A building energy consumption monitoring management system and method

Abstract

The present invention discloses a building energy consumption monitoring and management system and method, which relate to the technical field of energy consumption monitoring data management, a building energy consumption monitoring platform, a platform data acquisition module, an energy consumption data transmission management module and an equipment fault detection planning module. The building energy consumption monitoring platform is used to monitor, process and display the energy consumption data of a building, and the equipment used for energy consumption data acquisition is monitored for faults. The platform data acquisition module is used to collect the historical update data of the building energy consumption monitoring platform and the historical fault information of the equipment monitored by the platform; the energy consumption data transmission management module is used to establish a platform update prediction model, predict the update time of the platform, and perform transmission management on the temporary data collected by the equipment according to the prediction result; the equipment fault detection planning module is used to screen the target equipment, predict the fault time of the target equipment, and plan the target equipment fault detection method during the platform update period, so as to realize the abnormal condition processing during the platform update period.

Description

Technical Field

[0001] The invention relates to the technical field of energy consumption monitoring data management, and in particular to a building energy consumption monitoring management system and method. Background Art

[0002] The building energy consumption monitoring platform can be used to realize energy consumption monitoring of office buildings and large public buildings. The energy consumption monitoring data can help relevant departments improve the level of energy-saving operation management. The metering device automatically collects energy consumption data and uploads the collected energy consumption data to the building energy consumption monitoring platform. After the platform receives the data uploaded by the metering device, it will organize the data and display it to the user. For example, the energy consumption data of each hour of the queried day is displayed through the daily report interface, and the energy consumption data of each day of the queried month is displayed through the monthly report interface, such as displaying electricity consumption data of each hour or each day. Users can intuitively understand various energy consumption data of the building through the data displayed on the platform, such as electricity data, water data and gas data. In addition, the energy consumption monitoring platform also has the functions of fault monitoring and fault data display for metering devices with energy consumption data collection function, which can help to timely discover abnormal conditions of metering devices and issue early warnings to ensure the normal monitoring of energy consumption data;

[0003] However, the metering device may fail, which will affect the normal collection and uploading of energy consumption data in the building. The building energy consumption monitoring platform also needs to update the program from time to time to maintain the stability of the platform operation. During the platform update process, the metering device may lose communication with the platform, resulting in the data collected by the metering device during the update period may not be uploaded to the platform. After the update is completed, the platform will lack these data, which is not conducive to the platform obtaining complete energy consumption data to analyze energy consumption abnormalities; secondly, the metering device may fail during the platform update. When the metering device fails, energy consumption data cannot be collected, and the platform cannot timely monitor and warn of the failure of the metering device during the update. If the metering device is found to have failed only after the platform update is completed and the operating data of the metering device is received, and an early warning is issued to repair the metering device, since the fault repair also takes a certain amount of time, the device cannot collect energy consumption data and upload it to the platform during the repair process, which prolongs the failure time of the metering device and increases the missing energy consumption data. Summary of the invention

[0004] The purpose of the present invention is to provide a building energy consumption monitoring and management system and method to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions: a building energy consumption monitoring and management system, comprising: a building energy consumption monitoring platform, a platform data acquisition module, an energy consumption data transmission management module and an equipment fault detection planning module;

[0006] The building energy consumption monitoring platform is used to monitor, process and display the energy consumption data of the building, and at the same time perform fault monitoring on the equipment used to collect energy consumption data; the platform data collection module is used to collect the historical update data of the building energy consumption monitoring platform and the historical fault information of the equipment monitored by the platform; the energy consumption data transmission management module is used to establish a platform update prediction model, predict the update time of the platform, and manage the transmission of temporary data collected by the equipment based on the prediction results; the equipment fault detection planning module is used to analyze the abnormal overlap coefficient between the equipment failure condition and the platform update condition, screen out the target equipment, predict the failure time of the target equipment, and plan the target equipment fault detection method during the platform update period based on the prediction results.

[0007] Preferably, the building energy consumption monitoring platform includes an energy consumption data receiving unit, a data processing and display unit, and an equipment fault monitoring unit;

[0008] The energy consumption data of the building is collected through the metering device and uploaded to the energy consumption data receiving unit, the energy consumption data includes electricity consumption data, water data and gas data, and the energy consumption data receiving unit is used to receive the energy consumption data uploaded by the metering device; the data processing and display unit is used to organize the energy consumption data and display the energy consumption data of the building through a page; the equipment fault monitoring unit is used to monitor the operation data of the metering device in real time, and issue an early warning when a fault occurs in the monitored metering device and display the corresponding equipment fault through a page.

[0009] Preferably, the platform data collection module includes a platform update time collection unit and an equipment fault information collection unit; the platform update time collection unit is used to collect the time interval data of previous updates of the building energy consumption monitoring platform and the time data spent on each previous update; the equipment fault information collection unit is used to collect the number of previous failures of different metering devices and the time information of the failures.

[0010] Preferably, the energy consumption data transmission management module includes an update time period prediction unit and a temporary data transmission management unit;

[0011] The update time period prediction unit analyzes the time interval data of the building energy consumption monitoring platform in the past, establishes a platform update prediction model, predicts the start time of the next platform update and the time required for the next update, and generates update time period prediction data;

[0012] The temporary data transmission management unit controls all metering devices to temporarily store the energy consumption data collected during the predicted update time period and not upload it to the building energy consumption monitoring platform. After the platform update is completed, the temporarily stored energy consumption data will be uploaded to the building energy consumption monitoring platform.

[0013] Preferably, the equipment fault detection planning module includes an abnormal coincidence analysis unit, a target equipment screening unit, a target fault time prediction unit and a fault detection planning unit; the abnormal coincidence analysis unit analyzes the number of times the metering device has failed in the past and the time information of the failure, counts the number of times the time of the metering device failure falls within the time period in which the platform has been updated in the past, and analyzes the abnormal coincidence coefficient between the failure conditions of different metering devices and the update conditions of the platform; the target equipment screening unit compares the abnormal coincidence coefficients between the failure conditions of different metering devices and the update conditions of the platform, and screens out the target metering devices that need to be predicted for fault time; the target fault time prediction unit establishes a fault time prediction model, and the fault time prediction The prediction model predicts the failure time of the target metering device selected; the fault detection planning unit determines whether the predicted failure time of the target metering device is within the predicted platform update time period, and performs fault detection planning for the target metering device: if the predicted failure time of the target metering device is within the predicted platform update time period, the target metering device is manually detected during the predicted platform update time period, and if a failure of the target metering device is detected, the target metering device is repaired; if the predicted failure time of the target metering device is not within the predicted platform update time period, it is selected not to perform manual fault detection on the target metering device, and the operation data of the target metering device is monitored in real time after the platform update is completed.

[0014] A building energy consumption monitoring and management method, comprising:

[0015] S01: Monitor, process and display the building’s energy consumption data through the building energy consumption monitoring platform, and perform fault monitoring on the equipment used to collect energy consumption data;

[0016] S02: Collect historical update data of the building energy consumption monitoring platform and historical fault information of equipment monitored by the platform;

[0017] S03: Establish a platform update prediction model to predict the platform update time, and perform transmission management on the temporary data collected by the device based on the prediction results;

[0018] S04: Analyze the abnormal coincidence coefficient between the equipment failure condition and the platform update condition, select the target equipment, predict the failure time of the target equipment, and plan the target equipment failure detection method based on the prediction result.

[0019] Preferably, S01 includes: collecting electricity consumption data, water consumption data and gas consumption data through metering devices and uploading the energy consumption data to a building energy consumption monitoring platform; the building energy consumption monitoring platform organizes the energy consumption data after receiving the data, and displays the organized energy consumption data through a page; the building energy consumption monitoring platform simultaneously monitors the operating data of the metering device in real time, issues a fault warning when a fault is detected in the metering device, and displays a fault in the corresponding metering device through a page.

[0020] Preferably, the S02 includes: collecting the time interval set of previous updates of the building energy consumption monitoring platform as {T1, T2, ...T r}, where T1 represents the time interval between the second update and the first update of the platform. The building energy consumption platform has been updated r+1 times in the past. The time set collected for the platform to update r+1 times in the past is {t1, t2, ...t i ,...t r+1}, t i It represents the time taken by the platform for the i-th update in the past, and collects the number of failures of different metering devices in the past and the time information of the failures.

[0021] Preferably, the S03 includes: retrieving the time interval data of previous updates of the building energy consumption monitoring platform, and establishing a platform update prediction model:

[0022] T r+1 =μ*T r +(1-μ)*D r ;

[0023] The predicted time interval between the platform's r+2th update and r+1th update is T r+1 , where D r It represents the exponential smoothing value of the time interval between the platform's previous update time r+1 and the rth update time, 0<μ<1, μ refers to the smoothing coefficient of the platform update prediction model, and D is calculated according to the following method. r Solve: Calculate the exponential smoothing value D1 of the time interval between the second and first updates of the platform through D1=μ*T1+(1-μ)*[(T1+T2+T3) / 3], calculate the exponential smoothing value D2 of the time interval between the third and second updates of the platform through D2=μ*T1+(1-μ)*D1, calculate D3 through D3=μ*T2+(1-μ)*D2, D3 represents the exponential smoothing value of the time interval between the fourth and third updates of the platform, and so on to gradually solve D r , the time of the platform's r+1th update is T ’ , the predicted time for the platform's r+2th update is T’ +T r+1 , the time taken for the platform to update r+2 is predicted to be t ’ :t ’ =[∑ r+1 i=1 (t i )] / (r+1), the update time period of the generation platform for the r+2th time is [T ’ +T r+1 ,T ’ +T r+1 +t ’ ], control all metering devices will be in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] The energy consumption data collected during the time period is temporarily stored and not uploaded to the building energy consumption monitoring platform. After the platform update is completed, the temporarily stored energy consumption data will be uploaded to the building energy consumption monitoring platform;

[0024] Considering that the building energy consumption platform will perform program updates from time to time, and during the update period the platform cannot receive the energy consumption data uploaded by the metering device and organize and display the data, even if the metering device uploads the energy consumption data during the update period, the platform cannot receive it. In order to reduce unnecessary data upload operations during the platform update period, the historical update time data of the platform is collected through big data technology. By analyzing the historical update time data, a platform update prediction model is established to predict the next update time of the platform, generate the platform update time period prediction data, and dynamically predict the platform update time period. The energy consumption data management method during the update period is planned according to the prediction results: the energy consumption data collected during the update period is temporarily stored and no data upload operation is performed, which is conducive to reducing the number of unnecessary data upload operations of the metering device during the platform update period while avoiding the problem of data loss during the upload process.

[0025] Preferably, the S04 includes: retrieving the number of times a random metering device has failed in the past as a+1, and the time interval set of the time intervals of the corresponding metering device failing in the past as {F1, F2, ...F a}, F aIndicates the time interval between the a+1th failure time of the corresponding metering device and the ath failure time, retrieves the time information of the previous failure of the corresponding metering device, and counts the number of failures of the corresponding metering device in the platform update time period as K. After the platform update is completed, it is found that the energy consumption data that the metering device will temporarily store during the platform update and upload to the platform after the update is completed is missing, then it is determined that the metering device has failed in the corresponding platform update time period in the past. The abnormal coincidence coefficient U between the failure of a random metering device and the update of the platform is calculated according to the following formula j :

[0026] U j =K / (a+1)+1 / [(∑ a v=1 (F v )) / a];

[0027] Among them, F v represents the time interval between the time when the corresponding metering device fails for the v+1th time and the time when the corresponding metering device fails for the vth time. By analyzing the historical fault information of different metering devices, the set of abnormal coincidence coefficients between the fault conditions of different metering devices and the update conditions of the platform is obtained as U={U1, U2, ...U j , ...U m}, compare the abnormal coincidence coefficients, arrange the m metering devices in descending order according to the abnormal coincidence coefficients, and select the first e metering devices after sorting as the target metering devices that need to be predicted for failure time.

[0028] Preferably, the historical failure time interval data set of a random target metering device is retrieved as {H1, H2, ...H c}, the corresponding target metering device has experienced c+1 failures in the past, and a failure time prediction model is established to predict the time when the corresponding target metering device will fail for the c+2th time:

[0029] H c+1 =β*H c +(1-β)*Z c ;

[0030] Among them, 0<β<1, β represents the smoothing coefficient of the failure time prediction model, H c represents the time interval between the c+1th failure and the cth failure of the corresponding target metering device, Z c represents the exponential smoothing value of the time interval between the c+1th failure and the cth failure of the corresponding target metering device, Z c The solution method is similar to D rThe solution method is the same as that of , and the predicted time interval between the c+2th failure and the c+1th failure of the corresponding target metering device is H c+1 , the time when the corresponding target metering device fails for the c+1th time is obtained as h, and the time when the corresponding target metering device fails for the c+2th time is obtained as h+H c+1 , if h+H c+1 In [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] time period, in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] time period, perform manual fault detection on the corresponding target metering device, and if a fault is detected in the corresponding target metering device, perform maintenance on the corresponding target metering device; if h+H c+1 Not in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] During the time period, choose not to perform manual fault detection on the corresponding target metering device, and then perform real-time monitoring of the operating data of the corresponding target metering device after the platform update is completed;

[0031] Considering that abnormal conditions such as failure of metering devices may occur during platform updates, and the platform cannot timely monitor and warn of failures of metering devices during updates, the overlap coefficient of abnormal conditions is first analyzed based on the historical failure data of metering devices. The abnormal overlap coefficient is analyzed in combination with the ratio of the number of failures of metering devices during platform updates to the total number of failures and the interval time data of failures. The higher the ratio, the greater the probability that the corresponding metering device will fail during platform updates. At the same time, considering the frequency of device failures, for devices that occasionally fail, even if the historical ratio is high, it will weaken the possibility of subsequent failures during updates. Therefore, the abnormal overlap coefficient is analyzed in combination with the failure time interval parameter and the target device is screened, which improves the accuracy of the judgment result of whether the subsequent target device failure time is within the platform update time period, predicts the failure time of the target device and plans the fault detection method of the target device. When it is predicted that the subsequent failure time of the target device is within the platform subsequent update time period, personnel are arranged to perform manual fault detection on the target device within the update time period, which solves the problem that the failure data of the metering device cannot be obtained in time during the platform update, resulting in extended failure time of the metering device and increased missing energy consumption data.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention collects, manages and displays the energy consumption data of buildings through the building energy consumption platform, provides energy consumption reference data, and helps relevant departments improve the level of energy-saving management;

[0034] Considering that the building energy consumption platform will update its program irregularly, and during the update period the platform cannot receive the energy consumption data uploaded by the metering device and organize and display the data, the historical update time data of the platform is collected through big data technology. By analyzing the historical update time data, a platform update prediction model is established to predict the time of the next platform update, generate the platform update time period prediction data, and dynamically predict the platform update time period. According to the prediction results, the energy consumption data management method during the update period is planned: the energy consumption data collected during the update period is temporarily stored and no data upload operation is performed, which is conducive to reducing the number of unnecessary data upload operations of the metering device during the platform update period and avoiding the problem of data loss during the upload process;

[0035] The overlap coefficient of abnormal conditions is analyzed based on the historical fault data of the metering device. The abnormal overlap coefficient is analyzed based on the ratio of the number of failures of the metering device during the platform update to the total number of failures and the interval time data of the failures, and the target equipment is screened. The failure time of the target equipment is predicted and the fault detection method of the target equipment is planned. When it is predicted that the subsequent failure time of the target equipment is within the time period of the subsequent platform update, personnel are arranged to perform manual fault detection on the target equipment within the update time period, which solves the problem of not being able to obtain the fault data of the metering device in time during the platform update, resulting in prolonged failure time of the metering device and increased missing energy consumption data. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a structural schematic diagram of a building energy consumption monitoring and management system according to the present invention;

[0037] Figure 2 The present invention is a flow chart of a method for monitoring and managing building energy consumption. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0039] Example 1: Figure 1As shown, this embodiment provides a building energy consumption monitoring and management system, the system includes: a building energy consumption monitoring platform, a platform data acquisition module, an energy consumption data transmission management module and an equipment fault detection planning module;

[0040] The building energy consumption monitoring platform is used to monitor, process and display the building's energy consumption data, and to monitor the equipment used to collect energy consumption data for faults;

[0041] The platform data collection module is used to collect the historical update data of the building energy consumption monitoring platform and the historical fault information of the equipment monitored by the platform;

[0042] The energy consumption data transmission management module is used to establish a platform update prediction model, predict the platform update time, and manage the transmission of temporary data collected by the equipment based on the prediction results;

[0043] The equipment fault detection planning module is used to analyze the abnormal overlap coefficient between the equipment failure condition and the platform update condition, screen out the target equipment, predict the failure time of the target equipment, and plan the target equipment fault detection method during the platform update period based on the prediction results.

[0044] The building energy consumption monitoring platform includes an energy consumption data receiving unit, a data processing and display unit, and an equipment fault monitoring unit;

[0045] The energy consumption data of the building is collected through the metering device and uploaded to the energy consumption data receiving unit, the energy consumption data includes electricity consumption data, water consumption data and gas consumption data, and the energy consumption data receiving unit is used to receive the energy consumption data uploaded by the metering device;

[0046] The data processing and display unit is used to organize the energy consumption data and display the energy consumption data of the building through the page. For example, the energy consumption data of each hour in the queried day is displayed through the daily report interface, and the energy consumption data of each day in the queried month is displayed through the monthly report interface.

[0047] The equipment fault monitoring unit is used to monitor the operating data of the metering device in real time, issue an early warning when a fault occurs in the monitored metering device, and display the corresponding equipment fault through a page.

[0048] The platform data collection module includes a platform update time collection unit and an equipment fault information collection unit;

[0049] The platform update time collection unit is used to collect the time interval data of the previous updates of the building energy consumption monitoring platform and the time data spent on each previous update;

[0050] The equipment failure information collection unit is used to collect the number of failures that occurred in different metering devices in the past and the time information of the failures.

[0051] The energy consumption data transmission management module includes an update time period prediction unit and a temporary data transmission management unit;

[0052] The update time period prediction unit is used to analyze the time interval data of the building energy consumption monitoring platform in the past, establish a platform update prediction model, predict the start time of the next platform update and the time required for the next update, and generate update time period prediction data;

[0053] The temporary data transmission management unit controls all metering devices to temporarily store the energy consumption data collected during the predicted update time period and not upload it to the building energy consumption monitoring platform. After the platform update is completed, the temporarily stored energy consumption data will be uploaded to the building energy consumption monitoring platform.

[0054] The equipment fault detection planning module includes an abnormal coincidence analysis unit, a target equipment screening unit, a target fault time prediction unit and a fault detection planning unit;

[0055] The abnormal coincidence analysis unit is used to analyze the number of previous failures of the metering device and the time information of the failures, and the number of times the failure of the metering device falls within the time period of the platform's previous update is counted, and the abnormal coincidence coefficient between the failure conditions of different metering devices and the update conditions of the platform is analyzed;

[0056] By comparing the abnormal coincidence coefficients between the fault conditions of different metering devices and the update conditions of the platform through the target device screening unit, the target metering devices that need to be predicted for the fault time are screened out;

[0057] Establishing a failure time prediction model through a target failure time prediction unit, and predicting the failure time of the selected target metering device through the failure time prediction model;

[0058] The fault detection planning unit determines whether the predicted fault time of the target metering device is within the predicted platform update time period, and performs fault detection planning for the target metering device: if the predicted fault time of the target metering device is within the predicted platform update time period, perform manual fault detection on the target metering device during the predicted platform update time period, and if a fault is detected in the target metering device, perform maintenance on the target metering device; if the predicted fault time of the target metering device is not within the predicted platform update time period, choose not to perform manual fault detection on the target metering device, and perform real-time monitoring of the operating data of the target metering device after the platform update is completed.

[0059] Example 2: Figure 2 As shown, this embodiment provides a building energy consumption monitoring and management method, which is implemented based on the management system in the embodiment and specifically includes the following steps:

[0060] S01: Monitor, process and display the energy consumption data of the building through the building energy consumption monitoring platform, and perform fault monitoring on the equipment used for energy consumption data collection. Collect electricity consumption data, water consumption data and gas consumption data through metering devices and upload the energy consumption data to the building energy consumption monitoring platform. After receiving the data, the building energy consumption monitoring platform organizes the energy consumption data and displays the organized energy consumption data through the page. The building energy consumption monitoring platform also monitors the operation data of the metering device in real time, issues a fault warning when a fault is detected in the metering device, and displays the corresponding metering device fault through the page;

[0061] S02: Collect historical update data of the building energy consumption monitoring platform and historical fault information of the equipment monitored by the platform. The time interval set of the previous updates of the building energy consumption monitoring platform is {T1, T2, ...T r}, where T1 represents the time interval between the second update and the first update of the platform. The building energy consumption platform has been updated r+1 times in the past. The time set collected for the platform to update r+1 times in the past is {t1, t2, ...t i ,...t r+1}, collect the number of failures of different metering devices in the past and the time information of the failures;

[0062] S03: Establish a platform update prediction model to predict the platform update time, manage the transmission of temporary data collected by the equipment based on the prediction results, retrieve the time interval data of previous updates of the building energy consumption monitoring platform, and establish a platform update prediction model:

[0063] T r+1 =μ*T r +(1-μ)*D r ;

[0064] The predicted time interval between the platform's r+2th update and r+1th update is T r+1 , where D r It represents the exponential smoothing value of the time interval between the platform's previous update time r+1 and the rth update time, 0<μ<1, μ refers to the smoothing coefficient of the platform update prediction model, and D is calculated according to the following method. rSolve: Calculate the exponential smoothing value D1 of the time interval between the second and first updates of the platform through D1=μ*T1+(1-μ)*[(T1+T2+T3) / 3], calculate the exponential smoothing value D2 of the time interval between the third and second updates of the platform through D2=μ*T1+(1-μ)*D1, calculate D3 through D3=μ*T2+(1-μ)*D2, D3 represents the exponential smoothing value of the time interval between the fourth and third updates of the platform, and so on to gradually solve D r For example, the time interval set of the previous updates of the building energy consumption monitoring platform is collected as {T1, T2, T3, T4, T5}={30, 25, 28, 42, 40}, in days. The platform update prediction model is established: T6=μ*40+(1-μ)*D5, and the smoothing coefficient μ=0.4 of the platform update prediction model is set. D1≈29 is calculated by D1=μ*T1+(1-μ)*[(T1+T2+T3) / 3], D2≈29 is calculated by D2=μ*T1+(1-μ)*D1, D3≈27 is calculated by D3=μ*T2+(1-μ)*D2, D4≈27 and D5≈33 are calculated by D4=μ*T3+(1-μ)*D3. Finally, the time interval between the 7th and 6th updates of the platform is predicted to be T6≈36.

[0065] The time when the platform's r+1th update is obtained is T ’ , the predicted time for the platform's r+2th update is T ’ +T r+1 , the time taken for the platform to update r+2 is predicted to be t ’ :t ’ =[∑ r+1 i=1 (t i )] / (r+1), the update time period of the generation platform for the r+2th time is [T ’ +T r+1 ,T ’ +T r+1 +t ’ ], control all metering devices will be in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] The energy consumption data collected during the time period is temporarily stored and not uploaded to the building energy consumption monitoring platform. After the platform update is completed, the temporarily stored energy consumption data will be uploaded to the building energy consumption monitoring platform;

[0066] S04: Analyze the abnormal coincidence coefficient between the equipment failure and the platform update, select the target equipment, predict the failure time of the target equipment, plan the target equipment failure detection method according to the prediction result, retrieve the number of failures of a random metering device in the past as a+1, and the corresponding metering device previous failure time interval set is {F1, F2, ...F a}, F a Indicates the time interval between the a+1th failure time of the corresponding metering device and the ath failure time, retrieves the time information of the previous failure of the corresponding metering device, and counts the number of failures of the corresponding metering device in the platform update time period as K. After the platform update is completed, it is found that the energy consumption data that the metering device will temporarily store during the platform update and upload to the platform after the update is completed is missing, then it is determined that the metering device has failed in the corresponding platform update time period in the past. The abnormal coincidence coefficient U between the failure of a random metering device and the update of the platform is calculated according to the following formula j :

[0067] U j =K / (a+1)+1 / [(∑ a v=1 (F v )) / a];

[0068] Among them, F v represents the time interval between the time when the corresponding metering device fails for the v+1th time and the time when the corresponding metering device fails for the vth time. By analyzing the historical fault information of different metering devices, the set of abnormal coincidence coefficients between the fault conditions of different metering devices and the update conditions of the platform is obtained as U={U1, U2, ...U j , ...U m}, compare the abnormal coincidence coefficients, arrange the m metering devices in descending order according to the abnormal coincidence coefficients, select the first e metering devices after sorting as the target metering devices that need to be predicted for failure time, and retrieve the historical failure time interval data set of a random target metering device as {H1, H2, ...H c}, the corresponding target metering device has experienced c+1 failures in the past, and a failure time prediction model is established to predict the time when the corresponding target metering device will fail for the c+2th time:

[0069] H c+1 =β*H c +(1-β)*Z c ;

[0070] Among them, 0<β<1, β represents the smoothing coefficient of the failure time prediction model, H c represents the time interval between the c+1th failure and the cth failure of the corresponding target metering device, Zc represents the exponential smoothing value of the time interval between the c+1th failure and the cth failure of the corresponding target metering device, Z c The solution method is similar to D r The solution method is the same as that of , and the predicted time interval between the c+2th failure and the c+1th failure of the corresponding target metering device is H c+1 , the time when the corresponding target metering device fails for the c+1th time is obtained as h, and the time when the corresponding target metering device fails for the c+2th time is obtained as h+H c+1 , if h+H c+1 In [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] time period, in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] time period, perform manual fault detection on the corresponding target metering device, and if a fault is detected in the corresponding target metering device, perform maintenance on the corresponding target metering device; if h+H c+1 Not in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] During the time period, choose not to perform manual fault detection on the corresponding target metering device, and then monitor the operating data of the corresponding target metering device in real time after the platform update is completed.

[0071] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A building energy consumption monitoring and management system, characterized in that: The system includes: Building energy consumption monitoring platform, platform data acquisition module, energy consumption data transmission management module and equipment fault detection planning module; The building energy consumption monitoring platform is used to monitor, process and display the energy consumption data of the building, and to monitor the faults of the equipment used for energy consumption data collection; the platform data collection module is used to collect the historical update data of the building energy consumption monitoring platform and the historical fault information of the equipment monitored by the platform; the energy consumption data transmission management module is used to establish a platform update prediction model, predict the update time of the platform, and manage the transmission of temporary data collected by the equipment based on the prediction results; the equipment fault detection planning module is used to analyze the abnormal coincidence coefficient between the fault condition of the equipment and the update condition of the platform, screen out the target equipment, predict the fault time of the target equipment, and plan the target equipment fault detection method during the platform update period based on the prediction results; The equipment fault detection planning module includes an abnormal coincidence analysis unit, a target equipment screening unit, a target fault time prediction unit and a fault detection planning unit; the abnormal coincidence analysis unit analyzes the number of previous faults of the metering device and the time information of the faults, counts the number of times the time of the metering device faults falls within the time period of the platform's previous updates, and analyzes the abnormal coincidence coefficients between the fault conditions of different metering devices and the platform's update conditions; the target equipment screening unit compares the abnormal coincidence coefficients between the fault conditions of different metering devices and the platform's update conditions, and screens out the target metering devices that need to be predicted for fault time; The abnormal coincidence coefficient U between the fault condition of a random metering device and the update condition of the platform is calculated according to the following formula: j : U j =K / (a+1)+1 / [(∑ a v=1 (F v )) / a]; Among them, F v It represents the time interval between the time when the corresponding metering device fails for the v+1th time and the time when it fails for the vth time. The number of failures that the corresponding metering device has experienced in the platform update period in the past is K, and the number of failures that a random metering device has experienced in the past is a+1.

2. A building energy consumption monitoring and management system according to claim 1, characterized in that: The building energy consumption monitoring platform includes an energy consumption data receiving unit, a data processing and display unit, and an equipment fault monitoring unit; The energy consumption data of the building is collected through the metering device and uploaded to the energy consumption data receiving unit, the energy consumption data includes electricity consumption data, water consumption data and gas data, and the energy consumption data receiving unit is used to receive the energy consumption data uploaded by the metering device; the data processing and display unit is used to sort out the energy consumption data and display the energy consumption data of the building through a page; the equipment fault monitoring unit is used to monitor the operation data of the metering device in real time, and issue an early warning when the monitoring metering device fails, and display the corresponding equipment failure through a page; The platform data collection module includes a platform update time collection unit and an equipment fault information collection unit; the platform update time collection unit is used to collect the time interval data of previous updates of the building energy consumption monitoring platform and the time data spent on each previous update; the equipment fault information collection unit is used to collect the number of previous faults of different metering devices and the time information of the faults.

3. A building energy consumption monitoring and management system according to claim 2, characterized in that: The energy consumption data transmission management module includes an update time period prediction unit and a temporary data transmission management unit; The update time period prediction unit analyzes the time interval data of the building energy consumption monitoring platform in the past, establishes a platform update prediction model, predicts the start time of the next platform update and the time required for the next update, and generates update time period prediction data; The temporary data transmission management unit controls all metering devices to temporarily store the energy consumption data collected during the predicted update time period and not upload it to the building energy consumption monitoring platform. After the platform update is completed, the temporarily stored energy consumption data will be uploaded to the building energy consumption monitoring platform.

4. A building energy consumption monitoring and management system according to claim 3, characterized in that: A failure time prediction model is established by the target failure time prediction unit, and the failure time of the selected target metering device is predicted by the failure time prediction model; the fault detection planning unit determines whether the predicted failure time of the target metering device is within the predicted platform update time period, and performs fault detection planning for the target metering device: if the predicted failure time of the target metering device is within the predicted platform update time period, manual fault detection is performed on the target metering device during the predicted platform update time period, and if a fault is detected in the target metering device, the target metering device is repaired; If the predicted failure time of the target metering device is not within the predicted platform update time period, it is selected not to perform manual fault detection on the target metering device, and the operation data of the target metering device is monitored in real time after the platform update is completed.

5. A building energy consumption monitoring and management method, characterized in that: include: S01: Monitor, process and display the building’s energy consumption data through the building energy consumption monitoring platform, and perform fault monitoring on the equipment used to collect energy consumption data; S02: Collect historical update data of the building energy consumption monitoring platform and historical fault information of equipment monitored by the platform; S03: Establish a platform update prediction model to predict the platform update time, and manage the transmission of temporary data collected by the device based on the prediction results; S04: Analyze the abnormal coincidence coefficient between the failure status of the equipment and the update status of the platform, select the target equipment, predict the failure time of the target equipment, and plan the target equipment failure detection method based on the prediction result; The S04 includes: retrieving the number of times a random metering device has failed in the past as a+1, and the time interval set of the previous failure of the corresponding metering device is {F1, F2, ...F a }, F a Indicates the time interval between the a+1th fault time and the ath fault time of the corresponding metering device. Retrieve the time information of the previous faults of the corresponding metering device. Count the number of faults of the corresponding metering device in the platform update time period as K. Calculate the abnormal coincidence coefficient U between the fault situation of a random metering device and the update situation of the platform according to the following formula: j : U j =K / (a+1)+1 / [(∑ a v=1 (F v )) / a]; Among them, F v represents the time interval between the time when the corresponding metering device fails for the v+1th time and the time when the corresponding metering device fails for the vth time. By analyzing the historical fault information of different metering devices, the set of abnormal coincidence coefficients between the fault conditions of different metering devices and the update conditions of the platform is obtained as U={U1, U2, ...U j , ...U m }, compare the abnormal coincidence coefficients, arrange the m metering devices in descending order according to the abnormal coincidence coefficients, and select the first e metering devices after sorting as the target metering devices that need to be predicted for failure time.

6. A building energy consumption monitoring and management method according to claim 5, characterized in that: The S01 includes: collecting electricity consumption data, water consumption data and gas data through metering devices and uploading the energy consumption data to the building energy consumption monitoring platform. The building energy consumption monitoring platform organizes the energy consumption data after receiving the data and displays the organized energy consumption data through a page. The building energy consumption monitoring platform also monitors the operating data of the metering device in real time, issues a fault warning when a fault is detected in the metering device, and displays the corresponding metering device fault through a page.

7. A building energy consumption monitoring and management method according to claim 6, characterized in that: The S02 includes: collecting the time interval set of previous updates of the building energy consumption monitoring platform as {T1, T2, ...T r }, where T1 represents the time interval between the second update and the first update of the platform. The building energy consumption platform has been updated r+1 times in the past. The time set collected for the platform to update r+1 times in the past is {t1, t2, ...t i ,...t r+1 }, t i It represents the time taken for the platform to update for the i-th time in the past, and collects the number of failures of different metering devices in the past and the time information of the failures.

8. A building energy consumption monitoring and management method according to claim 7, characterized in that: The S03 includes: retrieving the time interval data of the previous updates of the building energy consumption monitoring platform, and establishing a platform update prediction model: T r+1 =μ*T r +(1-μ)*D r ; The predicted time interval between the platform's r+2th update and r+1th update is T r+1 , where D r It represents the exponential smoothing value of the time interval between the platform's previous update time r+1 and the rth update time, 0<μ<1, μ refers to the smoothing coefficient of the platform update prediction model, and D is calculated according to the following method. r Solve: Calculate the exponential smoothing value D1 of the time interval between the second update and the first update time of the platform through D1=μ*T1+(1-μ)*[(T1+T2+T3) / 3], calculate the exponential smoothing value D2 of the time interval between the third update and the second update time of the platform through D2=μ*T1+(1-μ)*D1, calculate D3 through D3=μ*T2+(1-μ)*D2, and so on to gradually solve D r , the time of the platform's r+1th update is T ’ , the predicted time for the platform's r+2th update is T ’ +T r+1 , the time taken for the platform to update r+2 is predicted to be t ’ :t ’ =[∑ r+1 i=1 (t i )] / (r+1), the update time period of the generation platform for the r+2th time is [T ’ +T r+1 ,T ’ +T r+1 +t ’ ], control all metering devices will be in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] The energy consumption data collected during the time period will be temporarily stored and not uploaded to the building energy consumption monitoring platform. After the platform update is completed, the temporarily stored energy consumption data will be uploaded to the building energy consumption monitoring platform.

9. A building energy consumption monitoring and management method according to claim 8, characterized in that: The historical failure time interval data set of a random target metering device is {H1, H2, ...H c }, the corresponding target metering device has experienced c+1 failures in the past, and a failure time prediction model is established to predict the time when the corresponding target metering device will fail for the c+2th time: H c+1 =β*H c +(1-β)*Z c ; Among them, 0<β<1, β represents the smoothing coefficient of the failure time prediction model, H c represents the time interval between the c+1th failure and the cth failure of the corresponding target metering device, Z c It represents the exponential smoothing value of the time interval between the c+1th failure of the corresponding target metering device and the cth failure. The time interval between the c+2th failure of the corresponding target metering device and the c+1th failure is predicted to be H. c+1 , the time when the corresponding target metering device fails for the c+1th time is obtained as h, and the time when the corresponding target metering device fails for the c+2th time is obtained as h+H c+1 , if h+H c+1 In [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] time period, in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] time period, perform manual fault detection on the corresponding target metering device, and if a fault is detected in the corresponding target metering device, perform maintenance on the corresponding target metering device; if h+H c+1 Not in [T ’ +T r+1 ,T ’ +T r+1 +t ’ ] During the time period, choose not to perform manual fault detection on the corresponding target metering device, and then monitor the operating data of the corresponding target metering device in real time after the platform update is completed.

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