Digital operation and maintenance management system and method based on BIM model

Abstract

The invention discloses a BIM model-based digital operation and maintenance management system and method, and relates to the technical field of intelligent control. A BIM integrated model is constructed based on park layout data, historical operation data and historical operation state data of each sewage treatment facility are collected, and an energy consumption model function is fitted for the sewage treatment facility based on the historical operation data; based on the historical operation data and the historical operation state data, fitting out an efficiency model function, collecting real-time energy price information, collecting operation state data of the sewage treatment facility in real time, comparing the current operation state data with the operation state data of the last period, and if a change value is greater than a preset change threshold value, determining that the sewage treatment facility is in a next period; based on the difference of the operation state data, combining an energy consumption model function, an efficiency model function and energy price information to generate a new equipment regulation and control scheme; highly integrated and intelligent sewage treatment facility operation and maintenance management is realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a digital operation and maintenance management system and method based on a BIM model. Background Art

[0002] The sewage treatment facilities in the park are key infrastructure to ensure the operation and environmental health of the park. These facilities usually include multiple process units such as aeration tanks, sedimentation tanks, and disinfection equipment, which consume a lot of electricity, chemicals and other energy to drive operation. With the continuous improvement of environmental protection requirements and the rise in energy costs, how to minimize energy consumption while ensuring the sewage treatment effect has become an important issue that park operators need to solve urgently.

[0003] There are many deficiencies in the operation and maintenance management of traditional sewage treatment facilities. On the one hand, operation and maintenance personnel often formulate operation scheduling plans based on experience and standard process parameters, which makes it difficult to fully consider the actual operation status and energy consumption. On the other hand, the lack of comprehensive monitoring and refined regulation of the operation process of facilities and equipment cannot ensure that the system is in the optimal operating state. In addition, due to the lack of advanced modeling and optimization methods, it is impossible to integrate multi-dimensional influencing factors and formulate a truly comprehensive optimal scheduling strategy.

[0004] A Chinese patent with authorization announcement number CN101833314B discloses a sewage treatment control system and a sewage treatment control method, including an operation station computer for collecting water quality index data of a sewage regulating tank inlet and sending it to a data analysis station computer; a data analysis station computer for measuring dissolved oxygen DO of the index data according to a genetic neural network GABP, and outputting the measured DO to a programmable controller PLC master station and a remote PLC station; a programmable controller PLC master station and a remote PLC station for optimizing PID parameters according to a genetic algorithm, and then controlling the measured DO according to the optimized PID parameters. However, this solution fails to consider the relationship between the global processing energy consumption and equipment control parameters in the park.

[0005] To this end, the present invention proposes a digital operation and maintenance management system and method based on the BIM model. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a digital operation and maintenance management system and method based on the BIM model, which realizes highly integrated and intelligent operation and maintenance management of sewage treatment facilities.

[0007] To achieve the above objectives, a digital operation and maintenance management method based on BIM model is proposed, which includes the following steps:

[0008] Step 1: Collect park layout data and build a BIM integrated model based on the park layout data;

[0009] Step 2: Collect the historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model, fit the energy consumption model function for the sewage treatment facility based on the historical operation data, and fit the efficiency model function based on the historical operation data and historical operation status data;

[0010] Step 3: Collect real-time energy price information;

[0011] Step 4: Collect the operating status data of the sewage treatment facility in real time, compare the current operating status data with the operating status data of the previous cycle, and if the change value is greater than the preset change threshold, go to step 5, otherwise maintain the current control parameters;

[0012] Step 5: Generate a new equipment control plan based on the differences in operating status data, combined with energy consumption model function, efficiency model function and energy price information.

[0013] The collecting of the park layout data and building the BIM integrated model based on the park layout data comprises the following steps:

[0014] Step 11: Obtain the building facility layout data of the park through surveying and mapping as the park layout data;

[0015] Step 12: Construct a 3D geometric model of the park based on the building facility layout data;

[0016] The construction of the three-dimensional geometric model of the park includes the following steps:

[0017] Step 121: Use BIM modeling software to import surveying and mapping data and create a park terrain model; the BIM modeling software may be Revit;

[0018] Step 122: constructing three-dimensional models of each building one by one in the park terrain model according to the architectural drawings;

[0019] Step 123: construct a water supply and drainage pipeline model based on the pipeline network layout data and associate pipeline attributes;

[0020] Step 124: construct a sewage treatment facility model based on the design data, including the facility appearance and internal process structures;

[0021] Step 13: Collect the equipment BIM model, and integrate the equipment BIM model with the park 3D geometric model to obtain an integrated 3D model;

[0022] Step 14: Mark the attributes of each structure and sewage treatment equipment in the park's 3D geometric model to form a BIM integrated model;

[0023] The collecting of historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model comprises the following steps:

[0024] Step 21: deploying an online monitoring system for the sewage treatment facility; specifically, the online monitoring system includes a flow meter and a water quality analyzer;

[0025] Step 22: Obtain the operation data of the past N monitoring time periods from the online monitoring system, and collect various equipment control parameters and actual energy consumption of each sewage treatment equipment in each sewage treatment facility in each monitoring time period; N is the preset number of monitoring time periods;

[0026] Step 23: Standardize the collected operation data and equipment control parameters within all monitoring time periods. The standardized operation data and equipment control parameters, as well as the actual energy consumption of the sewage treatment equipment, form a set of historical operation data;

[0027] Step 24: For various sewage treatment facilities, preset efficiency indicators of treatment efficiency; and calculate the actual treatment efficiency in each monitoring time period based on the efficiency indicators as an efficiency label; the efficiency labels in all monitoring time periods constitute the historical operation status data.

[0028] The method of fitting an energy consumption model function for a sewage treatment facility based on historical operation data comprises the following steps:

[0029] Step 31: extracting characteristic values ​​of the operation characteristics of the sewage treatment facility and parameter values ​​of the equipment control parameters in each monitoring time period from the historical operation data to form a set of input characteristic vectors;

[0030] Step 32: For each wastewater treatment facility:

[0031] Each group of input feature vectors in each monitoring time period of the sewage treatment facility is used as an input of a first polynomial regression model, the first polynomial regression model uses the predicted value of energy consumption of the sewage treatment equipment in the monitoring time period as an output, uses the actual energy consumption of the sewage treatment equipment in the monitoring time period as a prediction target, uses the square of the difference between the predicted energy consumption value and the actual energy consumption as a first prediction error, and uses minimizing the sum of the first prediction errors of each monitoring time period as a training target; the first polynomial regression model is trained until the sum of the first prediction errors reaches convergence and the training is stopped;

[0032] Step 33: The polynomial function corresponding to the trained first polynomial regression model is used as the energy consumption model function of the corresponding sewage treatment facility.

[0033] The step of fitting the efficiency model function based on the historical operation data and the historical operation status data comprises the following steps:

[0034] Step 41: For each wastewater treatment facility:

[0035] Each group of input feature vectors in each monitoring time period of the sewage treatment facility is used as the input of the second polynomial regression model, the second polynomial regression model uses the treatment efficiency prediction value of the sewage treatment facility in the monitoring time period as the output, uses the efficiency label of the sewage treatment facility in the monitoring time period as the prediction target, uses the square of the difference between the treatment efficiency prediction value and the efficiency label as the second prediction error, and uses minimizing the sum of the second prediction errors of each monitoring time period as the training target; the second polynomial regression model is trained until the sum of the second prediction errors reaches convergence and the training is stopped;

[0036] Step 42: The polynomial function corresponding to the trained second polynomial regression model is used as the efficiency model function of the corresponding sewage treatment facility.

[0037] The method of generating a new equipment control scheme based on the difference in operating status data, combined with the energy consumption model function, the efficiency model function and the energy price information is as follows:

[0038] Each sewage treatment facility is numbered as i;

[0039] The energy consumption model function of the i-th sewage treatment facility is marked as F(Gi, Ci); wherein Gi is the set of operating data of the i-th sewage treatment facility in each monitoring time period, and Ci is the set of equipment control parameters of the sewage treatment equipment of the i-th sewage treatment facility in the current time period;

[0040] Label the efficiency model function of the i-th sewage treatment facility as X(Gi, Ci);

[0041] The set of actual operation data of the i-th sewage treatment facility in the current monitoring time period is marked as gi;

[0042] The real-time energy price in the current monitoring time period is marked as p;

[0043] Setting the optimization objective function H based on the real-time energy price, energy consumption model function and efficiency model function;

[0044] Construct constraint condition set U;

[0045] Taking minimizing the optimization objective function H as the optimization goal and the constraint condition set U as the model constraint, a convex optimization model is constructed. The convex optimization model is solved by the optimization solving tool to obtain the solution set of the set Ci of equipment control parameters. The new equipment control scheme is to set the equipment control parameters of the sewage treatment equipment of the i-th sewage treatment facility to the values ​​corresponding to the equipment control parameters in the solution set Ci.

[0046] A digital operation and maintenance management system based on BIM model is proposed, which includes data collection module, integrated model building module, model fitting module and operation and maintenance control module; wherein each module is electrically connected;

[0047] The data collection module collects the park layout data and real-time energy price information, and sends the park layout data to the integrated model building module, and sends the real-time energy price information to the operation and maintenance control module;

[0048] The integrated model building module builds a BIM integrated model based on the park layout data and sends the BIM integrated model to the model fitting module;

[0049] The model fitting module collects the historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model, fits the energy consumption model function for the sewage treatment facility based on the historical operation data, fits the efficiency model function based on the historical operation data and historical operation status data, and sends the energy consumption model function and the efficiency model function to the operation and maintenance control module;

[0050] The operation and maintenance control module collects the operating status data of the sewage treatment facilities in real time, compares the current operating status data with the operating status data of the previous cycle, and if the change value is greater than the preset change threshold, a new equipment control plan is generated based on the difference in the operating status data, combined with the energy consumption model function, efficiency model function and energy price information.

[0051] An electronic device is proposed, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0052] The processor executes the above-mentioned digital operation and maintenance management method based on the BIM model by calling the computer program stored in the memory.

[0053] A computer-readable storage medium is provided, on which a rewritable computer program is stored;

[0054] When the computer program runs on a computer device, the computer device executes the above-mentioned digital operation and maintenance management method based on the BIM model.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention proposes a digital operation and maintenance management method based on a BIM integrated model. First, a BIM integrated digital model of the park is constructed through surveying and mapping and three-dimensional modeling technology, which integrates the geometric information and attribute data of sewage treatment facilities, pipe networks and equipment. Secondly, using machine learning and other technologies, energy consumption models and treatment efficiency models are established for each facility based on historical operation data. On this basis, the current facility operation status data is collected in real time, the state changes are analyzed through the model, and the optimization model is constructed in combination with real-time energy price information to solve the optimal equipment control parameter scheduling scheme that can minimize the overall cost and meet the treatment efficiency requirements. The present invention organically combines the BIM model data, operation monitoring data, model analysis and optimization scheduling of sewage treatment facilities to achieve highly integrated and intelligent operation and maintenance management of sewage treatment facilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of a digital operation and maintenance management method based on a BIM model in Example 1 of the present invention;

[0058] Figure 2 A module connection relationship diagram of a digital operation and maintenance management system based on a BIM model in Example 2 of the present invention;

[0059] Figure 3 This is a schematic diagram of the structure of an electronic device in Embodiment 3 of the present invention;

[0060] Figure 4 This is a schematic diagram of the computer-readable storage medium structure in Example 4 of the present invention. DETAILED DESCRIPTION

[0061] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Example 1

[0063] like Figure 1 As shown in the figure, the digital operation and maintenance management method based on the BIM model includes the following steps:

[0064] Step 1: Collect park layout data and build a BIM integrated model based on the park layout data;

[0065] Step 2: Collect the historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model, fit the energy consumption model function for the sewage treatment facility based on the historical operation data, and fit the efficiency model function based on the historical operation data and historical operation status data;

[0066] Step 3: Collect real-time energy price information;

[0067] Step 4: Collect the operating status data of the sewage treatment facility in real time, compare the current operating status data with the operating status data of the previous cycle, and if the change value is greater than the preset change threshold, go to step 5, otherwise maintain the current control parameters;

[0068] Step 5: Generate a new equipment control plan based on the differences in operating status data, combined with energy consumption model function, efficiency model function and energy price information.

[0069] The collecting of the park layout data and constructing the BIM integrated model based on the park layout data includes the following steps:

[0070] Step 11: Obtain the building facility layout data of the park through surveying and mapping as the park layout data;

[0071] Specifically, the building facility layout data includes but is not limited to terrain data within the park, design drawings of the buildings, measurement and layout data of the water supply and drainage network, and facility design data of the sewage treatment facilities; the terrain data includes elevation, slope, etc., the design drawings of the buildings include plan views, elevation views, cross-section views, etc., the measurement and layout data include pipeline paths, materials, pipe diameters, etc.; the facility design data includes facility locations, process parameters, etc.; the sewage treatment facilities include but are not limited to fixed facilities such as sewage networks, aeration tanks, sedimentation tanks, and disinfection equipment. Some sewage treatment equipment is generally installed in sewage treatment facilities, such as booster pump stations in sewage networks, aeration fans in aeration tanks, and agitators in sedimentation tanks. The booster pump stations require electric power to drive the pumps to pressurize and transport sewage or tap water.

[0072] Step 12: Construct a 3D geometric model of the park based on the building facility layout data;

[0073] Specifically, the construction of the three-dimensional geometric model of the park includes the following steps:

[0074] Step 121: Use BIM modeling software to import surveying and mapping data and create a park terrain model; the BIM modeling software may be Revit;

[0075] Step 122: constructing three-dimensional models of each building one by one in the park terrain model according to the architectural drawings;

[0076] Step 123: construct a water supply and drainage pipeline model based on the pipeline network layout data and associate pipeline attributes;

[0077] Step 124: Based on the design data, a sewage treatment facility model is constructed, including the facility appearance and internal process structures.

[0078] Step 13: Collect the equipment BIM model, and integrate the equipment BIM model with the park 3D geometric model to obtain an integrated 3D model;

[0079] Specifically, the integration method of the integrated three-dimensional model is:

[0080] Obtain BIM model files of sewage treatment equipment from sewage treatment equipment manufacturers, such as aeration fans, pump stations, mixers, etc.;

[0081] Import the equipment BIM model of the sewage treatment equipment into the 3D geometric model of the park;

[0082] According to the location information of each sewage treatment equipment in the design data, the equipment model is placed at the corresponding sewage treatment facility location.

[0083] Step 14: Mark the attributes of each structure and sewage treatment equipment in the three-dimensional geometric model of the park to form a BIM integrated model; the structures include but are not limited to buildings, sewage treatment facilities, sewage pipe networks, etc. in the park;

[0084] The attribute tags include: attribute information such as process parameters of sewage treatment facilities, equipment models, and rated parameter data of sewage treatment equipment.

[0085] Furthermore, the collection of historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model includes the following steps:

[0086] Step 21: deploying an online monitoring system for the sewage treatment facility; specifically, the online monitoring system includes a flow meter, a water quality analyzer, etc.;

[0087] Step 22: Obtain the operation data of the past N monitoring time periods from the online monitoring system, and collect various equipment control parameters and actual energy consumption of each sewage treatment equipment in each sewage treatment facility in each monitoring time period; N is the preset number of monitoring time periods; the duration of the monitoring time period is the preset duration, specifically, 24 hours; and the equipment control parameters are specifically set according to the different types of sewage treatment equipment, such as the pressure of the booster pump station, the wind speed of the aeration fan, and the stirring speed of the agitator. Different control parameters will affect the specific energy consumption;

[0088] Specifically, the operation data include but are not limited to: sewage inlet and outlet flow data, inlet and outlet water quality parameters, such as COD, BOD, SS, ammonia nitrogen, etc.; process operation parameters, such as aeration volume, reflux ratio, etc.;

[0089] Step 23: Standardize the collected operation data and equipment control parameters within all monitoring time periods. The standardized operation data and equipment control parameters, as well as the actual energy consumption of the sewage treatment equipment, form a set of historical operation data;

[0090] Step 24: For various sewage treatment facilities, preset efficiency indicators of treatment efficiency; and calculate the actual treatment efficiency in each monitoring time period according to the efficiency indicators as an efficiency label; the efficiency labels in all monitoring time periods constitute historical operation status data;

[0091] Specifically, the general setting method of the treatment efficiency can be the treatment volume of the treatment subject per unit time divided by the total amount of the treatment subject. Since different sewage treatment facilities treat different subjects, the units of the treatment rate are also different. The definition of the efficiency index is specifically set according to the differences in the treatment subjects.

[0092] Furthermore, fitting an energy consumption model function for a sewage treatment facility based on historical operation data includes the following steps:

[0093] Step 31: extracting characteristic values ​​of the operation characteristics of the sewage treatment facility and parameter values ​​of the equipment control parameters in each monitoring time period from the historical operation data to form a set of input characteristic vectors;

[0094] Step 32: For each wastewater treatment facility:

[0095] Each group of input feature vectors in each monitoring time period of the sewage treatment facility is used as an input of a first polynomial regression model, the first polynomial regression model uses the predicted value of energy consumption of the sewage treatment equipment in the monitoring time period as an output, uses the actual energy consumption of the sewage treatment equipment in the monitoring time period as a prediction target, uses the square of the difference between the predicted energy consumption value and the actual energy consumption as a first prediction error, and uses minimizing the sum of the first prediction errors of each monitoring time period as a training target; the first polynomial regression model is trained until the sum of the first prediction errors reaches convergence and the training is stopped;

[0096] Step 33: The polynomial function corresponding to the trained first polynomial regression model is used as the energy consumption model function of the corresponding sewage treatment facility.

[0097] Furthermore, fitting the efficiency model function based on the historical operation data and the historical operation status data includes the following steps:

[0098] Step 41: For each wastewater treatment facility:

[0099] Each group of input feature vectors in each monitoring time period of the sewage treatment facility is used as the input of the second polynomial regression model, the second polynomial regression model uses the treatment efficiency prediction value of the sewage treatment facility in the monitoring time period as the output, uses the efficiency label of the sewage treatment facility in the monitoring time period as the prediction target, uses the square of the difference between the treatment efficiency prediction value and the efficiency label as the second prediction error, and uses minimizing the sum of the second prediction errors of each monitoring time period as the training target; the second polynomial regression model is trained until the sum of the second prediction errors reaches convergence and the training is stopped;

[0100] Step 42: The polynomial function corresponding to the trained second polynomial regression model is used as the efficiency model function of the corresponding sewage treatment facility.

[0101] Furthermore, the real-time unit price of energy is real-time electricity price data collected from the energy management system, which can be obtained by reading the official website of the local energy bureau or the electricity market;

[0102] Furthermore, the method of collecting the operation status data of the sewage treatment facility in real time is to collect the treatment efficiency of the sewage treatment facility as the operation status data in real time when treating the sewage discharged in the park.

[0103] Furthermore, the method of generating a new equipment control scheme based on the difference in operating status data, combined with the energy consumption model function, the efficiency model function and the energy price information is as follows:

[0104] Each sewage treatment facility is numbered as i;

[0105] The energy consumption model function of the i-th sewage treatment facility is marked as F(Gi, Ci); wherein Gi is the set of operating data of the i-th sewage treatment facility in each monitoring time period, and Ci is the set of equipment control parameters of the sewage treatment equipment of the i-th sewage treatment facility in the current time period;

[0106] Label the efficiency model function of the i-th sewage treatment facility as X(Gi, Ci);

[0107] It is understandable that when sewage is treated in the park, the actual operation data of each sewage treatment facility can be collected in real time, and the changes of these operation data in a short period of time are small; that is, these operation data can be used as a constant set in the next monitoring time period;

[0108] The set of actual operation data of the i-th sewage treatment facility in the current monitoring time period is marked as gi;

[0109] The real-time energy price in the current monitoring time period is marked as p;

[0110] Based on the real-time energy price, energy consumption model function and efficiency model function, the optimization objective function H is set; the formula for the optimization objective function H is:

[0111] H=∑ i (a1×p×F(gi,Ci)-a2×X(g1,Ci)); where, F(gi,Ci) and

[0112] X(g1, Ci) refers to substituting the set gi of actual operating data into the energy consumption model function F(Gi, Ci) and the efficiency model function X(Gi, Ci), respectively. a1 and a2 are both preset proportional coefficients greater than 0. It can be understood that the optimization objective function H is used to balance the total energy consumption and treatment efficiency of the sewage treatment equipment, so as to reduce the energy consumption of the treatment while ensuring the sewage treatment efficiency.

[0113] Construct constraint condition set U;

[0114] The constraint set U is: Among them, ci_min and ci_max are respectively the preset maximum and minimum values ​​of the equipment control parameter ci to avoid equipment out of control. For example, the maximum and minimum pressures are set for the booster pump station to prevent the solved pressure value from exceeding the reasonable range and causing out of control;

[0115] Taking minimizing the optimization objective function H as the optimization goal and the constraint condition set U as the model constraint, a convex optimization model is constructed. The convex optimization model is solved by the optimization solving tool to obtain the solution set of the set Ci of equipment control parameters. The new equipment control scheme is to set the equipment control parameters of the sewage treatment equipment of the i-th sewage treatment facility to the values ​​corresponding to the equipment control parameters in the solution set Ci.

[0116] Example 2

[0117] like Figure 2 As shown, the digital operation and maintenance management system based on the BIM model includes a data collection module, an integrated model building module, a model fitting module and an operation and maintenance control module; wherein each module is electrically connected;

[0118] The data collection module collects the park layout data and real-time energy price information, and sends the park layout data to the integrated model building module, and sends the real-time energy price information to the operation and maintenance control module;

[0119] The integrated model building module builds a BIM integrated model based on the park layout data and sends the BIM integrated model to the model fitting module;

[0120] The model fitting module collects the historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model, fits the energy consumption model function for the sewage treatment facility based on the historical operation data, fits the efficiency model function based on the historical operation data and historical operation status data, and sends the energy consumption model function and the efficiency model function to the operation and maintenance control module;

[0121] The operation and maintenance control module collects the operating status data of the sewage treatment facilities in real time, compares the current operating status data with the operating status data of the previous cycle, and if the change value is greater than the preset change threshold, a new equipment control plan is generated based on the difference in the operating status data, combined with the energy consumption model function, efficiency model function and energy price information.

[0122] Example 3

[0123] Figure 3 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Figure 3 As shown, according to another aspect of the present application, an electronic device 100 is also provided. The electronic device 100 may include one or more processors and one or more memories. The memory stores a computer-readable code, and when the computer-readable code is executed by one or more processors, the digital operation and maintenance management method based on the BIM model as described above can be executed.

[0124] The method or device according to the embodiment of the present application can also be used by Figure 3 The electronic device architecture shown in FIG. Figure 3 As shown, the electronic device 100 may include a bus 101, one or more CPUs 102, a ROM 103, a RAM 104, a communication port 105 connected to a network, an input / output component 106, a hard disk 107, etc. The storage device in the electronic device 100, such as the ROM 103 or the hard disk 107, may store the digital operation and maintenance management method based on the BIM model provided in the present application.

[0125] Furthermore, the electronic device 100 may further include a user interface 108. Figure 3 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 3 One or more components of an electronic device are shown.

[0126] Example 4

[0127] Figure 4 Schematic diagram of a computer-readable storage medium structure provided by an embodiment of the present application. Figure 4As shown, it is a computer-readable storage medium 200 according to an embodiment of the present application. Computer-readable instructions are stored on the computer-readable storage medium 200. When the computer-readable instructions are executed by the processor, the digital operation and maintenance management method based on the BIM model according to the embodiment of the present application described with reference to the above figures can be executed. The computer-readable storage medium 200 includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache). Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0128] In addition, according to the implementation of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, the non-transitory machine-readable storage medium stores machine-readable instructions, the machine-readable instructions can be run by a processor to execute instructions corresponding to the method steps provided by the present application, and when the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0129] The methods, apparatuses, and devices of the present application may be implemented in many ways. For example, the methods, apparatuses, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers recording media storing programs for executing the method according to the present application.

[0130] In addition, the parts of the above-mentioned technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0131] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0132] The above preset parameters or preset thresholds are all set by technicians in this field according to actual conditions or obtained through large-scale data simulation.

[0133] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A digital operation and maintenance management method based on BIM model, characterized in that: The following steps are involved: Step 1: Collect park layout data and build a BIM integrated model based on the park layout data; Step 2: Collect the historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model, fit the energy consumption model function for the sewage treatment facility based on the historical operation data, and fit the efficiency model function based on the historical operation data and historical operation status data; Step 3: Collect real-time energy price information; Step 4: Collect the operating status data of the sewage treatment facility in real time, compare the current operating status data with the operating status data of the previous cycle, and if the change value is greater than the preset change threshold, go to step 5, otherwise maintain the current control parameters; Step 5: Generate a new equipment control plan based on the differences in operating status data, combined with energy consumption model function, efficiency model function and energy price information.

2. The digital operation and maintenance management method based on the BIM model according to claim 1 is characterized in that: The collecting of the park layout data and building the BIM integrated model based on the park layout data comprises the following steps: Step 11: Obtain the building facility layout data of the park through surveying and mapping as the park layout data; Step 12: Construct a 3D geometric model of the park based on the building facility layout data; Step 13: Collect the equipment BIM model, and integrate the equipment BIM model with the park 3D geometric model to obtain an integrated 3D model; Step 14: Mark the attributes of each structure and sewage treatment equipment in the park’s 3D geometric model to form a BIM integrated model.

3. The digital operation and maintenance management method based on the BIM model according to claim 2 is characterized in that: The construction of the three-dimensional geometric model of the park includes the following steps: Step 121: Use BIM modeling software to import surveying and mapping data and create a park terrain model; Step 122: constructing three-dimensional models of each building one by one in the park terrain model according to the architectural drawings; Step 123: construct a water supply and drainage pipeline model based on the pipeline network layout data and associate pipeline attributes; Step 124: Based on the design data, a sewage treatment facility model is constructed, including the facility appearance and internal process structures.

4. The digital operation and maintenance management method based on the BIM model according to claim 3 is characterized in that: The collecting of historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model comprises the following steps: Step 21: deploying an online monitoring system for the sewage treatment facility; the online monitoring system includes a flow meter and a water quality analyzer; Step 22: Obtain the operation data of the past N monitoring time periods from the online monitoring system, and collect various equipment control parameters and actual energy consumption of each sewage treatment equipment in each sewage treatment facility in each monitoring time period; N is the preset number of monitoring time periods; Step 23: Standardize the collected operation data and equipment control parameters within all monitoring time periods. The standardized operation data and equipment control parameters, as well as the actual energy consumption of the sewage treatment equipment, form a set of historical operation data; Step 24: For various sewage treatment facilities, preset efficiency indicators of treatment efficiency; and calculate the actual treatment efficiency in each monitoring time period based on the efficiency indicators as an efficiency label; the efficiency labels in all monitoring time periods constitute the historical operation status data.

5. The digital operation and maintenance management method based on the BIM model according to claim 4 is characterized in that: The method of fitting an energy consumption model function for a sewage treatment facility based on historical operation data comprises the following steps: Step 31: extracting characteristic values ​​of the operation characteristics of the sewage treatment facility and parameter values ​​of the equipment control parameters in each monitoring time period from the historical operation data to form a set of input characteristic vectors; Step 32: For each wastewater treatment facility: Each group of input feature vectors in each monitoring time period of the sewage treatment facility is used as an input of a first polynomial regression model, the first polynomial regression model uses the predicted value of energy consumption of the sewage treatment equipment in the monitoring time period as an output, uses the actual energy consumption of the sewage treatment equipment in the monitoring time period as a prediction target, uses the square of the difference between the predicted energy consumption value and the actual energy consumption as a first prediction error, and uses minimizing the sum of the first prediction errors of each monitoring time period as a training target; the first polynomial regression model is trained until the sum of the first prediction errors reaches convergence and the training is stopped; Step 33: The polynomial function corresponding to the trained first polynomial regression model is used as the energy consumption model function of the corresponding sewage treatment facility.

6. The digital operation and maintenance management method based on the BIM model according to claim 5 is characterized in that: The step of fitting the efficiency model function based on the historical operation data and the historical operation status data comprises the following steps: Step 41: For each wastewater treatment facility: Each group of input feature vectors in each monitoring time period of the sewage treatment facility is used as the input of the second polynomial regression model, the second polynomial regression model uses the treatment efficiency prediction value of the sewage treatment facility in the monitoring time period as the output, uses the efficiency label of the sewage treatment facility in the monitoring time period as the prediction target, uses the square of the difference between the treatment efficiency prediction value and the efficiency label as the second prediction error, and uses minimizing the sum of the second prediction errors of each monitoring time period as the training target; the second polynomial regression model is trained until the sum of the second prediction errors reaches convergence and the training is stopped; Step 42: The polynomial function corresponding to the trained second polynomial regression model is used as the efficiency model function of the corresponding sewage treatment facility.

7. The digital operation and maintenance management method based on the BIM model according to claim 6 is characterized in that: The method of generating a new equipment control scheme based on the difference in operating status data, combined with the energy consumption model function, the efficiency model function and the energy price information is as follows: Each sewage treatment facility is numbered as i; The energy consumption model function of the i-th sewage treatment facility is marked as F(Gi, Ci); wherein Gi is the set of operating data of the i-th sewage treatment facility in each monitoring time period, and Ci is the set of equipment control parameters of the sewage treatment equipment of the i-th sewage treatment facility in the current time period; Label the efficiency model function of the i-th sewage treatment facility as X(Gi, Ci); The set of actual operation data of the i-th sewage treatment facility in the current monitoring time period is marked as gi; The real-time energy price in the current monitoring time period is marked as p; Setting the optimization objective function H based on the real-time energy price, energy consumption model function and efficiency model function; Construct constraint condition set U; Taking minimizing the optimization objective function H as the optimization goal and the constraint condition set U as the model constraint, a convex optimization model is constructed. The convex optimization model is solved by the optimization solving tool to obtain the solution set of the set Ci of equipment control parameters. The new equipment control scheme is to set the equipment control parameters of the sewage treatment equipment of the i-th sewage treatment facility to the values ​​corresponding to the equipment control parameters in the solution set Ci.

8. A digital operation and maintenance management system based on a BIM model, which is used to implement the digital operation and maintenance management method based on a BIM model as described in any one of claims 1 to 7, characterized in that: It includes a data collection module, an integrated model building module, a model fitting module and an operation and maintenance control module; wherein each module is electrically connected to each other; The data collection module collects the park layout data and real-time energy price information, and sends the park layout data to the integrated model building module, and sends the real-time energy price information to the operation and maintenance control module; The integrated model building module builds a BIM integrated model based on the park layout data and sends the BIM integrated model to the model fitting module; The model fitting module collects the historical operation data and historical operation status data of each sewage treatment facility in the BIM integrated model, fits the energy consumption model function for the sewage treatment facility based on the historical operation data, fits the efficiency model function based on the historical operation data and historical operation status data, and sends the energy consumption model function and the efficiency model function to the operation and maintenance control module; The operation and maintenance control module collects the operating status data of the sewage treatment facilities in real time, compares the current operating status data with the operating status data of the previous cycle, and if the change value is greater than the preset change threshold, a new equipment control plan is generated based on the difference in the operating status data, combined with the energy consumption model function, efficiency model function and energy price information.

9. An electronic device, characterized in that: include: processor and memory, wherein The memory stores a computer program that can be called by the processor; The processor executes the digital operation and maintenance management method based on the BIM model as described in any one of claims 1 to 7 in the background by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: A rewritable computer program is stored thereon; When the computer program runs on a computer device, the computer device executes the digital operation and maintenance management method based on the BIM model as described in any one of claims 1 to 7 in the background.

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