Method and system for evaluating intelligent operation and maintenance management value of public building group

Abstract

The invention discloses a public building group intelligent operation and maintenance management value evaluation method, and relates to the technical field of management value evaluation. Collecting people flow density and internal energy consumption data of the building group in real time; establishing a value influence factor, and forming a core index of operation and maintenance value evaluation; based on the people flow density and the energy consumption data, predicting operation and maintenance demands of each region, and identifying high-demand and high-energy-consumption regions; operation and maintenance personnel and equipment are intelligently distributed according to the analyzed requirements; according to the effect of resource allocation, the instant improvement effect of resource use on the operation and maintenance value of the building group is evaluated; according to the method, operation and maintenance personnel and equipment are preferentially allocated to a high-demand area through an intelligent scheduling algorithm, efficient utilization of resources is realized, resource configuration is dynamically adjusted, timeliness and accuracy of operation and maintenance work are ensured, improvement of operation and maintenance values is quantified by evaluating the effect of resource use, operation and maintenance management values of a building group are improved, and the operation and maintenance efficiency of the building group is improved. And resource allocation of the building group in operation and maintenance management is enabled to be more scientific and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance management value assessment, and in particular to an assessment method and system for the intelligent operation and maintenance management value of a public building complex. Background Art

[0002] With the continuous development of the construction industry, the management needs of public building complexes have become increasingly complex, especially in terms of operation and maintenance (O&M); the traditional O&M management model mainly relies on manual inspections and fixed inspection cycles, lacking real-time monitoring and intelligent evaluation methods; therefore, the O&M management of modern public building complexes has gradually developed in the direction of intelligence, dataization, and systematization; intelligent O&M systems based on technologies such as big data, the Internet of Things, and cloud computing have emerged. These systems can monitor various types of data inside the building in real time, such as crowd density, energy consumption data, equipment operating status, etc., to help managers quickly identify the operating status of the building and make effective decisions.

[0003] Although current intelligent operation and maintenance technology has made certain progress, the existing technology still faces some shortcomings; first, the existing system focuses on the monitoring of a single data source, such as only analyzing energy consumption data or a single crowd density data, and fails to comprehensively consider the comprehensive impact of these factors on the operation and maintenance value of the building complex; second, traditional operation and maintenance evaluation methods mostly rely on static models, ignoring the real-time changes of dynamic data and the interactive effects of multi-dimensional data; this makes many operation and maintenance decisions lack flexibility and cannot respond in time to demand fluctuations caused by changes in crowd flow and energy consumption during the use of the building; more importantly, existing technologies often ignore regional management needs. Different areas of the building complex often have different usage characteristics and energy consumption levels, and unified operation and maintenance evaluation standards may not be applicable to all areas. Summary of the invention

[0004] In view of the problems existing in the above-mentioned background technology, the present invention proposes a method and system for evaluating the value of intelligent operation and maintenance management of public building complexes.

[0005] Therefore, the problem to be solved by the present invention is how to solve the problems of insufficient timeliness, insufficient credibility assessment and lack of flexible response mechanism.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides an evaluation method for the intelligent operation and maintenance management value of a public building complex, which includes: collecting the crowd density and internal energy consumption data of the building complex in real time to obtain core information closely related to the operation and maintenance value; establishing a value influencing factor based on energy consumption and crowd density to form a core indicator for operation and maintenance value evaluation; based on crowd density and energy consumption data, predicting the operation and maintenance needs of each area, identifying high-demand and high-energy consumption areas, and providing a basis for optimizing resource allocation; based on the analyzed needs, intelligently allocating operation and maintenance personnel and equipment, giving priority support to high-demand areas, and ensuring efficient use of resources; based on the effect of resource allocation, evaluating the immediate improvement effect of resource utilization on the operation and maintenance value of the building complex, and feeding back to the management system.

[0008] As a preferred solution of the method for evaluating the intelligent operation and maintenance management value of a public building complex described in the present invention, crowd sensors are installed at key entrances, floors and elevators of the building complex to monitor crowd density in real time and collect personnel distribution data in different time periods and areas so as to grasp the utilization rate of the building and the dynamics of crowd flow; smart electric meters and energy consumption monitoring equipment are used to collect energy consumption data of various areas of the building, wherein the energy consumption data includes electric energy data and water energy data, which are classified by area and equipment to form time series data of various energy consumptions, providing a key reference for subsequent operation and maintenance value evaluation.

[0009] The collected energy consumption and crowd density data are cleaned and standardized, transmitted to the data center through the Internet of Things, and stored by timestamp and region to ensure that the data can be called in real time and processed quickly.

[0010] As a preferred solution of the evaluation method of the intelligent operation and maintenance management value of the public building complex described in the present invention, according to the crowd density data and energy consumption data collected in step S1, the crowd density per unit area and the unit energy consumption at different times and regions are weightedly calculated to obtain the crowd density characteristic value and the energy consumption characteristic value respectively to ensure that the characteristic values ​​can reflect the usage and consumption level of the building complex, as follows:

[0011]

[0012] Among them, D ta A represents the flow density per unit area of ​​the ath area within time t. a represents the total area of ​​the ath region, P ta represents the total number of people in the ath region within time t, E ta represents the unit energy consumption of the ath region within time t, E total,ta Represents the total energy consumption of the ath area within time t.

[0013] The crowd density characteristic value and energy consumption characteristic value are normalized to obtain a standardized crowd density index. and standardized energy consumption indicators Ensure that each indicator has a consistent dimension in subsequent calculations so that the value impact factors calculated subsequently can accurately measure the operation and maintenance value of different regions.

[0014] As a preferred solution of the evaluation method of the intelligent operation and maintenance management value of public building complexes described in the present invention, wherein: based on the standardized crowd density index and standardized energy consumption indicators The operation and maintenance pressure of different regions is calculated according to the set weights to obtain the regional operation and maintenance pressure coefficient, as follows:

[0015]

[0016] Among them, ω D and ω E Represents the standardized crowd density index and standardized energy consumption indicators The weight, C a Represents the operation and maintenance pressure coefficient of the a-th region.

[0017] The regional operation and maintenance pressure coefficient is combined with the historical operation and maintenance data factor of the current region to construct the operation and maintenance value impact factor, forming the core indicators of operation and maintenance value evaluation, as follows:

[0018] V a =C a ×(1+H a )

[0019] Among them, V a represents the operation and maintenance value impact factor of the a-th region, H a Represents the historical operation and maintenance data factor of the a-th region.

[0020] The operation and maintenance value influencing factor can reflect the operation and maintenance value of a specific area under different usage scenarios, and provide a core reference indicator for the demand prediction in step S3.

[0021] As a preferred solution of the evaluation method of the intelligent operation and maintenance management value of public building complexes described in the present invention, based on the operation and maintenance historical data collected in the early stage, the crowd density and energy consumption change trends of each area in different time periods are analyzed to generate a time series model, which is as follows:

[0022]

[0023] in, represents the predicted crowd density, represents the predicted energy consumption value, α represents the preset smoothing coefficient, D(t) represents the crowd density data at time t, E(t) represents the energy consumption data at time t, represents the predicted value of passenger flow density demand at time t, represents the predicted energy consumption at time t, and Δt is the predicted time period; ensuring that the model can predict short-term demand fluctuations.

[0024] The operation and maintenance value impact factor V is combined with the time series model to predict the demand level of each area in a specific period of time and generate a list of high-demand areas, as follows:

[0025]

[0026] Among them, P ad represents the demand forecast indicator of high-demand areas, and d represents demand; the list can identify the areas that require priority operation and maintenance in the future and provide specific guidance for resource allocation in subsequent steps.

[0027] Based on the current standardized energy consumption indicators of each area, identify the areas where energy consumption exceeds the preset threshold during a specific period of time and form a list of high energy consumption areas, as follows:

[0028]

[0029] Among them, P ae It represents the high energy consumption area identification index, and e represents energy consumption.

[0030] Preset energy consumption index T ae , when P ae >T ae , the ath area is recorded in the list of high energy consumption areas; the list of high energy consumption areas ensures that energy-intensive areas receive appropriate priority in resource allocation to reduce the overall energy consumption load of the building.

[0031] Combined with the list of high-demand areas and the list of high-energy consumption areas, the operation and maintenance demand score of each area is calculated comprehensively, as follows:

[0032] S a =ω d ×P ad +ω e ×P ae

[0033] Among them, S a Represents the comprehensive operation and maintenance demand score of the ath region, which is used to indicate the operation and maintenance priority of each region. The higher the score, the more urgent the demand. d and ω e They represent the demand weight coefficients (used to adjust the high demand area prediction index P ad The impact on the total score) and the energy consumption weight coefficient (used to adjust the high energy consumption area identification index P ae impact on the overall score).

[0034] As a preferred solution of the evaluation method of the intelligent operation and maintenance management value of public building complexes described in the present invention, the operation and maintenance demand score S a As the resource allocation priority of each area, the priority sequence ensures that high-demand and high-energy consumption areas are given priority support in resource allocation. Based on the resource allocation priority, the required number of operation and maintenance resources (including personnel and equipment) is calculated for each high-priority area, and the resource allocation ratio is adjusted as needed to form a resource demand allocation table, as follows:

[0035] R a =β×S a

[0036] Among them, R a represents the amount of operation and maintenance resources in the ath region, and β represents the preset resource allocation ratio coefficient; the resource demand allocation table ensures that each region obtains reasonable resource support.

[0037] According to the resource demand allocation table, operation and maintenance personnel and equipment are allocated in real time through a dynamic scheduling algorithm. The dynamic scheduling algorithm uses the real-time operation and maintenance status of each area (such as task completion progress and personnel availability) to dynamically adjust the allocation results to achieve optimal resource scheduling.

[0038] After resource scheduling is completed, based on the feedback from the operation and maintenance personnel and the equipment usage status, the resource usage is recorded to form a resource utilization analysis report, which provides a reference for the evaluation of the operation and maintenance value enhancement effect in step S5.

[0039] As a preferred scheme for the evaluation method of the intelligent operation and maintenance management value of the public building complex described in the present invention, according to the actual resource usage of each area in step S4, the effect data after resource allocation is collected, including data such as personnel response time, task completion efficiency and equipment operation status, so as to reflect the degree to which resource allocation satisfies the regional operation and maintenance needs.

[0040] Based on the resource allocation effect data, combined with the operation and maintenance value influencing factor V a , calculate the operation and maintenance value improvement rate after resource allocation, and obtain the operation and maintenance improvement effect index, which is as follows:

[0041]

[0042] Among them, V improve,a represents the improvement rate of the operation and maintenance value of the ath region, V new,a represents the operation and maintenance value of the ath region after resource allocation, V old,a It represents the operation and maintenance value of the ath area before resource allocation; the operation and maintenance improvement effect index is used to evaluate the impact of the current allocation strategy on the overall operation and maintenance value of the building complex.

[0043] The operation and maintenance improvement effect indicators of multiple areas are used for weighted aggregation to generate the overall operation and maintenance value assessment results of the building complex, as follows:

[0044]

[0045] Among them, V overall represents the overall operation and maintenance value assessment result of the building complex, G represents the total number of regions, represents the weight coefficient of the ath area; the evaluation result can quantify the comprehensive contribution of the current operation and maintenance management strategy to the building value, and provide a basis for further optimization of the management system.

[0046] The overall operation and maintenance value assessment results are fed back to the management system, and future resource allocation strategies and optimization plans are adjusted based on the assessment results to form a closed-loop feedback mechanism in order to dynamically improve the operation and maintenance management value of the building complex.

[0047] In the second aspect, an embodiment of the present invention provides an evaluation system for the intelligent operation and maintenance management value of a public building complex, which includes a data acquisition module for installing crowd and energy consumption monitoring equipment in key areas of the building complex, collecting crowd density and energy consumption data in real time, and transmitting the data to a data center through the Internet of Things to grasp the use of the building and support subsequent operation and maintenance value evaluation; a characteristic value calculation and standardization module for performing weighted calculation and standardized processing on the collected crowd density and energy consumption data, generating standardized density and energy consumption indicators, ensuring data consistency, and providing accurate input for operation and maintenance pressure and value calculation; an operation and maintenance value evaluation module for performing weighted calculation and standardized processing on the collected crowd density and energy consumption data, generating standardized density and energy consumption indicators, ensuring data consistency, and providing accurate input for operation and maintenance pressure and value calculation; and an operation and maintenance value evaluation module for evaluating the value of the building based on the standardized density and energy consumption data. The module calculates the regional operation and maintenance pressure coefficient based on the historical operation and maintenance data, generates the operation and maintenance value influencing factor as the core data support for demand forecasting; the module of demand forecasting and resource priority is used to predict the operation and maintenance demand of each region based on the operation and maintenance value influencing factor and time series model, generates a list of high-demand and high-energy consumption areas, calculates the operation and maintenance demand score comprehensively, and determines the resource allocation priority of each region; the module of resource allocation and effect feedback is used to allocate and schedule resources according to priority, generates an operation and maintenance effect analysis report by recording resource usage, and calculates the overall operation and maintenance value of the building complex in combination with the operation and maintenance improvement effect, and feeds back to the management system to form an optimization closed loop.

[0048] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the method for evaluating the value of intelligent operation and maintenance management of a public building complex as described in the first aspect of the present invention are implemented.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: the computer program instructions, when executed by a processor, implement the steps of the method for evaluating the value of intelligent operation and maintenance management of a public building complex as described in the first aspect of the present invention.

[0050] The beneficial effects of the present invention are as follows: by collecting the crowd density and energy consumption data of the building complex in real time, the present invention can accurately grasp the usage and energy consumption level of the building, and provide core reference information for the subsequent operation and maintenance value evaluation; by weighted calculation and standardized processing of the crowd density and energy consumption data, a value influencing factor is established, so that the operation and maintenance value evaluation of each area is more accurate, and a scientific basis is provided for the prediction of operation and maintenance demand; based on the time series model, combined with the operation and maintenance value influencing factor, the operation and maintenance demand of each area is accurately predicted, and the high-demand and high-energy consumption areas are identified, thereby providing a clear priority order for resource allocation; through the intelligent scheduling algorithm, the operation and maintenance personnel and equipment are preferentially allocated to the high-demand areas, so as to realize the efficient utilization of resources, and dynamically adjust the resource allocation to ensure the timeliness and accuracy of the operation and maintenance work; finally, by evaluating the effect of resource utilization, the improvement of the operation and maintenance value is quantified, and a closed-loop optimization mechanism based on feedback is formed to continuously improve the operation and maintenance management value of the building complex; the whole process ensures that the resource allocation of the building complex in the operation and maintenance management is more scientific and efficient, reduces the operating cost, optimizes the energy use of the building, and improves the overall operation and maintenance value. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 Schematic diagram of the steps of the evaluation method for the intelligent operation and maintenance management value of public building complexes.

[0053] Figure 2 This is a structural diagram of the evaluation system for the intelligent operation and maintenance management value of public building complexes. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0057] Example 1

[0058] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a method for evaluating the value of intelligent operation and maintenance management of public building complexes, including:

[0059] Step S1: Collect the crowd density and internal energy consumption data of the building complex in real time to obtain core information closely related to operation and maintenance value.

[0060] Specifically, crowd sensors are installed at key entrances, floors and elevators of the building complex to monitor crowd density in real time and collect personnel distribution data in different time periods and areas in order to understand the utilization rate of the building and the dynamics of crowd flow; smart meters and energy consumption monitoring equipment are used to collect energy consumption data from various areas of the building. The energy consumption data includes electricity data and water energy data, which are classified by area and equipment to form time series data of various energy consumption, providing key reference for subsequent operation and maintenance value evaluation.

[0061] Furthermore, the collected energy consumption and crowd density data are cleaned and standardized, transmitted to the data center through the Internet of Things, and stored by timestamp and region to ensure that the data can be called in real time and processed quickly.

[0062] Step S2: Establish value influencing factors based on energy consumption and passenger density to form core indicators for operation and maintenance value assessment.

[0063] Specifically, according to the crowd density data and energy consumption data collected in step S1, weighted calculations are performed on the crowd density per unit area and the unit energy consumption at different times and regions to obtain crowd density characteristic values ​​and energy consumption characteristic values, respectively, to ensure that the characteristic values ​​can reflect the usage and consumption level of the building complex, as follows:

[0064]

[0065] Among them, D ta A represents the flow density per unit area of ​​the ath area within time t.a represents the total area of ​​the ath region, P ta represents the total number of people in the ath region within time t, E ta represents the unit energy consumption of the ath region within time t, E total,ta represents the total energy consumption of the ath region within time t;

[0066] The crowd density characteristic value and energy consumption characteristic value are normalized to obtain a standardized crowd density index. and standardized energy consumption indicators Ensure that each indicator has a consistent dimension in subsequent calculations so that the value impact factors calculated subsequently can accurately measure the operation and maintenance value of different regions.

[0067] Furthermore, based on the standardized crowd density index and standardized energy consumption indicators The operation and maintenance pressure of different regions is calculated according to the set weights to obtain the regional operation and maintenance pressure coefficient, as follows:

[0068]

[0069] Among them, ω D and ω E Represents the standardized crowd density index and standardized energy consumption indicators The weight, C a represents the operation and maintenance pressure coefficient of the ath region;

[0070] The regional operation and maintenance pressure coefficient is combined with the historical operation and maintenance data factor of the current region to construct the operation and maintenance value impact factor, forming the core indicators of operation and maintenance value evaluation, as follows:

[0071] V a =C a ×(1+H a )

[0072] Among them, V a represents the operation and maintenance value impact factor of the a-th region, H a Represents the historical operation and maintenance data factor of the a-th region;

[0073] The operation and maintenance value influencing factor can reflect the operation and maintenance value of a specific area under different usage scenarios, and provide a core reference indicator for the demand prediction in step S3.

[0074] Step S3: Based on the crowd density and energy consumption data, predict the operation and maintenance needs of each area, identify high-demand and high-energy consumption areas, and provide a basis for optimizing resource allocation.

[0075] Specifically, based on the historical operation and maintenance data collected in the early stage, the trend of passenger density and energy consumption in each area in different time periods is analyzed to generate a time series model, as follows:

[0076]

[0077] in, represents the predicted crowd density, represents the predicted energy consumption value, α represents the preset smoothing coefficient, D(t) represents the crowd density data at time t, E(t) represents the energy consumption data at time t, represents the predicted value of passenger flow density demand at time t, represents the predicted energy consumption at time t, and Δt is the predicted time period; ensuring that the model can predict short-term demand fluctuations.

[0078] Furthermore, the operation and maintenance value impact factor V is combined with the time series model to predict the demand level of each area in a specific period of time and generate a list of high-demand areas, as follows:

[0079]

[0080] Among them, P ad represents the demand forecast indicator of high-demand areas, and d represents demand; the list can identify areas that require priority maintenance in the future and provide specific guidance for resource allocation in subsequent steps

[0081] Based on the current standardized energy consumption indicators of each area, identify the areas where energy consumption exceeds the preset threshold during a specific period of time and form a list of high energy consumption areas, as follows:

[0082]

[0083] Among them, P ae represents the high energy consumption area identification index, and e represents energy consumption;

[0084] Preset energy consumption index T ae , when P ae >T ae , then the ath area is recorded in the list of high energy consumption areas; the list of high energy consumption areas ensures that energy-intensive areas receive appropriate priority in resource allocation to reduce the overall energy consumption load of the building

[0085] Combined with the list of high-demand areas and the list of high-energy consumption areas, the operation and maintenance demand score of each area is calculated comprehensively, as follows:

[0086] S a =ω d ×P ad +ω e ×Pae

[0087] Among them, S a Represents the comprehensive operation and maintenance demand score of the ath region, which is used to indicate the operation and maintenance priority of each region. The higher the score, the more urgent the demand. d and ω e They represent the demand weight coefficients (used to adjust the high demand area prediction index P ad The impact on the total score) and the energy consumption weight coefficient (used to adjust the high energy consumption area identification index P ae impact on the overall score).

[0088] Step S4: Based on the analyzed needs, intelligently allocate operation and maintenance personnel and equipment, give priority to supporting high-demand areas, and ensure efficient use of resources.

[0089] Specifically, the operation and maintenance requirements are scored as S a As the resource allocation priority of each area, the priority sequence ensures that high-demand and high-energy consumption areas are given priority support in resource allocation. Based on the resource allocation priority, the required number of operation and maintenance resources (including personnel and equipment) is calculated for each high-priority area, and the resource allocation ratio is adjusted as needed to form a resource demand allocation table, as follows:

[0090] R a =β×S a

[0091] Among them, R a represents the amount of operation and maintenance resources in the ath region, and β represents the preset resource allocation ratio coefficient; the resource demand allocation table ensures that each region obtains reasonable resource support.

[0092] Furthermore, according to the resource demand allocation table, operation and maintenance personnel and equipment are allocated in real time through a dynamic scheduling algorithm. The dynamic scheduling algorithm utilizes the real-time operation and maintenance status of each area (such as task completion progress, personnel availability) to dynamically adjust the allocation results to achieve optimal resource scheduling.

[0093] After resource scheduling is completed, based on the feedback from the operation and maintenance personnel and the equipment usage status, the resource usage is recorded to form a resource utilization analysis report, which provides a reference for the evaluation of the operation and maintenance value enhancement effect in step S5.

[0094] Step S5: Based on the effect of resource allocation, evaluate the immediate improvement effect of resource utilization on the operation and maintenance value of the building complex, and feed back to the management system.

[0095] Specifically, according to the actual resource usage of each area in step S4, the effect data after resource allocation is collected, including data such as personnel response time, task completion efficiency and equipment operation status, so as to reflect the degree to which resource allocation meets the regional operation and maintenance needs.

[0096] Based on the resource allocation effect data, combined with the operation and maintenance value influencing factor V a , calculate the operation and maintenance value improvement rate after resource allocation, and obtain the operation and maintenance improvement effect index, which is as follows:

[0097]

[0098] Among them, V improve,a represents the improvement rate of the operation and maintenance value of the ath region, V new,a represents the operation and maintenance value of the ath region after resource allocation, V old,a It represents the operation and maintenance value of the ath area before resource allocation; the operation and maintenance improvement effect index is used to evaluate the impact of the current allocation strategy on the overall operation and maintenance value of the building complex.

[0099] Furthermore, the operation and maintenance improvement effect indicators of multiple areas are used for weighted aggregation to generate the overall operation and maintenance value assessment results of the building complex, as follows:

[0100]

[0101] Among them, V overall represents the overall operation and maintenance value assessment result of the building complex, G represents the total number of regions, Represents the weight coefficient of the ath area; the evaluation result can quantify the comprehensive contribution of the current operation and maintenance management strategy to the building value, and provide a basis for further optimization of the management system

[0102] The overall operation and maintenance value assessment results are fed back to the management system, and future resource allocation strategies and optimization plans are adjusted based on the assessment results to form a closed-loop feedback mechanism in order to dynamically improve the operation and maintenance management value of the building complex.

[0103] Furthermore, the present embodiment also provides an evaluation system for the intelligent operation and maintenance management value of a public building complex, including a data acquisition module, which is used to install crowd flow and energy consumption monitoring equipment in key areas of the building complex, collect crowd density and energy consumption data in real time, and transmit the data to a data center through the Internet of Things to understand the use of the building and support subsequent operation and maintenance value evaluation; a characteristic value calculation and standardization module, which is used to perform weighted calculation and standardized processing on the collected crowd density and energy consumption data, generate standardized density and energy consumption indicators, ensure data consistency, and provide accurate input for operation and maintenance pressure and value calculation; an operation and maintenance value evaluation module, which is used to calculate and standardize the crowd density and energy consumption data based on the standardized density and energy consumption data. Indicators, calculate the regional operation and maintenance pressure coefficient, and combine historical operation and maintenance data to generate the operation and maintenance value impact factor as the core data support for demand forecasting; the demand forecasting and resource priority module is used to predict the operation and maintenance needs of each region based on the operation and maintenance value impact factor and time series model, generate a list of high-demand and high-energy consumption areas, comprehensively calculate the operation and maintenance demand score, and determine the resource allocation priority of each region; the resource allocation and effect feedback module is used to allocate and schedule resources according to priority, generate an operation and maintenance effect analysis report by recording resource usage, and calculate the overall operation and maintenance value of the building complex in combination with the operation and maintenance improvement effect, and feedback to the management system to form an optimization closed loop.

[0104] This embodiment also provides a computer device, which is suitable for the evaluation method of the intelligent operation and maintenance management value of a public building complex, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the evaluation method of the intelligent operation and maintenance management value of a public building complex proposed in the above embodiment.

[0105] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0106] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for evaluating the value of intelligent operation and maintenance management of a public building complex as proposed in the above embodiment is implemented.

[0107] In summary, the present invention can accurately grasp the usage and energy consumption level of buildings by collecting the crowd density and energy consumption data of the building complex in real time, and provide core reference information for the subsequent operation and maintenance value evaluation; by weighted calculation and standardized processing of the crowd density and energy consumption data, a value influencing factor is established, so that the operation and maintenance value evaluation of each area is more accurate, and a scientific basis is provided for the prediction of operation and maintenance demand; based on the time series model, combined with the operation and maintenance value influencing factor, the operation and maintenance demand of each area is accurately predicted, and the high-demand and high-energy consumption areas are identified, thereby providing a clear priority order for resource allocation; through the intelligent scheduling algorithm, the operation and maintenance personnel and equipment are preferentially allocated to the high-demand areas, so as to realize the efficient utilization of resources, and dynamically adjust the resource allocation to ensure the timeliness and accuracy of the operation and maintenance work; finally, by evaluating the effect of resource utilization, the improvement of the operation and maintenance value is quantified, and a closed-loop optimization mechanism based on feedback is formed to continuously improve the operation and maintenance management value of the building complex; the whole process ensures that the resource allocation of the building complex in the operation and maintenance management is more scientific and efficient, reduces the operating cost, optimizes the energy use of the building, and improves the overall operation and maintenance value.

[0108] Example 2

[0109] In the second embodiment, a method for evaluating the value of intelligent operation and maintenance management of a public building complex is provided. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0110] Table 1 Operation and maintenance value assessment experimental data

[0111]

[0112]

[0113] It can be seen from Table 1 that the impact of implementing the invention content has obvious advantages over the prior art. First, we noticed the differences in the population density per unit area and the unit energy consumption data of each area. According to the data in the table, the population density per unit area of ​​area 2 is 4.1 people / ㎡, which is higher than that of other areas. At the same time, its unit energy consumption is 1.4 kWh / ㎡, which also shows high energy consumption. Area 5 shows a higher population density per unit area (4.3 people / ㎡) and energy consumption characteristic value (1.05), which indicates that it is a high-demand and high-energy consumption area in the operation of the building complex. Such areas need special attention and should be given priority to obtain more operation and maintenance support in the process of resource allocation and optimization.

[0114] After standardization, the standardized passenger density index and standardized energy consumption index further clearly show the differences in operation and maintenance requirements between regions; for example, the standardized passenger density index of region 4 is 0.95, and the higher standardized energy consumption index (0.80) shows that the energy consumption of this region is also high when the passenger density is high, requiring more resource support; while the standardized indicators of region 6 are 0.85 and 0.70 respectively, indicating that it has lower demands and may not need to allocate too many resources in priority when allocating resources.

[0115] Regarding the calculation of regional operation and maintenance pressure coefficients and operation and maintenance value influencing factors, the data in the table further strengthens our understanding of the innovativeness of the invention; the operation and maintenance pressure coefficient of area 2 is relatively high, indicating that it is under great pressure in the operation and maintenance process and requires special attention; in traditional building complex operation and maintenance management, it is often difficult to accurately identify these high-demand areas, and the present invention can more accurately calculate the operation and maintenance pressure of each area by integrating crowd density and energy consumption data.

[0116] In addition, by comparing the standardized passenger density index and the standardized energy consumption index, it can be seen that the comprehensive operation and maintenance demand score in the invention (combining the demand weight coefficient and the energy consumption weight coefficient) provides a more scientific basis for resource allocation in different areas; Area 2 and Area 5 rank higher in the comprehensive operation and maintenance demand score, which shows that the system can accurately identify high-demand areas and provide reasonable resource support, avoiding resource waste.

[0117] In general, the present invention has the following innovations and advantages compared with the existing technology: by collecting the crowd density and energy consumption data of the building complex in real time and performing standardized processing, the present invention can accurately grasp the operation and maintenance needs of each area, avoiding the rough evaluation based on a single data point in the traditional method; the operation and maintenance value influencing factor proposed by the present invention provides a more accurate calculation model for the prediction of regional operation and maintenance needs and resource allocation, effectively reducing the operation and maintenance costs of energy-intensive areas; the comprehensive operation and maintenance demand score combines the demand weight coefficient and the energy consumption weight coefficient to make resource allocation more in line with actual needs, avoid the blind allocation in the traditional method, and improve the operation and maintenance efficiency; through the combination of historical data and the feedback of operation and maintenance data, the present invention also optimizes the resource scheduling strategy and improves the overall operation and maintenance efficiency of the building complex.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. The method for evaluating the value of intelligent operation and maintenance management of public building complexes is characterized by: include: Step S1: collect the crowd density and internal energy consumption data of the building complex in real time; Step S2: Establish value influencing factors based on energy consumption and passenger flow density to form core indicators for operation and maintenance value assessment; Step S3: Based on the crowd density and energy consumption data, predict the operation and maintenance needs of each area and identify high-demand and high-energy consumption areas; Step S4: Intelligently allocate operation and maintenance personnel and equipment based on the analysis requirements; Step S5: Based on the effect of resource allocation, evaluate the immediate improvement effect of resource utilization on the operation and maintenance value of the building complex.

2. The method for evaluating the value of intelligent operation and maintenance management of public buildings as claimed in claim 1, characterized in that: The step S1 comprises the following steps: Install crowd flow sensors at key entrances, floors and elevators of the building complex to monitor crowd density in real time and collect data on personnel distribution in different time periods and areas; use smart meters and energy consumption monitoring equipment to collect energy consumption data in various areas of the building, including electricity data and water energy data, which are classified by area and equipment to form time series data of various energy consumption; The collected energy consumption and crowd density data are cleaned and standardized, transmitted to the data center through the Internet of Things, and stored by timestamp and region.

3. The method for evaluating the value of intelligent operation and maintenance management of public buildings as claimed in claim 2, characterized in that: The step S2 comprises the following steps: According to the crowd density data and energy consumption data collected in step S1, weighted calculations are performed on the crowd density per unit area and the unit energy consumption at different times and regions to obtain the crowd density characteristic value and the energy consumption characteristic value, respectively, as follows: Among them, D ta A represents the flow density per unit area of ​​the ath area within time t. a represents the total area of ​​the ath region, P ta represents the total number of people in the ath region within time t, E ta represents the unit energy consumption of the ath region within time t, E total,ta represents the total energy consumption of the ath region within time t; The crowd density characteristic value and energy consumption characteristic value are normalized to obtain a standardized crowd density index. and standardized energy consumption indicators 4. The method for evaluating the value of intelligent operation and maintenance management of public buildings as claimed in claim 3 is characterized by: The step S2 further comprises the following steps: Based on the standardized crowd density index and standardized energy consumption indicators The operation and maintenance pressure of different regions is calculated according to the set weights to obtain the regional operation and maintenance pressure coefficient, as follows: Among them, ω D and ω E Represents the standardized crowd density index and standardized energy consumption indicators The weight, C a represents the operation and maintenance pressure coefficient of the ath region; The regional operation and maintenance pressure coefficient is combined with the historical operation and maintenance data factor of the current region to construct the operation and maintenance value impact factor, forming the core indicators of operation and maintenance value evaluation, as follows: V a =C a ×(1+H a ) Among them, V a represents the operation and maintenance value impact factor of the a-th region, H a Represents the historical operation and maintenance data factor of the a-th region.

5. The method for evaluating the value of intelligent operation and maintenance management of public buildings as claimed in claim 4, characterized in that: The step S3 comprises the following steps: Based on the historical operation and maintenance data collected in the early stage, the trend of passenger density and energy consumption in each area in different time periods is analyzed to generate a time series model, as follows: in, represents the predicted crowd density, represents the predicted energy consumption value, α represents the preset smoothing coefficient, D(t) represents the crowd density data at time t, E(t) represents the energy consumption data at time t, represents the predicted value of passenger flow density demand at time t, It represents the predicted energy consumption at time t, and Δt is the predicted time period; The operation and maintenance value impact factor V is combined with the time series model to predict the demand level of each area in a specific period of time and generate a list of high-demand areas, as follows: Among them, P ad represents the demand forecast index of high-demand areas, and d represents demand; Based on the current standardized energy consumption indicators of each area, identify the areas where energy consumption exceeds the preset threshold during a specific period of time and form a list of high energy consumption areas, as follows: Among them, P ae represents the high energy consumption area identification index, and e represents energy consumption; Preset energy consumption index T ae , when P ae >T ae , then the ath area is recorded in the list of high energy consumption areas; Combined with the list of high-demand areas and the list of high-energy consumption areas, the operation and maintenance demand score of each area is calculated comprehensively, as follows: S a =ω d ×P ad +oh e ×P ae Among them, S a represents the comprehensive operation and maintenance demand score of the a-th region, ω d and ω e They represent the demand weight coefficient and energy consumption weight coefficient respectively.

6. The method for evaluating the value of intelligent operation and maintenance management of public buildings as claimed in claim 5, characterized in that: The step S4 comprises the following steps: Score the operation and maintenance requirements a As the resource allocation priority of each area, based on the resource allocation priority, the number of operation and maintenance resources is calculated for each high-priority area, and the allocation ratio of resources is adjusted as needed to form a resource demand allocation table, which is as follows: R a =β×S a Among them, R a represents the amount of operation and maintenance resources in the ath region, and β represents the preset resource allocation ratio coefficient; According to the resource demand allocation table, operation and maintenance personnel and equipment are allocated in real time through a dynamic scheduling algorithm. After the resource scheduling is completed, based on the feedback from the operation and maintenance personnel and the equipment usage status, the resource usage is recorded to form a resource utilization analysis report.

7. The method for evaluating the value of intelligent operation and maintenance management of public buildings as claimed in claim 6, characterized in that: The step S5 comprises the following steps: According to the actual resource usage of each area in step S4, collect the effect data after resource allocation, including personnel response time, task completion efficiency and equipment operation status; Based on the resource allocation effect data, combined with the operation and maintenance value influencing factor V a , calculate the operation and maintenance value improvement rate after resource allocation, and obtain the operation and maintenance improvement effect index, which is as follows: Among them, V improve,a represents the improvement rate of the operation and maintenance value of the ath region, V new,a represents the operation and maintenance value of the ath region after resource allocation, V old,a represents the operation and maintenance value of the ath region before resource allocation; The operation and maintenance improvement effect indicators of multiple areas are used for weighted aggregation to generate the overall operation and maintenance value assessment results of the building complex, as follows: Among them, V overall represents the overall operation and maintenance value assessment result of the building complex, G represents the total number of regions, represents the weight coefficient of the ath region; Feedback the overall operation and maintenance value assessment results to the management system, and adjust future resource allocation strategies and optimization plans based on the assessment results.

8. An evaluation system for the intelligent operation and maintenance management value of a public building complex, based on the evaluation method for the intelligent operation and maintenance management value of a public building complex according to any one of claims 1 to 7, characterized in that: Also includes: Data collection module, which is used to install crowd flow and energy consumption monitoring equipment in key areas of the building complex, collect crowd density and energy consumption data in real time, and transmit the data to the data center through the Internet of Things; The characteristic value calculation and standardization module is used to perform weighted calculation and standardization on the collected traffic density and energy consumption data to generate standardized density and energy consumption indicators; The operation and maintenance value assessment module is used to calculate the regional operation and maintenance pressure coefficient based on standardized density and energy consumption indicators, and generate the operation and maintenance value impact factor in combination with historical operation and maintenance data; The demand forecasting and resource priority module is used to predict the operation and maintenance demand of each area based on the operation and maintenance value influencing factors and time series models, generate a list of high-demand and high-energy consumption areas, and comprehensively calculate the operation and maintenance demand score; The resource allocation and effect feedback module is used to allocate and schedule resources according to priority, generate an operation and maintenance effect analysis report by recording resource usage, and calculate the overall operation and maintenance value of the building complex based on the operation and maintenance improvement effect.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for evaluating the value of intelligent operation and maintenance management of a public building complex as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the value of intelligent operation and maintenance management of a public building complex as described in any one of claims 1 to 7 are implemented.

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