply air path, return air path, and drainage path, structural domain data is processed by structural domain mapping to obtain structural domain ledger records.

[0007] Preferably, determining the continuous humidification drive amount of the thin-film bridge includes: Extract normalized humidity in the pre-tooth region, temperature in the interdental region, humidity in the interdental region, ventilation intensity in the interdental region, and humidity in the post-tooth region from the structural domain ledger records; Based on the normalized interdental temperature and humidity, and according to the set dew point calculation rules, the interdental dew point data is determined. The difference between the normalized interdental temperature and the interdental dew point data is normalized to obtain dew point approximation data. Based on the normalized humidity in the back tooth region, the normalized humidity in the front tooth region, the normalized ventilation intensity in the interdental region, and the dew point approximation data, the continuous humidification driving amount of the membrane-supported bridge was determined.

[0008] Preferably, determining the proportion of structural coupling includes: Determine the energy consumption account weighting coefficients corresponding to the energy consumption account data; Determine the safety account weight coefficients corresponding to the safety account data; Based on the normalized energy consumption account data, the normalized safety account data, the energy consumption account weighting coefficient, and the safety account weighting coefficient, the shared burden data is determined; Based on the continuous wet driving amount and shared burden data of the thin-film bridge, the proportion of structural coupling is determined.

[0009] Preferably, the obtained bridge stripping and fusion rule base includes: The energy consumption account identifier corresponding to the energy consumption account data and the safety account identifier corresponding to the safety account data are identified as the bridge-related account identifier. The management account item identifier corresponding to the management account data is determined as an independent account item identifier; Based on the arrangement relationship between independent account identifiers and bridge-related account identifiers, multiple account reference sequences are generated; Configure the coupling processing flags for the account reference sequence; Configure the structure stripping marker for the account reference sequence; By combining the account reference sequence, coupling processing marker, and structure stripping marker, multiple candidate bridge stripping and fusion rules are obtained; The candidate bridge stripping and fusion rules are entered into the rule library to obtain the bridge stripping and fusion rule library.

[0010] Preferably, obtaining particle identification includes: Extract rule identifiers from the rule base of the bridge stripping and fusion; According to the rule identifier, the candidate bridge stripping and fusion rules are processed by particle mapping to obtain the particle identifier corresponding to the candidate bridge stripping and fusion rules.

[0011] Preferably, the resulting particle swarm includes: Configure the current position of the particle corresponding to the particle identifier; Configure the particle history preference record corresponding to the particle identifier; Based on rule identifiers, particle identifiers, current particle positions, and historical preferred particle records, particle data items are established. All particle data items are processed to obtain the particle swarm set.

[0012] Preferably, the rules for obtaining the bridging and fusion of the target performance object include: Based on the structural coupling ratio, the particle swarm particle set is iteratively updated to obtain the updated particle swarm particle set. The target particle is determined based on the updated particle swarm set; The bridge stripping and fusion rules corresponding to the target particles are determined as the bridge stripping and fusion rules for the target performance objects.

[0013] Preferably, performance evaluation of the target performance object includes: According to the bridge stripping and fusion rules of the target performance object, the normalized management account data, energy consumption account data and safety account data are rearranged to obtain the rearranged management account data, energy consumption account data and safety account data; The rearranged energy consumption data and safety data are coupled to obtain the coupling result. The coupling result is structurally stripped to obtain the stripped coupling result. The rearranged management ledger data is merged and aggregated with the decoupled coupling results to obtain the performance output value; Based on the performance output values, conduct performance evaluations on the target performance objects.

[0014] To address the aforementioned problems, this invention also provides a multi-dimensional KPI indicator quantification and performance evaluation system, the system comprising: The domain ledger mapping module performs structure domain mapping processing on the structure domain data of the target performance object based on the condensation reflux tooth of the target performance object, and obtains the structure domain ledger record; The driving calculation module determines the continuous humidification driving amount of the thin-film bridge based on the normalized humidity in the pre-tooth area, temperature in the tooth gap area, humidity in the tooth gap area, ventilation intensity in the tooth gap area, and humidity in the post-tooth area recorded in the structural domain ledger. The coupling ratio module combines the energy consumption data, safety data, and membrane hanging bridge continuous moisture drive amount from the business account data to generate the structural coupling ratio. The rule entry module generates multiple account reference sequences based on the energy consumption account identifier corresponding to the energy consumption account data, the security account identifier corresponding to the security account data, and the management account identifier corresponding to the management account data. It then enters the candidate bridge stripping and fusion rules corresponding to the account reference sequences into the rule library to obtain the bridge stripping and fusion rule library. The particle swarming module maps candidate bridge stripping and fusion rules to particles based on the bridge stripping and fusion rule library, obtains particle identifiers, and obtains a particle swarm set based on the particle identifiers. The iterative evaluation module, based on the proportion of structural coupling, performs iterative updates on the particle swarm set to obtain the bridging stripping and fusion rules for the target performance object, and then evaluates the performance of the target performance object.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, by spatially mapping the temperature, humidity and ventilation intensity of the internal flow guiding structure neighborhood of the equipment, a structural domain ledger record is constructed, and the formation and expansion capacity of liquid water film is further calculated. This allows for the quantitative expression of water vapor migration and condensation processes in the local humid and hot environment, making the structural influence originally hidden in the operating data explicitly separated. This avoids the mixed interference of environmental changes on the indicators and improves the analytical accuracy and consistency of multi-dimensional data.

[0016] 2. In this invention, by constructing a reference sequence based on account identifiers and combining the processing position and the stripping position to form a rule set, the correlation between energy consumption data and safety data can be expressed according to different structural influence conditions. By iteratively updating and filtering out rules consistent with the actual operating state, the structural influence part in synchronous fluctuation data can be identified and separated, avoiding misjudgment caused by multi-source superposition during the evaluation process, and improving the accuracy and stability of the evaluation results.

[0017] 3. In this invention, the stripped operational data and management data are uniformly aggregated to form a performance output value, and the output result is sorted and evaluated. This ensures that the evaluation criteria only reflect the actual operation and maintenance behavior and changes in operational status, eliminating the interference from structural environmental factors. This achieves an effective correspondence between the evaluation results and the actual management level, further improving the objectivity, comparability, and reliability of the performance evaluation results. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a multi-dimensional KPI indicator quantification and performance evaluation method provided in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] This embodiment provides a method for quantifying and evaluating multi-dimensional KPI indicators. (See also...) Figure 1 Specifically, this includes: S1. Based on the condensation reflux tooth of the target performance object, perform structural domain mapping processing on the structural domain data of the target performance object to obtain structural domain ledger records; In an embodiment of the present invention, obtaining the structure domain ledger record includes: Obtain the raw data of the target performance object; The target performance object refers to the energy storage battery compartment, energy storage converter cabinet, energy storage site, operation and maintenance team or management area that is subject to KPI quantification and performance evaluation; the raw data refers to the operation data, alarm data, energy consumption data and management data collected by the target performance object within the statistical period.

[0021] Collect energy consumption data, safety alarm data, operation and maintenance management data, temperature data, humidity data, and ventilation intensity data of the target performance object within the same statistical period. Generate original data records based on the target performance object number, data source, collection time, data type, and data value. Perform field integrity verification on the original data records and retain the data records that simultaneously contain the target performance object number, collection time, data type, and data value to obtain the original data of the target performance object.

[0022] Define the condensation reflux tooth of the target performance object; Condensation return teeth refer to toothed or stepped flow-guiding structures installed at the intersection or adjacent position of the supply air path, return air path and drainage path inside the target performance object. This structure is formed by sealing folds, flow-guiding edges or drainage groove edges, and its geometric shape is a continuous or discontinuous toothed boundary. When air flows in this area and there are temperature and humidity changes, condensation water is easily formed on the surface of the structure and adheres and migrates along the tooth edges, so that the condensation water forms a thin film state that crosses the gaps between adjacent teeth, thereby forming a return and moisture retention path on the surface of the structure.

[0023] A sealing fold refers to an edge structure formed by bending the edge of a material to prevent air or liquid leakage; a flow guide edge refers to a boundary structure used to change the direction of fluid flow; a drainage groove edge refers to the boundary part of a drainage groove, used to define the flow path of condensate; a toothed boundary refers to a boundary shape formed by multiple adjacent toothed units arranged continuously or discontinuously; a thin film state refers to the state in which liquid water covers a solid surface in a continuous and relatively thin layer; a return path refers to the path in which liquid flows in the opposite direction or circulates along the structural surface; and a continuous wetting path refers to the flow path in which liquid water is continuously distributed on the structural surface and maintains a wetted state.

[0024] Obtain the internal structural design documents and on-site survey data of the target performance object. The structural design documents include the layout of the air supply path, return air path, and drainage path, as well as the assembly information of the internal components. The on-site survey data reflects the actual direction of each path and the installation status of the components. In order to divide the air supply path, return air path, and drainage path, locate all intersections and adjacent positions of the three types of paths. At each location, identify the sealing folds formed by material bending, the guide edges that guide the flow of fluid, and the edges of the drainage channels that limit the flow of condensate. Then, analyze the overall geometric shape of the edge structure and screen out the structure formed by the continuous or discontinuous arrangement of multiple adjacent tooth-shaped units to form a toothed boundary as a candidate. The airflow state and condensate generation and migration process under normal operating conditions of the target performance object are simulated to verify whether a continuous thin layer of liquid water will form on the surface of the candidate structure, whether backflow deviating from the preset direction and a continuously wet flow path will occur, and finally the structure that meets the verification conditions is defined as the condensate backflow tooth.

[0025] The raw data is divided into business account data and structural domain data. The business account data includes energy consumption account data, safety account data and management account data, while the structural domain data includes data in the front area, back area and rear area of ​​the tooth arranged around the condensation return tooth. Business account data refers to the data set used to characterize the operational management and performance evaluation status of the target performance object; energy consumption account data refers to the energy consumption data generated by the electricity consumption of air conditioning, dehumidification, fans, and auxiliary equipment of the target performance object; safety account data refers to the safety status data generated by the humidity alarm, insulation alarm, and thermal management alarm of the target performance object; management account data refers to the operation and maintenance management data generated by the inspection, work order, and defect closure of the target performance object; structural domain data refers to the temperature, humidity, and ventilation intensity data collected around the space area where the condensate return tooth is located.

[0026] The data in the pre-tooth zone refers to the temperature, humidity, and ventilation intensity data of the area before the airflow reaches the condensation return tooth; the data in the gap zone refers to the temperature, humidity, and ventilation intensity data of the area between adjacent tooth edges, folded edges, and drainage edges of the condensation return tooth; and the data in the post-tooth zone refers to the temperature, humidity, and ventilation intensity data of the area after the airflow passes through the condensation return tooth.

[0027] The system reads the data source field, data type field, collection location field, and data value field from the original data records. Data records whose data type field indicates auxiliary power consumption, air conditioning power consumption, dehumidification power consumption, or fan power consumption are classified into energy consumption data. Data records whose data type field indicates humidity alarm, insulation alarm, or thermal management alarm are classified into safety data. Data records whose data type field indicates inspection, work order, or defect closure are classified into management data. The system matches the collection location field with the location coordinates, corresponding path relationships, and adjacent structure information in the definition results of the condenser return tooth. Data records located in the path area before the air reaches the condenser return tooth are classified into the tooth front area data. Data records located in the adjacent tooth edge, tooth gap, sealing fold, and drainage groove edge areas of the condenser return tooth are classified into the tooth gap area data. Data records located in the path area after the air passes through the condenser return tooth are classified into the tooth back area data, thus obtaining business account data and structure domain data.

[0028] Determine the supply air path, return air path, and drainage path for the target performance object; The air supply path refers to the flow path of air supplied by the air conditioning or ventilation equipment to the interior of the target performance object; the return air path refers to the flow path of air returning to the air conditioning or ventilation equipment after passing through the interior of the target performance object; the drainage path refers to the path of condensate flowing out along the guide edge, drainage channel or drainage outlet.

[0029] Obtain the locations of the air conditioning outlet, supply air outlet, return air outlet, duct boundary, condensate collection point, guide edge, drainage trough, and drain outlet inside the target performance object. Record the airflow path as the airflow route from the air conditioning outlet or supply air outlet into the target performance object and through the duct boundary. Record the airflow route as the return air route after passing through the target performance object and returning to the return air outlet. Record the liquid flow route of condensate from the condensate collection point through the guide edge and drainage trough to the drain outlet as the drainage path. Associate the supply air path, return air path, and drainage path with the target performance object number, the starting point of the path, the path passing through the location, and the ending point of the path, respectively.

[0030] Based on the spatial relationship of the condensate return teeth in the supply air path, return air path, and drainage path, structural domain data is processed by structural domain mapping to obtain structural domain ledger records.

[0031] Spatial positional relationship refers to the front-to-back, adjacent, and confluence relationships of the condenser return tooth relative to the supply air path, return air path, and drainage path; structural domain mapping processing refers to the processing of classifying structural domain data into the front tooth area, tooth gap area, and tooth rear area according to the correspondence between the condenser return tooth and the airflow path and drainage path; structural domain ledger recording refers to the corresponding data record set formed after classifying the collected temperature, humidity, and ventilation intensity data into the front tooth area, tooth gap area, and tooth rear area according to the spatial positional relationship of the condenser return tooth in the supply air path, return air path, and drainage path.

[0032] Acquire the definition results of the condenser return tooth, the supply air path, the return air path, the drainage path, and the structural domain data. Perform position mapping processing between the acquisition location in the structural domain data and the position coordinates, supply air path locations, return air path locations, and drainage path locations in the definition results of the condenser return tooth. Write the structural domain data corresponding to the path area before the air reaches the condenser return tooth into the tooth front area ledger record. Write the structural domain data corresponding to the adjacent tooth edges, tooth gaps, sealing folds, and drainage groove edge areas of the condenser return tooth into the tooth gap area ledger record. Write the structural domain data corresponding to the path area after the air passes through the condenser return tooth into the tooth rear area ledger record. Finally, merge the tooth front area ledger record, tooth gap area ledger record, and tooth rear area ledger record into a structural domain ledger record.

[0033] S2. Determine the continuous humidification drive quantity of the membrane bridge based on the normalized humidity in the pre-tooth region, the normalized temperature in the interdental region, the normalized humidity in the interdental region, the normalized ventilation intensity in the interdental region, and the normalized humidity in the post-tooth region recorded in the structural domain ledger. In embodiments of the present invention, determining the continuous wet driving amount of the thin-film bridge includes: Extract normalized humidity in the pre-tooth region, temperature in the interdental region, humidity in the interdental region, ventilation intensity in the interdental region, and humidity in the post-tooth region from the structural domain ledger records; Normalized pre-tooth humidity refers to the numerical expression of water vapor content in the air in the pre-tooth region after being converted to a uniform scale, reflecting the degree of humidity of the air before it enters the condensation return tooth; normalized gap temperature refers to the numerical expression of air temperature in the region adjacent to the condensation return tooth after being converted to a uniform scale, reflecting the thermal state of the condensation formation environment; normalized gap humidity refers to the numerical expression of water vapor content in the air in the region adjacent to the condensation return tooth after being converted to a uniform scale, reflecting the degree of humidity in the condensation occurrence area; normalized gap ventilation intensity refers to the numerical expression of airflow capacity in the region adjacent to the condensation return tooth after being converted to a uniform scale, reflecting the degree of influence of air on water vapor migration and condensate diffusion.

[0034] Read the pre-tooth zone ledger record, inter-tooth zone ledger record, and post-tooth zone ledger record from the structural domain ledger record. Obtain the normalized pre-tooth zone humidity based on the humidity field in the pre-tooth zone ledger record. Obtain the normalized inter-tooth zone temperature, normalized inter-tooth zone humidity, and normalized inter-tooth zone ventilation intensity based on the temperature, humidity, and ventilation intensity fields in the inter-tooth zone ledger record, respectively. Obtain the normalized post-tooth zone humidity based on the humidity field in the post-tooth zone ledger record. Then, associate and record the obtained data according to the target performance object number, structural domain name, and data type.

[0035] Based on the normalized interdental temperature and humidity, and according to the set dew point calculation rules, the interdental dew point data is determined. Dew point data in the tooth gap region refers to the temperature data corresponding to the saturation of water vapor in the air in the tooth gap region, and is used to characterize the critical temperature state at which air begins to condense.

[0036] Read the normalized interdental temperature and humidity from the structural domain ledger record. Use the normalized interdental humidity as the interdental relative humidity and the normalized interdental temperature as the interdental temperature. Calculate the intermediate value according to the dew point calculation formula: the intermediate value equals 17.27 multiplied by the interdental temperature, divided by the sum of 237.7 and the interdental temperature, and add the natural logarithm of the interdental relative humidity. Determine the interdental dew point data according to the dew point conversion formula: the interdental dew point data equals 237.7 multiplied by the intermediate value, divided by 17.27, minus the intermediate value. Then, associate and store the interdental dew point data with the target performance object number, the condensate reflux tooth identifier, and the interdental ledger record.

[0037] It should be noted that 17.27 and 237.7 are both constants used in the dew point conversion process to describe the relationship between changes in air saturated vapor pressure. 17.27 is used to characterize the degree of influence of temperature changes on the air's water vapor saturation capacity, while 237.7 is used to characterize the conversion scale between temperature and saturated vapor pressure. Both are derived from the empirical conversion law of dew point in the relationship between air heat and humidity. This conversion law can reflect the relationship between air reaching a saturated condensation state under different temperature and humidity conditions. Since the humid and hot air inside the energy storage battery compartment and the converter equipment cabinet also meets the air heat and humidity balance law, using the above constants to calculate the dew point data in the tooth gap area can ensure that the calculation results accurately reflect the temperature position when the air in the tooth gap area reaches a condensation state, thereby ensuring a correspondence between the dew point data in the tooth gap area and the actual condensation formation process.

[0038] It should be noted that the condensation process of water vapor in the air is determined by the combined effect of temperature and water vapor content. When the air temperature drops to a certain specific temperature, the water vapor in the air reaches a saturated state and begins to condense. This specific temperature is the dew point temperature. The dew point calculation rule is based on the correspondence between water vapor partial pressure and temperature. By introducing air temperature and relative humidity into a combination of logarithmic and temperature functions, the saturation condition position corresponding to the current water vapor state can be obtained. The calculation of intermediate quantities reflects the degree of deviation of the current water vapor partial pressure in the air from the saturated water vapor partial pressure. The subsequent conversion process maps this degree of deviation to the corresponding temperature value, thereby obtaining the condensation critical temperature that matches the current air water vapor content. Therefore, the result obtained according to this calculation process can accurately characterize the temperature state when the air in the gap region reaches the condensation condition, which is the dew point data of the gap region.

[0039] The difference between the normalized interdental temperature and the interdental dew point data is normalized to obtain dew point approximation data. Dew point approximation data refers to the numerical expression of the relationship between the current temperature of the tooth gap region and the dew point temperature after a unified scale transformation, which is used to reflect the degree to which air is close to condensation.

[0040] Read the normalized interdental temperature from the structural domain ledger record. Read the interdental dew point data associated with the same target performance object number, the same condensation reflux tooth identifier, and the same interdental ledger record. Subtract the interdental dew point data from the normalized interdental temperature to obtain the interdental temperature-dew difference data. Obtain the interdental temperature-dew difference data corresponding to each target performance object in the same evaluation set. Determine the maximum and minimum values ​​in the interdental temperature-dew difference data. Subtract the minimum value from each interdental temperature-dew difference data and divide by the first difference: Qt=Lx_max-Lx_min, where Lx_max and Lx_min are the maximum and minimum values ​​in the interdental temperature-dew difference data, respectively. Obtain the dew point approximation data for the corresponding target performance object. Associate the dew point approximation data with the target performance object number, condensation reflux tooth identifier, and interdental ledger record.

[0041] It should be noted that whether water vapor in the air condenses depends on the relationship between the current temperature and the dew point temperature. When the air temperature equals the dew point temperature, the water vapor reaches saturation and begins to condense. When the air temperature is higher than the dew point temperature, the water vapor remains unsaturated and does not condense. Therefore, the temperature-dew difference obtained by subtracting the dew point temperature of the interdental region from the interdental region temperature can directly reflect the degree of deviation of the air from the condensation condition. The smaller the temperature-dew difference, the closer it is to the condensation state; the larger the temperature-dew difference, the farther it is from the condensation state. Due to differences in temperature and humidity conditions among different target performance objects, the absolute range of this difference also varies. By normalizing the temperature-dew difference to a uniform scale, the degree of condensation proximity among different objects becomes comparable, thereby mapping the condensation trend under different environmental conditions to a continuous value within a uniform range. This continuous value can characterize the degree to which the air is close to condensation. Therefore, the result of this processing is the dew point approximation data.

[0042] Based on the normalized humidity in the back tooth region, the normalized humidity in the front tooth region, the normalized ventilation intensity in the interdental region, and the dew point approximation data, the continuous humidification driving amount of the membrane-supported bridge was determined.

[0043] The formula for calculating the continuous moisture driving amount of the thin-film bridge is as follows: ; In the formula, It is the continuous moisture driving quantity of the thin-film bridge. It is dew point approximation data. This is the normalized humidity in the post-dental region. This is the normalized predental humidity. It is the normalized ventilation intensity of the interdental area.

[0044] The driving force of continuous humidification of the thin film bridging refers to the comprehensive quantity formed by the combined effects of differences in air humidity distribution, airflow transport capacity, and the degree to which air approaches a condensation state in the area where the condensation return tooth is located. It is used to characterize the ability of water vapor to change from a gaseous state to a liquid state in this area and form a continuous liquid film along the structural surface. At the same time, it reflects the tendency of the liquid film to cross the gaps between adjacent tooth gaps and to continuously extend along the guide edge, fold edge, or drainage edge. The larger the quantity, the easier it is for water vapor to form a continuous thin film on the structural surface and achieve gap connection and extension, thus reflecting the degree of driving force for condensate to form a bridging and continuously expand in the tooth gap area.

[0045] It should be noted that the migration and condensation of water vapor in the air are simultaneously controlled by the water vapor concentration gradient, the airflow transport capacity, and the degree of air saturation. The absolute value of the difference between the normalized humidity in the back-tooth region and the normalized humidity in the front-tooth region corresponds to the water vapor concentration difference formed along the airflow path. This concentration difference determines the driving force for water vapor diffusion from high-humidity areas to low-humidity areas. The normalized ventilation intensity in the tooth gap region corresponds to the airflow transport capacity, which determines the efficiency with which water vapor is carried and spreads along the structural surface in the tooth gap region. Dew point approximation data reflects the current air temperature. The degree of proximity of the temperature to the dew point determines the likelihood of water vapor undergoing a phase change and forming a liquid film. The higher the degree of proximity of the dew point, the easier it is for water vapor to condense and adhere to the structural surface. Therefore, by multiplying and combining the dew point proximity data, the water vapor concentration difference, and the airflow transport capacity, we can simultaneously characterize the coupling effect of three processes: water vapor migration to the structural surface, condensation on the surface, and continuous expansion along the toothed structure. The combined result corresponds to the driving intensity of condensate forming a continuous film in the toothed region and forming a bridge across adjacent toothed areas and continuing to expand, thus obtaining the driving force of film bridge continuous moistening.

[0046] S3. Determine the structural coupling ratio based on the energy consumption data, safety data, and continuous wet drive of the thin-film bridge according to the business account data; In embodiments of the present invention, determining the structural coupling ratio includes: Determine the energy consumption account weighting coefficients corresponding to the energy consumption account data; Obtain the operation and maintenance assessment management documents on which this performance evaluation is based. The operation and maintenance assessment management documents contain the indicator importance allocation rules corresponding to different assessment levels. The assessment levels include equipment unit level, team level, site level and regional level. Match the corresponding assessment level according to the type of target performance object. Extract the relative importance ratio of energy consumption indicators to safety indicators from the indicator importance allocation rules of the assessment level. Convert the relative importance ratio into a continuous value in the range of 0 to 1 as the energy consumption account weight coefficient.

[0047] Determine the safety account weight coefficients corresponding to the safety account data; Read the energy consumption account weight coefficient associated with the current performance evaluation cycle identifier and the target performance object number. Calculate the safety account weight coefficient by subtracting the energy consumption account weight coefficient from the value 1. The value range of the safety account weight coefficient is automatically limited to the interval between 0 and 1. Associate and store the safety account weight coefficient with the same evaluation cycle identifier, the same target performance object number, and the corresponding energy consumption account weight coefficient.

[0048] The energy consumption account weighting coefficient refers to the proportion of energy consumption account data in the shared burden data; the safety account weighting coefficient refers to the proportion of safety account data in the shared burden data.

[0049] Based on the normalized energy consumption account data, the normalized safety account data, the energy consumption account weighting coefficient, and the safety account weighting coefficient, the shared burden data is determined; Shared burden data is used to characterize the operational burden of a target performance object under the combined effects of auxiliary energy consumption and safety status.

[0050] The formula for calculating shared burden data is as follows: ; In the formula, It is shared burden data. It is the energy consumption account weighting coefficient. This is normalized energy consumption data. It is the safety account weighting coefficient. It is the normalized security account data; The energy consumption account weighting coefficient is used to characterize the proportion of energy consumption account data in the shared burden data. Therefore, the value range of the energy consumption account weighting coefficient is set to be greater than or equal to 0 and less than or equal to 1. The safety account weighting coefficient is determined by subtracting the energy consumption account weighting coefficient from 1, so that the value range of the safety account weighting coefficient is also kept between greater than or equal to 0 and less than or equal to 1. This ensures that the total proportion of energy consumption account data and safety account data in the shared burden data is consistent, so that the shared burden data can simultaneously reflect the comprehensive impact of energy consumption changes and safety changes on the operating status of the target performance object.

[0051] It should be noted that the overall burden borne by the target performance object during operation can be decomposed into the burden caused by energy consumption and the burden caused by changes in safety status. The normalized energy consumption account data reflects the energy consumption level at a unit scale, and the normalized safety account data reflects the degree of safety risk impact at a unit scale. The two types of data are superimposed at a unified scale. The energy consumption account weight coefficient is used to characterize the contribution ratio of energy consumption to the overall burden, and the safety account weight coefficient is used to characterize the contribution ratio of safety risk to the overall burden. The sum of the weight coefficients maintains a complete expression of the overall contribution. By multiplying the energy consumption account data with its corresponding weight, the contribution of energy consumption to the overall burden is obtained. By multiplying the safety account data with its corresponding weight, the contribution of safety risk to the overall burden is obtained. The linear superposition of the two contribution amounts yields the total burden under a unified metric. This total burden includes information on both resource consumption and safety impact, thus forming a comprehensive expression of the operational burden of the target performance object. This comprehensive expression result is the shared burden data.

[0052] Based on the continuous wet driving amount and shared burden data of the thin-film bridge, the proportion of structural coupling is determined.

[0053] The structural coupling ratio is used to characterize the degree of influence of the continued moisture drive formed in the condensation reflux tooth region on the joint changes in energy consumption account data and safety account data.

[0054] Read the continuous moisture driving quantity and shared burden data of the membrane hanging bridge corresponding to the same target performance object. Perform dot product processing on the continuous moisture driving quantity and shared burden data to obtain the coupling dot product data. Accumulate the square values ​​of each data item in the continuous moisture driving quantity of the membrane hanging bridge and take the square root to obtain the modulus data of the continuous moisture driving quantity of the membrane hanging bridge. Accumulate the square values ​​of each data item in the shared burden data and take the square root to obtain the modulus data of the shared burden data. Multiply the modulus data of the continuous moisture driving quantity of the membrane hanging bridge and the modulus data of the shared burden data to obtain the modulus product data. Divide the coupling dot product data by the modulus product data to obtain the structural coupling ratio.

[0055] It should be noted that the continuous humidification driving force of the thin-film bridge reflects the driving intensity distribution formed by water vapor migration and condensation processes, while the shared burden data reflects the burden distribution of the target performance object under the combined effects of energy consumption and safety impact. Both can be regarded as changes on the same time series or the same set of objects. By performing dot product processing on the two, we can obtain the cumulative synchronous changes of the two at their corresponding positions. The larger the cumulative amount, the more consistent the direction and magnitude of the changes. By squaring and summing the continuous humidification driving force of the thin-film bridge and the shared burden data respectively and taking the square root, we obtain their respective modulus, which is used to characterize the overall scale of their respective change intensity. Dividing the dot product result by the modulus product is equivalent to normalizing the degree of synchronous change, so that the result is not affected by their absolute magnitude, but only retains the degree of consistency of the change direction. Thus, we obtain a ratio value that characterizes the degree of correlation between the two changes. The larger the ratio value, the stronger the correlation between the continuous humidification driving force of the thin-film bridge and the shared burden change, and the better it reflects the coupling relationship caused by structural action. Therefore, this calculation result is the structural coupling ratio.

[0056] S4. Based on the energy consumption account identifier corresponding to the energy consumption account data, the security account identifier corresponding to the security account data, and the management account identifier corresponding to the management account data, generate multiple account reference sequences, and put the candidate bridge stripping and fusion rules corresponding to the account reference sequences into the rule library to obtain the bridge stripping and fusion rule library. In an embodiment of the present invention, a bridge stripping and fusion rule base is obtained, including: The energy consumption account identifier corresponding to the energy consumption account data and the safety account identifier corresponding to the safety account data are identified as the bridge-related account identifier. Energy consumption item identifier refers to the data marker used to indicate the source, type and attribution of each energy consumption data item in the energy consumption account data; safety item identifier refers to the data marker used to indicate the source, type and attribution of each safety data item in the safety account data; bridge-related item identifier refers to the data marker composed of energy consumption item identifier and safety item identifier, used to indicate the scope of items related to the continued moisture effect of the condensate return tooth area.

[0057] Read the data item source field, data item type field, data item ownership field, and energy consumption account item identifier from the energy consumption account data. Read the data item source field, data item type field, data item ownership field, and safety account item identifier from the safety account data. Establish an association record between the energy consumption account item identifier and the corresponding energy consumption account data field. Establish an association record between the safety account item identifier and the corresponding safety account data field. Write the associated energy consumption account item identifier and the associated safety account item identifier into the same bridge-related account item record to obtain the bridge-related account item identifier.

[0058] The management account item identifier corresponding to the management account data is determined as an independent account item identifier; Management item identifiers refer to data tags used to indicate the source, type, and affiliation of each management data item in the management account data; independent item identifiers refer to data tags formed by management item identifiers, used to indicate the scope of account items that do not directly participate in the coupled processing of energy consumption account data and safety account data.

[0059] Read the data item source field, data item type field, data item ownership field, and management account item identifier from the management account data. Establish a related record between the management account item identifier and the corresponding management account data field. Write the related management account item identifier into the independent account record to obtain the independent account item identifier.

[0060] Based on the arrangement relationship between independent account identifiers and bridge-related account identifiers, multiple account reference sequences are generated; Read the independent account identifier and the bridge-related account identifier. Use the independent account identifier as the reference object for management account data, and use the bridge-related account identifier as the reference object for energy consumption account data and safety account data. Generate an account reference sequence record according to the arrangement relationship of the independent account identifier before the bridge-related account identifier, the independent account identifier after the bridge-related account identifier, and the independent account identifier between the energy consumption account identifier and the safety account identifier. Associate each account reference sequence record with the target performance object number, statistical period, independent account identifier and bridge-related account identifier to obtain multiple account reference sequences.

[0061] Configure the coupling processing flags for the account reference sequence; Read the energy consumption account identifier and safety account identifier in each account reference sequence, record the adjacent position, interval position and containment position of the energy consumption account identifier and safety account identifier in the account reference sequence as coupling processing positions, establish an association record between the coupling processing position and the corresponding account reference sequence, and write the association record into the account reference sequence to obtain the account reference sequence after configuring the coupling processing mark.

[0062] Configure the structure stripping marker for the account reference sequence; The item reference sequence refers to the data reference order formed according to the arrangement relationship between the independent item identifier and the bridge-related item identifier; the coupling processing mark refers to the position mark used to indicate that the energy consumption item and the safety item are combined in the item reference sequence; the structure stripping mark refers to the position mark used to indicate that the data part of the bridge-related item caused by the continued moisture effect of the condensation return tooth is stripped.

[0063] Read the account reference sequence after configuration coupling processing mark, read the bridge-related account identifier in the account reference sequence, record the position corresponding to the energy consumption account identifier, the position corresponding to the safety account identifier, and the position jointly corresponding to the energy consumption account identifier and the safety account identifier in the bridge-related account identifier as the structure stripping position, establish an association record between the structure stripping position and the corresponding account reference sequence, and write the association record into the account reference sequence to obtain the account reference sequence after configuration structure stripping mark.

[0064] By combining the account reference sequence, coupling processing marker, and structure stripping marker, multiple candidate bridge stripping and fusion rules are obtained; Read the account reference sequence after the configuration coupling processing mark and the account reference sequence after the configuration structure stripping mark. Write the account reference sequence, coupling processing mark and structure stripping mark corresponding to the same target performance object number, the same statistical period and the same account reference sequence number into the same rule record. Use the account reference order, coupling processing position and structure stripping position in the rule record as rule content items respectively, and configure the rule identifier for the rule record to obtain the candidate bridge stripping and fusion rule.

[0065] The candidate bridge stripping and fusion rules are entered into the rule library to obtain the bridge stripping and fusion rule library.

[0066] The bridge stripping and fusion rule base refers to a data set that stores multiple candidate bridge stripping and fusion rules.

[0067] The candidate bridge stripping and fusion rule refers to the rule expression used to describe the interaction between various operating data during the migration, condensation, and expansion along the structural surface of water vapor in the condensation return tooth region. This rule reflects the coupling mode of energy consumption change and safety status change during the continuous wetting process. At the same time, it reflects the need to separate some data correlations when condensate forms a continuous liquid film between the tooth gaps and occurs cross-gap connection. This rule is used to characterize the influence of water vapor on the distribution of operating load when it transforms from gaseous to liquid state and expands along the structural surface under the combined action of airflow transport, humidity gradient, and near-condensation conditions.

[0068] Read multiple candidate bridge stripping and fusion rules, verify whether each candidate bridge stripping and fusion rule contains a rule identifier, account reference order, coupling processing position, and structure stripping position, write the candidate bridge stripping and fusion rules containing the above content into the rule storage record, index and arrange the rule storage record according to the target performance object number, statistical period, and rule identifier, and summarize the indexed and arranged rule storage records to obtain the bridge stripping and fusion rule library.

[0069] S5. Based on the bridge stripping and fusion rule base, perform particle mapping on the candidate bridge stripping and fusion rules to obtain particle identifiers, and obtain the particle swarm set based on the particle identifiers. In an embodiment of the present invention, obtaining particle identification includes: Extract rule identifiers from the rule base of the bridge stripping and fusion; Read the rule storage records corresponding to each candidate bridge stripping and fusion rule in the bridge stripping and fusion rule base, read the rule identifier field in each rule storage record, and use the read rule identifier field as the rule identifier of the candidate bridge stripping and fusion rule.

[0070] According to the rule identifier, the candidate bridge stripping and fusion rules are processed by particle mapping to obtain the particle identifier corresponding to the candidate bridge stripping and fusion rules.

[0071] Read the rule identifier, locate the corresponding candidate bridge stripping and fusion rule in the bridge stripping and fusion rule library according to the rule identifier, treat the candidate bridge stripping and fusion rule as a particle object in the particle swarm iteration, configure a particle identifier field for the particle object, establish a correspondence between the particle identifier field and the rule identifier, and obtain the particle identifier corresponding to the candidate bridge stripping and fusion rule.

[0072] In embodiments of the present invention, a particle swarm particle set is obtained, including: Configure the current position of the particle corresponding to the particle identifier; Read the rule identifier corresponding to the particle identifier, obtain the account reference order, coupling processing position and structure stripping position in the candidate bridge stripping and fusion rule corresponding to the rule identifier, write the record position of the account reference order, the record position of the coupling processing position, and the record position of the structure stripping position in the candidate bridge stripping and fusion rule into the same current position record, associate the current position record with the particle identifier, and obtain the current position of the particle corresponding to the particle identifier.

[0073] Configure the particle history preference record corresponding to the particle identifier; The current position of a particle refers to the current search state record of the candidate bridge stripping and fusion rule corresponding to the particle identifier during the particle swarm iteration process; the particle history optimization record refers to the optimization state record formed by the candidate bridge stripping and fusion rule corresponding to the particle identifier during the completed iteration process.

[0074] Read the particle identifier, the rule identifier corresponding to the particle identifier, the candidate bridge stripping and fusion rule corresponding to the rule identifier, and the structural coupling ratio obtained in step three. Extract the account reference order, coupling processing position, and structural stripping position from the candidate bridge stripping and fusion rules. Adapt and record the structural stripping position in the candidate bridge stripping and fusion rules according to the structural coupling ratio. Write the adapted structural stripping position, account reference order, and coupling processing position into the historical optimization record. Establish a correspondence between the historical optimization record and the particle identifier, rule identifier, and structural coupling ratio to obtain the particle historical optimization record corresponding to the particle identifier.

[0075] Based on rule identifiers, particle identifiers, current particle positions, and historical preferred particle records, particle data items are established. Read the rule identifier, particle identifier, current particle position, and historical preferred particle record corresponding to the same candidate bridge stripping and fusion rule. Write the rule identifier into the rule field of the particle data item, write the particle identifier into the particle field of the particle data item, write the current particle position into the position field of the particle data item, and write the historical preferred particle record into the preferred particle field of the particle data item. Merge the rule field, particle field, position field, and preferred particle field into a complete particle record according to the correspondence of the same candidate bridge stripping and fusion rule to obtain the particle data item.

[0076] All particle data items are processed to obtain the particle swarm set.

[0077] A particle data item refers to a data record consisting of a rule identifier, a particle identifier, the particle's current position, and a particle's historical preferred record; a particle swarm or particle set refers to a data set formed by summarizing all particle data items.

[0078] Read all particle data items, group them into the same particle swarm record according to the target performance object number and statistical period, retain the rule field, particle field, position field and preferred field of each particle data item in the particle swarm record, establish the association relationship between the particle swarm record and the target performance object number, statistical period and the bridge stripping and fusion rule base, and obtain the particle swarm particle set.

[0079] S6. Based on the structural coupling ratio, the particle swarm is iteratively updated to obtain the bridge stripping and fusion rules of the target performance object, and the performance of the target performance object is evaluated.

[0080] In an embodiment of the present invention, the bridge stripping and fusion rules for the target performance object are obtained, including: Based on the structural coupling ratio, the particle swarm particle set is iteratively updated to obtain the updated particle swarm particle set. Each particle data item in the particle swarm particle set is taken as the particle data item to be updated. The current iteration round is set as the starting round. For each particle data item to be updated, the corresponding candidate bridge stripping and fusion rule is determined according to the rule identifier in the particle data item to be updated. The account reference order, coupling processing position and structural stripping position are determined from the candidate bridge stripping and fusion rule. The normalized management account data, normalized energy consumption account data and normalized safety account data are arranged into the corresponding account positions according to the account reference order. The arranged energy consumption account data and safety account data are assigned to the same coupling record according to the coupling processing position. The data position corresponding to the condensation reflux tooth continued moisture effect is marked in the coupling record according to the structural stripping position. The stripping adaptation record corresponding to the particle data item to be updated is obtained. The stripping adaptation record is matched with the structural coupling ratio to form the current round adaptation result of the particle data item to be updated. Based on the adaptation results of this round, adjust the current position of the particles in the particle data items to be updated, so that the arrangement position, coupling processing position, structure stripping position, and rule index position of the current particle position correspond to the adaptation results of this round. Write the adjusted current particle position into the particle data item to be updated, and compare and update the adjusted current particle position with the particle historical optimization record to obtain the particle data item updated in this round. After completing the update of all particle data items to be updated in this round in the particle swarm particle set, compare the current iteration round with the particle swarm iteration count. If the current iteration round has not reached the particle swarm iteration count, use the particle data item updated in this round as the particle data item to be updated in the next round, and increment the current iteration round and continue to execute the above update process. When the current iteration round reaches the particle swarm iteration count, stop the iteration, and summarize all particle data items obtained in the current round into the updated particle swarm particle set.

[0081] The target particle is determined based on the updated particle swarm set; For each updated particle data item, the particle identifier, rule identifier, updated current particle position, and particle history preferred record are read. Based on the rule identifier, the corresponding candidate bridge stripping and fusion rule is extracted from the bridge stripping and fusion rule library. The account reference order, coupling processing position, and structural stripping position in the candidate bridge stripping and fusion rule are read. Normalized management account data, normalized energy consumption account data, and normalized safety account data are retrieved according to the account reference order. The coupling aggregation result of energy consumption account data and safety account data is formed according to the coupling processing position. According to the structural stripping position, the stripping object record corresponding to the structural coupling ratio is extracted from the coupling aggregation result. The stripping object record is verified to correspond with the updated current particle position. Particle data items that can completely correspond to the rule identifier, account reference order, coupling processing position, structural stripping position, stripping object record, and updated current particle position are determined as optional particle data items. Among the optional particle data items, the particle data item with the same particle history preferred record and updated current particle position is selected, and the particle identifier corresponding to the particle data item is determined as the target particle.

[0082] The bridge stripping and fusion rules corresponding to the target particles are determined as the bridge stripping and fusion rules for the target performance objects.

[0083] It should be noted that the particle swarm iteration update process is constrained by the proportion of structural coupling, so that each candidate bridge stripping and fusion rule gradually conforms to the actual coupling relationship formed by water vapor migration, condensation and continued moisture expansion in the condensation reflux tooth region during continuous updates. The particle data item corresponding to the target particle can simultaneously maintain consistency with the changes in operating burden reflected by the proportion of structural coupling in terms of account item reference order, coupling processing position and structural stripping position. This indicates that the bridge stripping and fusion rule corresponding to the particle can fully express the actual correlation between energy consumption change and safety status change under the driving effect of continued moisture. Therefore, determining the bridge stripping and fusion rule corresponding to the target particle as the bridge stripping and fusion rule of the target performance object can ensure that the subsequent performance evaluation process is based on the rule that best conforms to the actual structural interaction relationship.

[0084] In an embodiment of the present invention, performance evaluation of the target performance object includes: According to the bridge stripping and fusion rules of the target performance object, the normalized management account data, energy consumption account data and safety account data are rearranged to obtain the rearranged management account data, energy consumption account data and safety account data; The rearranged management account data refers to the management account data after adjusting the reference order according to the bridge stripping and fusion rules of the target performance object; the rearranged energy consumption account data refers to the energy consumption account data after adjusting the reference order according to the bridge stripping and fusion rules of the target performance object; the rearranged safety account data refers to the safety account data after adjusting the reference order according to the bridge stripping and fusion rules of the target performance object.

[0085] Based on the account reference order in the bridge stripping and fusion rules, the order of the management account identifier, energy consumption account identifier, and safety account identifier in the fusion process is determined. The data items corresponding to the management account identifier in the normalized management account data are written to the reference position corresponding to the management account identifier. The data items corresponding to the energy consumption account identifier in the normalized energy consumption account data are written to the reference position corresponding to the energy consumption account identifier. The data items corresponding to the safety account identifier in the normalized safety account data are written to the reference position corresponding to the safety account identifier. The target performance object number, statistical period, account identifier, data type, and normalized data value are retained for the data items that have been written, forming the rearranged management account data, rearranged energy consumption account data, and rearranged safety account data.

[0086] The rearranged energy consumption data is coupled with the safety data to obtain the coupling result. The coupling result refers to the correlation response data formed between changes in energy consumption and changes in safety status under the combined effects of water vapor migration, condensation, and airflow transport within the condensation reflux tooth region.

[0087] Based on the coupling processing position in the bridge stripping and fusion rules, the correspondence between the energy consumption account identifier and the safety account identifier in the account reference order is determined. The energy consumption account identifier in the correspondence is located to the data item in the rearranged energy consumption account data, and the safety account identifier in the correspondence is located to the data item in the rearranged safety account data. The located energy consumption account data item and safety account data item are grouped into the same coupling record. The target performance object number, statistical period, energy consumption account identifier, safety account identifier, energy consumption account normalized data value, safety account normalized data value, coupling processing position, and corresponding bridge stripping and fusion rule identifier are recorded in the coupling record. All coupling records are summarized according to the bridge stripping and fusion rule identifier to obtain the coupling result.

[0088] The coupling result is structurally stripped to obtain the stripped coupling result. The post-coupling result refers to the remaining correlation data after removing the influence of the continuous liquid film formed by condensate between the tooth gaps and the resulting cross-gap conduction in the above-mentioned correlation response. This data reflects the operational relationship that is not affected by the continuous wet expansion of the structure.

[0089] Based on the structural stripping location in the bridge stripping and fusion rules of the target performance object, determine the energy consumption and safety items corresponding to the tooth gap moisture-borrowing bridging in the coupling result. Use the normalized energy consumption data value, the normalized safety data value, and the structural coupling ratio within the same coupling record in the coupling result as the data basis for structural stripping processing. Determine the proportion of data in the coupling record belonging to the condensation return tooth continuous moisture effect based on the structural coupling ratio. Separate the portion of the normalized energy consumption data value corresponding to the condensation return tooth continuous moisture effect from the normalized energy consumption data value, and separate the portion of the normalized safety data value corresponding to the condensation return tooth continuous moisture effect from the normalized safety data value. Retain the remaining energy consumption data value and the remaining safety data value after separation. Write the energy consumption item identifier, safety item identifier, remaining energy consumption data value, remaining safety data value, structural stripping location, structural coupling ratio, target performance object number, and statistical period into the same stripping record to obtain the coupling result after stripping.

[0090] The rearranged management ledger data is merged and aggregated with the decoupled coupling results to obtain the performance output value; Based on the target performance object number and statistical period, the rearranged management account data is correlated with the decoupled coupling results. The normalized management account data value in the rearranged management account data is used as the management contribution data. The remaining data values ​​of energy consumption account and safety account in the decoupled coupling results are used as the decoupled operation contribution data. The management contribution data and the decoupled operation contribution data are included in the same performance aggregation record. The management contribution data, remaining data values ​​of energy consumption account and remaining data values ​​of safety account in the performance aggregation record are aggregated according to the account reference order in the bridge decoupling and fusion rules to form a single performance expression data of the target performance object in the statistical period. This single performance expression data is determined as the performance output value.

[0091] Based on the performance output values, conduct performance evaluations on the target performance objects.

[0092] Performance output value refers to the data results that comprehensively reflect the overall operating status of the target performance object under the combined effects of energy consumption, changes in environmental humidity, and structural airflow.

[0093] Obtain the performance output value corresponding to the target performance object, establish a record linking the performance output value with the target performance object number, statistical period, and performance evaluation item, sort the performance output values ​​corresponding to each target performance object within the same statistical period according to their numerical values, obtain the performance ranking result, write the ranking position, performance output value, target performance object number, and statistical period of each target performance object in the performance ranking result into the performance evaluation record, generate the performance evaluation result corresponding to the target performance object based on the performance evaluation record, and use the performance evaluation result for the performance ranking, assessment archiving, and determination of the rectification order of the target performance object.

[0094] Within the energy storage battery compartment, the continuous water film conduction across the tooth gaps, formed by the combined action of the toothed flow guide structure and the humid and hot airflow, causes synchronous changes in energy consumption and safety indicators. This change does not originate from the level of operation and maintenance management, but is caused by the humidity migration path within the structure. If this impact is not separated, the structural influence may be misjudged as fluctuations in management performance, leading to distorted assessment results. The performance output value obtained through processing has separated the impact of this type of water film conduction across the tooth gaps from the original indicators, allowing it to reflect only the management contribution and operational status of the target performance object in the actual operation and maintenance process. Therefore, it is necessary to conduct performance evaluation of the target performance object based on this performance output value to ensure that the evaluation results can truly reflect the level of management performance and avoid interference from structural factors.

[0095] It should be noted that the phenomenon of continuous water film conduction across tooth gaps refers to the process in which, within a space with a toothed flow guiding structure, condensate forms a continuous film on the surface of the structure, and under the combined influence of humidity gradient, airflow, and surface adhesion, it crosses and connects with adjacent tooth gaps, so that the originally separated tooth gap regions form a connected channel through the liquid water film, thereby allowing water to continuously migrate and expand between multiple tooth gaps.

[0096] An embodiment of the present invention also provides a multi-dimensional KPI indicator quantification and performance evaluation system; in this embodiment, the functions of each module / unit are as follows: The domain ledger mapping module performs structure domain mapping processing on the structure domain data of the target performance object based on the condensation reflux tooth of the target performance object, and obtains the structure domain ledger record; The driving calculation module determines the continuous humidification driving amount of the thin-film bridge based on the normalized humidity in the pre-tooth area, temperature in the tooth gap area, humidity in the tooth gap area, ventilation intensity in the tooth gap area, and humidity in the post-tooth area recorded in the structural domain ledger. The coupling ratio module combines the energy consumption data, safety data, and membrane bridge continuous moisture drive quantity from the business account data to generate the structural coupling ratio. The rule entry module generates multiple account reference sequences based on the energy consumption account identifier corresponding to the energy consumption account data, the security account identifier corresponding to the security account data, and the management account identifier corresponding to the management account data. It then enters the candidate bridge stripping and fusion rules corresponding to the account reference sequences into the rule library to obtain the bridge stripping and fusion rule library. The particle swarming module maps candidate bridge stripping and fusion rules to particles based on the bridge stripping and fusion rule library, obtains particle identifiers, and obtains a particle swarm set based on the particle identifiers. The iterative evaluation module, based on the proportion of structural coupling, performs iterative updates on the particle swarm set to obtain the bridge stripping and fusion rules for the target performance object, and then evaluates the performance of the target performance object.

[0097] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-dimensional KPI indicator quantification and performance evaluation method, characterized in that, The steps include: Based on the condensation reflux tooth of the target performance object, perform structural domain mapping processing on the structural domain data of the target performance object to obtain structural domain ledger records; Based on the normalized humidity in the pre-tooth region, temperature in the inter-tooth region, humidity in the inter-tooth region, ventilation intensity in the inter-tooth region, and humidity in the post-tooth region recorded in the structural domain ledger, the continuous humidification drive of the thin-film bridge is determined. By combining the energy consumption data, safety data, and continuous moisture drive of the thin-film hanging bridge from the business account data, the structural coupling ratio is generated; Based on the energy consumption account identifier corresponding to the energy consumption account data, the safety account identifier corresponding to the safety account data, and the management account identifier corresponding to the management account data, multiple account reference sequences are generated, and the candidate bridge stripping and fusion rules corresponding to the account reference sequences are stored in the rule library to obtain the bridge stripping and fusion rule library. Based on the bridge stripping and fusion rule base, the candidate bridge stripping and fusion rules are mapped to obtain particle identifiers, and the particle swarm set is obtained based on the particle identifiers. Based on the structural coupling ratio, the particle swarm is iteratively updated to obtain the bridge stripping and fusion rules for the target performance object, and the performance of the target performance object is evaluated.

2. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 1, characterized in that, The structure domain ledger records are obtained, including: Obtain the raw data of the target performance object; Define the condensation reflux tooth of the target performance object; The raw data is divided into business account data and structural domain data. The business account data includes energy consumption account data, safety account data and management account data, while the structural domain data includes data in the front area, back area and rear area of ​​the tooth arranged around the condensation return tooth. Determine the supply air path, return air path, and drainage path for the target performance object; Based on the spatial relationship of the condensate return teeth in the supply air path, return air path, and drainage path, structural domain data is processed by structural domain mapping to obtain structural domain ledger records.

3. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 2, characterized in that, Determine the continuous moisture drive amount of the thin-film bridge, including: Extract normalized humidity in the pre-tooth region, temperature in the interdental region, humidity in the interdental region, ventilation intensity in the interdental region, and humidity in the post-tooth region from the structural domain ledger records; Based on the normalized interdental temperature and humidity, and according to the set dew point calculation rules, the interdental dew point data is determined. The difference between the normalized interdental temperature and the interdental dew point data is normalized to obtain dew point approximation data. Based on the normalized humidity in the back tooth region, the normalized humidity in the front tooth region, the normalized ventilation intensity in the interdental region, and the dew point approximation data, the continuous humidification driving amount of the membrane-supported bridge was determined.

4. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 3, characterized in that, Determine the proportion of structural coupling, including: Determine the energy consumption account weighting coefficients corresponding to the energy consumption account data; Determine the safety account weight coefficients corresponding to the safety account data; Based on the normalized energy consumption account data, the normalized safety account data, the energy consumption account weighting coefficient, and the safety account weighting coefficient, the shared burden data is determined; Based on the continuous wet driving amount and shared burden data of the thin-film bridge, the proportion of structural coupling is determined.

5. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 1, characterized in that, The obtained bridge stripping and fusion rule base includes: The energy consumption account identifier corresponding to the energy consumption account data and the safety account identifier corresponding to the safety account data are identified as the bridge-related account identifier. The management account item identifier corresponding to the management account data is determined as an independent account item identifier; Based on the arrangement relationship between independent account identifiers and bridge-related account identifiers, multiple account reference sequences are generated; Configure the coupling processing flags for the account reference sequence; Configure the structure stripping marker for the account reference sequence; By combining the account reference sequence, coupling processing marker, and structure stripping marker, multiple candidate bridge stripping and fusion rules are obtained; The candidate bridge stripping and fusion rules are entered into the rule library to obtain the bridge stripping and fusion rule library.

6. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 1, characterized in that, The particle identifiers are obtained, including: Extract rule identifiers from the rule base of the bridge stripping and fusion; According to the rule identifier, the candidate bridge stripping and fusion rules are processed by particle mapping to obtain the particle identifier corresponding to the candidate bridge stripping and fusion rules.

7. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 6, characterized in that, The particle swarm is obtained, including: Configure the current position of the particle corresponding to the particle identifier; Configure the particle history preference record corresponding to the particle identifier; Based on rule identifiers, particle identifiers, current particle positions, and historical preferred particle records, particle data items are established. All particle data items are processed to obtain the particle swarm set.

8. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 1, characterized in that, The rules for bridging, stripping, and merging the target performance object are obtained, including: Based on the structural coupling ratio, the particle swarm particle set is iteratively updated to obtain the updated particle swarm particle set. The target particle is determined based on the updated particle swarm set; The bridge stripping and fusion rules corresponding to the target particles are determined as the bridge stripping and fusion rules for the target performance objects.

9. The method for quantifying and evaluating multi-dimensional KPI indicators according to claim 2, characterized in that, Performance evaluation of the target performance subjects includes: According to the bridge stripping and fusion rules of the target performance object, the normalized management account data, energy consumption account data and safety account data are rearranged to obtain the rearranged management account data, energy consumption account data and safety account data; The rearranged energy consumption data and safety data are coupled to obtain the coupling result. The coupling result is structurally stripped to obtain the stripped coupling result. The rearranged management ledger data is merged and aggregated with the decoupled coupling results to obtain the performance output value; Based on the performance output values, conduct performance evaluations on the target performance objects.

10. A system for applying the multi-dimensional KPI indicator quantification and performance evaluation method according to any one of claims 1-9, characterized in that, The system includes: The domain ledger mapping module performs structure domain mapping processing on the structure domain data of the target performance object based on the condensation reflux tooth of the target performance object, and obtains the structure domain ledger record; The driving calculation module determines the continuous humidification driving amount of the thin-film bridge based on the normalized humidity in the pre-tooth area, temperature in the tooth gap area, humidity in the tooth gap area, ventilation intensity in the tooth gap area, and humidity in the post-tooth area recorded in the structural domain ledger. The coupling ratio module combines the energy consumption data, safety data, and membrane hanging bridge continuous moisture drive amount from the business account data to generate the structural coupling ratio. The rule entry module generates multiple account reference sequences based on the energy consumption account identifier corresponding to the energy consumption account data, the security account identifier corresponding to the security account data, and the management account identifier corresponding to the management account data. It then enters the candidate bridge stripping and fusion rules corresponding to the account reference sequences into the rule library to obtain the bridge stripping and fusion rule library. The particle swarming module maps candidate bridge stripping and fusion rules to particles based on the bridge stripping and fusion rule library, obtains particle identifiers, and obtains a particle swarm set based on the particle identifiers. The iterative evaluation module, based on the proportion of structural coupling, performs iterative updates on the particle swarm set to obtain the bridging stripping and fusion rules for the target performance object, and then evaluates the performance of the target performance object.

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