Heating ventilation air conditioner monitoring system based on cloud platform

Abstract

The invention discloses a heating, ventilating and air conditioning monitoring system based on a cloud platform, and relates to the technical field of air conditioning monitoring. An adjusting index unit and a plurality of equipment monitoring units are arranged to collect real-time environment adjusting indexes and monitoring values of all monitoring parameters of all heating and ventilating equipment in a target area; a convection optimization analysis unit is arranged to judge real-time monitoring values of all monitoring parameters of all heating and ventilation equipment to obtain a plurality of abnormal analysis data; analyzing all obtained abnormal analysis data to determine energy consumption associated equipment and associated energy consumption values of all heating and ventilation equipment related to all monitoring parameters in the target area, and updating corresponding transmission key weights by a transmission management unit based on the energy consumption associated equipment and the associated energy consumption values; the influence of monitoring parameter abnormity on the energy consumption of other heating and ventilation equipment is introduced through the mode, so that the sequencing transmission of monitoring values of different monitoring parameters is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning monitoring, and in particular to a HVAC monitoring system based on a cloud platform. Background Art

[0002] The HVAC monitoring system based on the cloud platform uses the Internet of Things technology to connect the HVAC equipment distributed in every corner of the building to the cloud platform, collect equipment operation status data in real time, and upload it to the cloud platform for centralized processing and analysis, providing strong support for equipment operation and maintenance management, fault warning and energy-saving optimization; In the data transmission link, the transmission priority of monitoring data is currently set mainly based on the importance of HVAC equipment. For example, the data of core cooling and heating equipment and large air-conditioning units that undertake large-area environmental regulation tasks are given higher priority, while the data priority of auxiliary equipment such as small ventilation fans in local areas is relatively low. At the same time, when facing abnormal data, the principle of priority transmission is also followed to ensure that equipment operation abnormalities can be detected in time; However, although different types of abnormal data are derived from abnormal changes in monitoring parameters, they have different impacts on the energy consumption of other HVAC equipment. Under the current transmission priority setting, these two types of abnormal data may be treated equally. During the transmission process, high-impact abnormal data may encounter network congestion, transmission delays, etc. because they are in the same priority queue as low-impact abnormal data, and cannot reach the cloud platform for analysis and processing in time; In order to solve the above problems, the present invention proposes a solution. Summary of the invention

[0003] The purpose of the present invention is to provide a HVAC monitoring system based on a cloud platform in order to solve the problems raised in the above background technology.

[0004] The present invention provides a HVAC monitoring system based on a cloud platform, comprising: Convection optimization analysis unit, used to store abnormal analysis data of target areas at several moments, the abnormal analysis data including several abnormal HVAC equipment and their corresponding abnormal monitoring parameters, environmental adjustment indicators and all monitoring values ​​of energy consumption of several HVAC equipment in a retrospective period; The convection optimization analysis unit is further used to extract all abnormal analysis data including one abnormal HVAC device from all stored abnormal analysis data, and analyze all the extracted abnormal analysis data according to a preset analysis rule to obtain the convection optimization analysis data of the target area; A transmission management unit, configured to optimize and update the transmission key weights of all monitoring parameters of all HVAC equipment in the target area stored in the transmission management unit according to a preset optimization update rule after receiving the convection optimization analysis data of the target area; The transmission management unit is also used to transmit the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area in order from large to small according to the transmission key weights of all monitoring parameters of all HVAC equipment in the target area currently stored in the transmission management unit after receiving the real-time monitoring values ​​of all monitoring parameters and environmental adjustment indicators of all HVAC equipment in the target area.

[0005] Furthermore, the convection optimization analysis unit is also used to determine the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area according to preset judgment rules after receiving the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area and environmental adjustment indicators, to obtain abnormal analysis data of the target area at several moments.

[0006] Furthermore, the environmental adjustment index includes collected values ​​of several environmental parameters of the target area during pre-adjustment and adjusted values ​​of several environmental parameters of the target area, and the environmental parameters include temperature, humidity, air flow rate, noise, oxygen content, harmful gas concentration and particle size concentration.

[0007] Furthermore, the optimization update rule for optimizing and updating the transmission key weights of all monitoring parameters of all HVAC equipment in the target area stored in the transmission management unit is as follows: S31: randomly selecting a HVAC device from all HVAC devices in the target area as the device to be optimized, and randomly selecting a monitoring parameter from all monitoring parameters of the device to be optimized as the parameter to be optimized; S32: extracting the associated energy consumption values ​​L1, L2, ..., Lk of all energy consumption associated devices of the parameters to be optimized of the device to be optimized from the convection optimization analysis data, where k≥1, and obtaining the transmission key weight M1 of the parameters to be optimized of the device to be optimized currently stored in the transmission management unit; S33: Using formula Calculate and obtain the new transmission key weight N1 of the optimized parameters of the device to be optimized after iterative optimization. In the formula, Ll represents each of the associated energy consumption values ​​L1, L2, ..., Lk, λ1 is the preset standard weight balance score, β1 and β2 are respectively the preset first optimization ratio factor and the second optimization ratio factor; It should be noted here that if there is a monitoring parameter energy consumption associated device of a certain HVAC equipment that is 0, the value of λ1 is 0; S34: all monitoring parameters of the device to be optimized are selected in sequence as parameters to be optimized, and new transmission key weights of all monitoring parameters of the device to be optimized are obtained after iterative optimization according to S32 to S33; S35: selecting all HVAC equipment in the target area as equipment to be optimized in sequence according to S31 to S34, and calculating and obtaining new transmission key weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization; The transmission management unit updates the transmission key weights of all monitoring parameters of all HVAC equipment stored in the transmission management unit after calculating new transmission key weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization.

[0008] Compared with the prior art, it has the following beneficial effects: The present invention collects real-time environmental adjustment indicators and monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area by setting an adjustment index unit and several equipment monitoring units, and sets a transmission management unit to sort and transmit the monitoring values ​​of different monitoring parameters according to the transmission key weights of each HVAC equipment based on different monitoring parameters after optimization, thereby reducing the possibility that high-impact monitoring values ​​cannot be transmitted in time due to network congestion, transmission delay and other problems; The present invention sets a convection optimization analysis unit to judge the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment based on the abnormal thresholds of all monitoring parameters of all HVAC equipment stored therein in combination with real-time environmental adjustment indicators to obtain a number of abnormal analysis data, and analyzes all the abnormal analysis data obtained that only contain one abnormal HVAC equipment to determine the energy consumption-related devices and the associated energy consumption values ​​of all HVAC equipment in the target area related to all monitoring parameters, and the transmission management unit updates the corresponding transmission key weights based on the energy consumption-related devices and the associated energy consumption values. In this way, the influence of the abnormal monitoring parameters on the energy consumption of other HVAC equipment is introduced, so that the sorting and transmission of the monitoring values ​​of different monitoring parameters are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0010] 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.

[0011] See also Figure 1 ,This application provides a HVAC monitoring system based on a cloud platform, including a cloud-side monitoring management terminal and a local-side monitoring terminal; The cloud-side monitoring management terminal is used to manage the monitoring data of several HVAC equipment in the target area on the cloud side, and the cloud-side monitoring management terminal includes a cloud-side data convection unit, a convection optimization analysis unit and a cloud-side abnormality analysis unit; The cloud-side data convection unit receives the real-time monitoring values ​​of all monitoring parameters and environmental adjustment indicators of all HVAC equipment in the target area and transmits them to the convection optimization analysis unit and the cloud-side abnormality analysis unit respectively; The cloud-side abnormality analysis unit pre-stores a trained fault diagnosis model. The training data set for training the fault diagnosis model in this application includes fault data and health data. The fault data refers to the data collected when the HVAC system fails, and the health data refers to the data collected when the HVAC system is operating normally. In this application, the model training of the fault diagnosis model adopts a supervised learning method, and the model is trained using labeled data, where the label refers to the fault category; In this application, the model evaluation indicators of the fault diagnosis model include accuracy, recall and F1 value; After receiving the monitoring values ​​of all monitoring parameters of all HVAC equipment in the transmitted target area, the cloud-side abnormal analysis unit inputs them into the fault diagnosis model for fault diagnosis; The convection optimization analysis unit stores abnormal thresholds of all monitoring parameters of all HVAC equipment in the target area based on a number of preset standard environmental adjustment indicators. After receiving the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area and the environmental adjustment indicators, the convection optimization analysis unit determines the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area according to the preset determination rules; The following is a determination rule for determining the monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area at any time in combination with the environmental adjustment index at the time: S11: randomly selecting a HVAC device from all HVAC devices in the target area as the device to be determined, and randomly selecting a monitoring parameter from all monitoring parameters of the device to be determined as the parameter to be determined; S12: acquiring an abnormal threshold value of a parameter to be determined of the device to be determined from the convection optimization analysis unit according to the environmental adjustment index at the moment; Subtract the abnormal threshold index from the monitored value of the parameter to be determined of the device to be determined at the current moment, and calculate the absolute value of the difference result to obtain the abnormal determination value A1 of the parameter to be determined; S13: Compare the abnormal determination values ​​A1 and P1. If the abnormal determination value A1 ≥ P1, it is determined that the monitored value of the parameter to be determined of the device to be determined at the moment is abnormal. Otherwise, it is determined that the monitored value of the parameter to be determined of the device to be determined at the moment is not abnormal, and no processing is performed. P1 is a preset standard abnormal difference index. S14: selecting all monitoring parameters from all monitoring parameters of the device to be determined as parameters to be determined in turn, and performing abnormality determination on the monitoring values ​​of the parameters to be determined at the moment in turn according to S12 to S13; S15: all HVAC equipment in the target area are selected in turn as equipment to be determined, and abnormality determination is performed on the monitored values ​​of all monitoring parameters of all HVAC equipment in the target area at the said moment in turn according to S11 to S14, and the HVAC equipment and monitoring parameters corresponding to the monitored values ​​at the said moment determined to be abnormal are obtained; Marking all the acquired HVAC equipment as abnormal HVAC equipment in the target area at the time, and marking the monitoring parameters corresponding to the monitoring values ​​determined to be abnormal as abnormal monitoring parameters corresponding to the abnormal HVAC equipment in the target area at the time; And extract all monitoring values ​​of energy consumption of all HVAC equipment except all abnormal HVAC equipment within a retrospective period from the monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area at the moment to obtain abnormal analysis data of the target area at the moment, the abnormal analysis data also includes all abnormal HVAC equipment in the target area at the moment and their corresponding abnormal monitoring parameters and environmental adjustment indicators at the moment; In this application, a traceback cycle is when starting from the previous moment and tracing back to the past by B1 time, where B1 is the preset standard traceability time length; The convection optimization analysis unit stores the obtained abnormal analysis data of the target area at the moment; The convection optimization analysis unit extracts all abnormal analysis data containing only one abnormal HVAC device from the stored abnormal analysis data of all target areas, and analyzes all the extracted abnormal analysis data according to a preset analysis rule, and the analysis rule is as follows: S21: Traverse all the extracted abnormal analysis data, extract all the abnormal HVAC equipment contained therein and remove duplicates from all the extracted abnormal HVAC equipment, and mark all the remaining abnormal HVAC equipment after removal of duplicates as C1, C2, ..., Cc, where c≥1; S22: Obtain all abnormal analysis data of abnormal HVAC equipment C1 from all extracted abnormal analysis data, and mark them as D1, D2, ..., Dd, where d≥1; Then, all abnormal monitoring parameters contained therein are extracted from all abnormal analysis data of the abnormal HVAC equipment C1 and deduplicated, and all the remaining abnormal monitoring parameters after deduplication are marked as Z1, Z2, ..., Zz, where z≥1; S23: Traverse the abnormal analysis data D1, D2, ..., Dd, extract all HVAC equipment contained therein and remove duplicates from all the extracted HVAC equipment, and mark all the remaining HVAC equipment after removal of duplicates as E1, E2, ..., Ee, where e≥1; S24: Obtain all abnormal analysis data including the monitoring value of the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 from the abnormal analysis data D1, D2, ..., Dd, and re-label all the acquired abnormal analysis data as F1, F2, ..., Ff in the order of the abnormal analysis data D1, D2, ..., Dd, 1≤f≤d; S25: According to the preset correlation determination rule, whether the energy consumption of the HVAC equipment E1 is compared with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 is correlated with abnormality is determined. If it is determined that the correlation is abnormal, the HVAC equipment E1 is marked as the energy consumption associated device of the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 and the associated energy consumption value of the HVAC equipment E1 compared with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 is calculated and obtained. Otherwise, no processing is performed. The correlation determination rule is as follows: S251: Mark the monitored values ​​of the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 as G1, G2, ..., Gg in sequence according to the order of collection time, 1≤g≤B1; S252: Compare the monitored values ​​G1, G2, ..., Gg and G in turn, where G is the healthy energy consumption value preset for the HVAC equipment E1 based on the environmental adjustment index included in the abnormal analysis data F1: If there is a monitoring value greater than or equal to G among the monitoring values ​​G1, G2, ..., Gg, it is determined that the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 is associated with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1. At this time, the monitoring value is calibrated as an abnormal monitoring value and re-labeled as H1. Using the formula Calculate and obtain the associated energy consumption change value J1 of the abnormal monitoring parameter Z1 of the HVAC equipment E1 compared with the abnormal HVAC equipment C1 in the abnormal analysis data F1. In the formula, I1 and I2 are the collection times corresponding to the monitoring value G1 and the abnormal monitoring value H1, respectively. ɑ1 and ɑ2 are the preset first balance factor and the second balance factor, respectively, which are used to adjust the features of different dimensions to the same calculation dimension for numerical calculation; If there is no monitoring value greater than or equal to G among the monitoring values ​​G1, G2, ..., Gg, it is determined that the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 is not associated with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, and no processing is performed; S253: According to S251 to S252, determine whether there is any associated abnormality between the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 in the abnormal analysis data F2, F3, ..., Ff. After all the determinations are completed, if there are f-P2 abnormal analysis data in which the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 are associated with abnormalities, then use the addition and averaging formula to calculate the average value of the sum of the associated energy consumption iteration values ​​of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 in the f-P2 abnormal analysis data, recalibrate the average value as the associated energy consumption value of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, and calibrate the HVAC equipment E1 as the energy consumption associated device of the abnormal HVAC equipment C1 related to the abnormal monitoring parameter Z1, wherein P2 is the preset standard threshold for determining the number of abnormalities; If there is no abnormal correlation between the energy consumption of HVAC equipment E1 and the abnormal monitoring parameter Z1 of abnormal HVAC equipment C1 in the f-P2 abnormal analysis data, no processing is performed; S26: According to S22 to S25, whether the energy consumption of the HVAC equipment E1 is related to the abnormal monitoring parameters Z2, Z3, ..., Zz of the abnormal HVAC equipment C1 is judged in sequence, and based on the judgment result, several energy consumption related devices and their related energy consumption values ​​of several abnormal monitoring parameters of the abnormal HVAC equipment C1 are obtained; S27: According to 23 to S26, the energy consumption of the HVAC equipment E2, E3, ..., Ee is compared with the abnormal monitoring parameters Z1, Z2, ..., Zz of the abnormal HVAC equipment C1 to determine whether there is an associated abnormality, and based on the determination result, several energy consumption associated devices and their associated energy consumption values ​​of several abnormal monitoring parameters of the abnormal HVAC equipment C1 are obtained; S28: according to S21 to S27, several energy consumption related devices and their related energy consumption values ​​of several abnormal monitoring parameters of abnormal HVAC equipment C2, C3, ..., Cc are calculated in sequence; The convection optimization analysis unit generates convection optimization analysis data of the target area according to several energy consumption-related devices and their associated energy consumption values ​​of several abnormal monitoring parameters of the abnormal HVAC equipment C1, C2, ..., Cc calculated, and transmits the convection optimization analysis data to the local monitoring terminal; The local monitoring terminal is used to manage the monitoring values ​​of several HVAC equipment in the target area locally. In this application, the HVAC equipment includes chillers, cooling towers, air handling units, fan coils, thermostats, filters, air valves, mufflers, humidifiers and heat recovery equipment; The local monitoring terminal includes a transmission management unit, an adjustment index unit and several equipment monitoring units. The transmission management unit pre-stores the transmission key weights of all monitoring parameters of all HVAC equipment in the target area. The initial transmission key weights are preset by the management personnel according to the importance of the HVAC equipment and the data volume of each monitoring parameter. After receiving the transmitted convection optimization analysis data, the local monitoring end transmits it to the transmission management unit. After receiving the transmitted convection optimization analysis data, the transmission management unit optimizes and updates the transmission key weights of all monitoring parameters of all HVAC equipment in the target area stored in the transmission management unit according to the preset optimization update rules. The optimization and update steps are as follows: S31: randomly selecting a HVAC device from all HVAC devices in the target area as the device to be optimized, and randomly selecting a monitoring parameter from all monitoring parameters of the device to be optimized as the parameter to be optimized; S32: extracting from the convection optimization analysis data all energy consumption-related devices of the device to be optimized parameters compared to the associated energy consumption values ​​L1, L2, ..., Lk of the device to be optimized parameters, k≥1; Acquire the transmission key weight M1 of the to-be-optimized parameter of the to-be-optimized device currently stored in the transmission management unit; S33: Using formula Calculate and obtain the new transmission key weight N1 of the optimized parameters of the device to be optimized after iterative optimization. In the formula, Ll represents each of the associated energy consumption values ​​L1, L2, ..., Lk, λ1 is the preset standard weight balance score, β1 and β2 are respectively the preset first optimization ratio factor and the second optimization ratio factor; It should be noted here that if there is a monitoring parameter energy consumption associated device of a certain HVAC equipment that is 0, the value of λ1 is 0; S34: all monitoring parameters of the device to be optimized are selected in sequence as parameters to be optimized, and new transmission key weights of all monitoring parameters of the device to be optimized are obtained after iterative optimization according to S32 to S33; S35: selecting all HVAC equipment in the target area as equipment to be optimized in sequence according to S31 to S34, and calculating and obtaining new transmission key weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization; The transmission management unit updates the transmission key weights of all monitoring parameters of all HVAC equipment stored in the transmission management unit after calculating and obtaining new transmission key weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization; The adjustment index unit collects the environmental adjustment index in the target area in real time and transmits it to the transmission management unit, wherein the environmental adjustment index includes the collected values ​​of several environmental parameters of the target area during pre-adjustment and the adjusted values ​​of several environmental parameters of the target area, wherein the environmental parameters include temperature, humidity, air flow rate, noise, oxygen content, harmful gas concentration and particle size concentration; In this application, the adjustment values ​​of several environmental parameters in the target area refer to the target values ​​that need to be achieved after the HVAC equipment is working; One of the equipment monitoring units corresponds to a HVAC equipment in the monitoring target area. The equipment monitoring unit collects the monitoring values ​​of several monitoring parameters of the corresponding HVAC equipment in real time and transmits them to the transmission management unit, wherein the monitoring parameters are selected by the management personnel according to the functions of the HVAC equipment. For example, the function of the chiller is to produce cold water and provide a cold source, so the management personnel select the monitoring parameters including the cold water outlet temperature, the cold water return temperature, the refrigerant pressure and the energy consumption; In this application, any HVAC equipment has one monitoring parameter of energy consumption among all the selected monitoring parameters; The transmission management unit receives the monitored values ​​of all monitoring parameters of all HVAC equipment in the real-time target area and the environmental adjustment index and transmits them together to the transmission management unit; After receiving the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area and the environmental adjustment index, the transmission management unit transmits the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area to the cloud-side data convection unit according to the transmission key weights of all monitoring parameters of all HVAC equipment in the target area currently stored in the transmission management unit, and transmits the environmental adjustment index to the cloud-side data convection unit in descending order of the transmission key weights, and simultaneously transmits the environmental adjustment index to the cloud-side data convection unit; Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0012] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The HVAC monitoring system based on the cloud platform is characterized by: include: Convection optimization analysis unit, used to store abnormal analysis data of target areas at several moments, the abnormal analysis data including several abnormal HVAC equipment and their corresponding abnormal monitoring parameters, environmental adjustment indicators and all monitoring values ​​of energy consumption of several HVAC equipment in a retrospective period; The convection optimization analysis unit is further used to extract all abnormal analysis data including one abnormal HVAC device from all stored abnormal analysis data, and analyze all the extracted abnormal analysis data according to a preset analysis rule to obtain the convection optimization analysis data of the target area; A transmission management unit, configured to optimize and update the transmission key weights of all monitoring parameters of all HVAC equipment in the target area stored in the transmission management unit according to a preset optimization update rule after receiving the convection optimization analysis data of the target area; The transmission management unit is also used to transmit the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area in order from large to small according to the transmission key weights of all monitoring parameters of all HVAC equipment in the target area currently stored in the transmission management unit after receiving the real-time monitoring values ​​of all monitoring parameters and environmental adjustment indicators of all HVAC equipment in the target area.

2. The HVAC monitoring system based on the cloud platform according to claim 1, characterized in that: The convection optimization analysis unit is also used to determine the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area according to preset judgment rules after receiving the real-time monitoring values ​​of all monitoring parameters of all HVAC equipment in the target area and environmental adjustment indicators, and obtain abnormal analysis data of the target area at several moments.

3. The HVAC monitoring system based on the cloud platform according to claim 1, characterized in that: The convection optimization analysis unit stores abnormal thresholds of all monitoring parameters of all HVAC equipment in the target area based on a number of preset standard environmental adjustment indicators.

4. The HVAC monitoring system based on the cloud platform according to claim 1, characterized in that: The environmental adjustment index includes the collected values ​​of several environmental parameters of the target area during pre-adjustment and the adjusted values ​​of several environmental parameters of the target area. The environmental parameters include temperature, humidity, air flow rate, noise, oxygen content, concentration of harmful gases and particle size concentration.

5. The HVAC monitoring system based on the cloud platform according to claim 1, characterized in that: Among all the selected monitoring parameters of any HVAC equipment, one of the monitoring parameters is energy consumption.

6. The HVAC monitoring system based on the cloud platform according to claim 1, characterized in that: The analysis rules for the convection optimization analysis data of the target area are as follows: S21: Traverse all the extracted abnormal analysis data, extract all abnormal HVAC equipment contained therein and remove duplicates, and mark all the abnormal HVAC equipment remaining after removal of duplicates as C1, C2, ..., Cc, where c≥1; S22: Obtain all abnormal analysis data of the abnormal HVAC equipment C1 from all the extracted abnormal analysis data, mark them as D1, D2, ..., Dd, d≥1, then extract all abnormal monitoring parameters contained in all the abnormal analysis data of the abnormal HVAC equipment C1 and remove duplicates, and mark all the remaining abnormal detection parameters after removal of duplicates as Z1, Z2, ..., Zz, z≥1; S23: Traverse the abnormal analysis data D1, D2, ..., Dd, extract all HVAC equipment contained therein and remove duplicates, and mark all remaining HVAC equipment after removing duplicates as E1, E2, ..., Ee, where e≥1; S24: Obtain all abnormal analysis data including the monitoring value of the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 from the abnormal analysis data D1, D2, ..., Dd, and re-label all the acquired abnormal analysis data as F1, F2, ..., Ff in the order of the abnormal analysis data D1, D2, ..., Dd, 1≤f≤d; S25: according to the preset correlation determination rule, whether the energy consumption of the HVAC equipment E1 is correlated with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 is correlated with abnormality, if it is determined that the correlation is abnormal, the HVAC equipment E1 is marked as the energy consumption associated device of the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, and the associated energy consumption value of the HVAC equipment E1 compared with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 is calculated, otherwise, no processing is performed; S26: According to S22 to S25, whether the energy consumption of the HVAC equipment E1 is related to the abnormal monitoring parameters Z2, Z3, ..., Zz of the abnormal HVAC equipment C1 is judged in sequence, and based on the judgment result, several energy consumption related devices and their related energy consumption values ​​of several abnormal monitoring parameters of the abnormal HVAC equipment C1 are obtained; S27: According to 23 to S26, the energy consumption of the HVAC equipment E2, E3, ..., Ee is compared with the abnormal monitoring parameters Z1, Z2, ..., Zz of the abnormal HVAC equipment C1 to determine whether there is an associated abnormality, and based on the determination result, several energy consumption associated devices and their associated energy consumption values ​​of several abnormal monitoring parameters of the abnormal HVAC equipment C1 are obtained; S28: According to S21 to S27, several energy consumption-related devices and their associated energy consumption values ​​of several abnormal monitoring parameters of abnormal HVAC equipment C2, C3, ..., Cc are calculated in sequence, and the convection optimization analysis unit generates convection optimization analysis data of the target area according to the several energy consumption-related devices and their associated energy consumption values ​​of several abnormal monitoring parameters of abnormal HVAC equipment C1, C2, ..., Cc obtained by calculation.

7. The HVAC monitoring system based on the cloud platform according to claim 6, characterized in that: S25, the association determination rules are as follows: S251: Mark the monitored values ​​of the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 as G1, G2, ..., Gg in sequence according to the order of collection time, 1≤g≤B1; S252: Compare the monitored values ​​G1, G2, ..., Gg and G in turn, where G is the healthy energy consumption value preset for the HVAC equipment E1 based on the environmental adjustment index included in the abnormal analysis data F1: If any of the monitoring values ​​G1, G2, ..., Gg is greater than or equal to G, it is determined that the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 is associated with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1. At this time, the monitoring value is calibrated as an abnormal monitoring value and re-labeled as H1. Using the formula Calculate and obtain the associated energy consumption change value J1 of the abnormal monitoring parameter Z1 of the HVAC equipment E1 compared with the abnormal HVAC equipment C1 in the abnormal analysis data F1. In the formula, I1 and I2 are the collection times corresponding to the monitoring value G1 and the abnormal monitoring value H1, respectively. ɑ1 and ɑ2 are the preset first balance factor and the second balance factor, respectively. Otherwise, no processing is performed. S253: According to S251 to S252, determine whether there is any associated abnormality between the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 in the abnormal analysis data F2, F3, ..., Ff. After all the determinations are completed, if there are f-P2 abnormal analysis data in which the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 are associated with abnormalities, then use the addition and averaging formula to calculate the average value of the sum of the associated energy consumption change values ​​of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 in the f-P2 abnormal analysis data, recalibrate the average value to the associated energy consumption value of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, and calibrate the HVAC equipment E1 as an energy consumption associated device of the abnormal HVAC equipment C1 related to the abnormal monitoring parameter Z1, where P2 is the preset standard abnormality determination quantity threshold.

8. The HVAC monitoring system based on a cloud platform according to claim 1, characterized in that: The optimization update rule for optimizing and updating the transmission key weights of all monitoring parameters of all HVAC equipment in the target area stored in the transmission management unit is as follows: S31: randomly selecting a HVAC device from all HVAC devices in the target area as the device to be optimized, and randomly selecting a monitoring parameter from all monitoring parameters of the device to be optimized as the parameter to be optimized; S32: extracting the associated energy consumption values ​​L1, L2, ..., Lk of all energy consumption associated devices of the parameters to be optimized of the device to be optimized from the convection optimization analysis data, where k≥1, and obtaining the transmission key weight M1 of the parameters to be optimized of the device to be optimized currently stored in the transmission management unit; S33: Using formula Calculate and obtain the new transmission key weight N1 of the optimized parameters of the device to be optimized after iterative optimization. In the formula, Ll represents each of the associated energy consumption values ​​L1, L2, ..., Lk, λ1 is the preset standard weight balance score, β1 and β2 are respectively the preset first optimization ratio factor and the second optimization ratio factor; It should be noted here that if there is a monitoring parameter energy consumption associated device of a certain HVAC equipment that is 0, the value of λ1 is 0; S34: all monitoring parameters of the device to be optimized are selected in sequence as parameters to be optimized, and new transmission key weights of all monitoring parameters of the device to be optimized are obtained after iterative optimization according to S32 to S33; S35: selecting all HVAC equipment in the target area as equipment to be optimized in sequence according to S31 to S34, and calculating and obtaining new transmission key weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization; The transmission management unit updates the transmission key weights of all monitoring parameters of all HVAC equipment stored in the transmission management unit after calculating new transmission key weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization.

Images