HVAC Monitoring System Based on Cloud Platform

Abstract

The present invention discloses a heating, ventilation and air conditioning (HVAC) monitoring system based on a cloud platform. The present invention relates to the technical field of air conditioning monitoring. The present invention collects the monitoring values of real-time environmental adjustment indicators and all monitoring parameters of all HVAC devices in the target area by setting an adjustment index unit and a number of device monitoring units. The convection optimization analysis unit determines a number of abnormal analysis data by judging the monitoring values of all monitoring parameters of all HVAC devices in real time, and analyzes all the obtained abnormal analysis data to determine the energy consumption associated devices and the associated energy consumption values of all HVAC devices in the target area with respect to all monitoring parameters. The transmission management unit updates the corresponding transmission key weights based on the energy consumption associated devices and the associated energy consumption values. By this means, the influence of abnormal monitoring parameters on the energy consumption of other HVAC devices is introduced, making the sorting and transmission of the monitoring values of different monitoring parameters 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;

[0003] 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;

[0004] 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;

[0005] In order to solve the above problems, the present invention proposes a solution. Summary of the invention

[0006] 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.

[0007] The present invention provides a HVAC monitoring system based on a cloud platform, comprising:

[0008] 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;

[0009] 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;

[0010] A transmission management unit, which is configured to, after receiving the convection optimization analysis data of the target area, optimize and update the transmission critical weights of all monitoring parameters of all HVAC devices in the target area stored in the transmission management unit according to a preset optimization update rule;

[0011] The transmission management unit is further configured to, after receiving the monitoring values of all monitoring parameters of all HVAC devices in the target area in real time and the environmental regulation indicators, transmit the monitoring values of all monitoring parameters of all HVAC devices in the target area in real time in the order from large to small according to the transmission critical weights of all monitoring parameters of all HVAC devices in the target area currently stored in the transmission management unit.

[0012] Furthermore, the convection optimization analysis unit is also configured to, after receiving the monitoring values of all monitoring parameters of all HVAC devices in the target area in real time and the environmental regulation indicators, determine the monitoring values of all monitoring parameters of all HVAC devices in the target area in real time according to a preset determination rule to obtain the abnormal analysis data of the target area at several moments.

[0013] Furthermore, the environmental regulation indicators include the collected values of several environmental parameters of the target area during pre-regulation and the regulated values of several environmental parameters of the target area, and the environmental parameters include temperature, humidity, air velocity, noise, oxygen content, harmful gas concentration, and particle concentration.

[0014] Furthermore, the optimization update rule for optimizing and updating the transmission critical weights of all monitoring parameters of all HVAC devices in the target area stored in the transmission management unit is as follows:

[0015] S31: Randomly select an HVAC device from all HVAC devices in the target area as the device to be optimized, and randomly select a monitoring parameter from all monitoring parameters of the device to be optimized as the parameter to be optimized;

[0016] S32: Extract from the convection optimization analysis data the associated energy consumption values L1, L2,..., Lk of all energy consumption associated devices of the parameter to be optimized of the device to be optimized compared to the parameter to be optimized of the device to be optimized, where k ≥ 1, and obtain the transmission critical weight M1 of the parameter to be optimized of the device to be optimized currently stored in the transmission management unit;

[0017] S33: Use the formula to calculate and obtain the new transmission critical weight N1 of the parameter to be optimized 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 a preset standard weight balance fraction, and β1 and β2 are respectively preset first optimization proportion factors and second optimization proportion factors;

[0018] It should be noted here that if the energy consumption associated equipment of the monitoring parameter of a certain HVAC equipment is 0, the value of λ1 is 0;

[0019] S34: Select all the monitoring parameters of the equipment to be optimized as the parameters to be optimized in sequence, and calculate and obtain the new transmission critical weights of all the monitoring parameters of the equipment to be optimized after iterative optimization according to S32 to S33;

[0020] S35: Select all the HVAC equipment in the target area as the equipment to be optimized in sequence according to S31 to S34, and calculate and obtain the new transmission critical weights of all the monitoring parameters of all the HVAC equipment in the target area after iterative optimization;

[0021] After calculating the new transmission critical weights of all the monitoring parameters of all the HVAC equipment in the target area after iterative optimization, the transmission management unit updates the transmission critical weights of all the monitoring parameters of all the HVAC equipment stored in the transmission management unit.

[0022] Compared with the prior art, the following beneficial effects are achieved:

[0023] The present invention collects the real-time environmental adjustment indicators and the monitoring values of all the monitoring parameters of all the HVAC equipment in the target area by setting an adjustment index unit and several equipment monitoring units, and sets the transmission management unit to sort and transmit the monitoring values of different monitoring parameters according to the transmission critical weights of each HVAC equipment based on different monitoring parameters after optimization. In this way, the possibility that the monitoring values with high influence cannot be transmitted in time due to problems such as network congestion and transmission delay is reduced;

[0024] The present invention sets a convection optimization analysis unit to determine a number of abnormal analysis data by combining the abnormal thresholds of all the monitoring parameters of all the HVAC equipment stored therein with the real-time environmental adjustment indicators for the monitoring values of all the monitoring parameters of all the HVAC equipment in real time, and analyzes all the abnormal analysis data that only contain one abnormal HVAC equipment among the obtained abnormal analysis data to determine the energy consumption associated equipment and the associated energy consumption values of all the HVAC equipment in the target area related to all the monitoring parameters. And the transmission management unit updates the corresponding transmission critical weights based on the energy consumption associated equipment and the associated energy consumption values. In this way, the influence of the monitoring parameter abnormality on the energy consumption of the other HVAC equipment is introduced, making the sorting and transmission of the monitoring values of different monitoring parameters more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Please refer to Figure 1 , this application provides a heating, ventilation and air conditioning (HVAC) monitoring system based on a cloud platform, including a cloud-side monitoring and management terminal and a local-side monitoring terminal;

[0028] The cloud-side monitoring and management terminal is used to manage the monitoring data of several HVAC devices in the target area on the cloud side. The cloud-side monitoring and management terminal includes a cloud-side data convection unit, a convection optimization analysis unit, and a cloud-side anomaly analysis unit;

[0029] After the cloud-side data convection unit receives the monitoring values of all monitoring parameters of all HVAC devices in the real-time target area and the environmental adjustment indicators transmitted, it transmits them to the convection optimization analysis unit and the cloud-side anomaly analysis unit respectively;

[0030] A pre-trained fault diagnosis model is stored in the cloud-side anomaly analysis unit. In this application, the training data set for training the fault diagnosis model includes fault data and healthy data. Fault data refers to the data collected when the HVAC system fails, and healthy data refers to the data collected when the HVAC system is operating normally;

[0031] In this application, the model training of the fault diagnosis model adopts a supervised learning method, using labeled data to train the model, and the label refers to the fault category;

[0032] In this application, the model evaluation indicators of the fault diagnosis model include accuracy rate, recall rate, and F1 value;

[0033] After the cloud-side anomaly analysis unit receives the monitoring values of all monitoring parameters of all HVAC devices in the target area transmitted, it inputs them into the fault diagnosis model for fault diagnosis;

[0034] The convection optimization analysis unit stores the anomaly thresholds of all monitoring parameters of all HVAC devices in the target area based on a preset number of standard environmental adjustment indicators. After the convection optimization analysis unit receives the monitoring values of all monitoring parameters of all HVAC devices in the real-time target area and the environmental adjustment indicators transmitted, it determines the monitoring values of all monitoring parameters of all HVAC devices in the real-time target area according to the preset determination rules;

[0035] The determination rules for determining the monitoring values of all monitoring parameters of all HVAC devices in the target area at any received moment in combination with the environmental adjustment indicators at that moment are as follows:

[0036] S11: Randomly select one HVAC device from all HVAC devices in the target area as the device to be determined, and randomly select one monitoring parameter from all monitoring parameters of the device to be determined as the parameter to be determined;

[0037] S12: Obtain the abnormal threshold of the parameter to be determined of the device to be determined from the convective optimization analysis unit according to the environmental adjustment indicators at that moment;

[0038] Subtract the abnormal threshold index from the monitoring value of the parameter to be determined of the device to be determined at the current moment, and calculate the absolute value of the result of the subtraction to obtain the abnormal determination value A1 of the parameter to be determined;

[0039] S13: Compare the abnormal determination value A1 with P1. If the abnormal determination value A1 ≥ P1, it is determined that the monitoring value of the parameter to be determined of the device to be determined at that moment is abnormal; otherwise, it is determined that the monitoring value of the parameter to be determined of the device to be determined at that moment is not abnormal and no processing is performed. P1 is a preset standard abnormal difference index;

[0040] S14: Sequentially select all monitoring parameters from all monitoring parameters of the device to be determined as the parameter to be determined, and sequentially perform abnormal determination on the monitoring value of the parameter to be determined at that moment according to S12 to S13;

[0041] S15: Sequentially select all HVAC devices in the target area as the device to be determined, and sequentially perform abnormal determination on the monitoring values of all monitoring parameters of all HVAC devices in the target area at that moment according to S11 to S14, and obtain the HVAC devices and monitoring parameters corresponding to the monitoring values determined to be abnormal at that moment;

[0042] Calibrate all the obtained HVAC devices as the abnormal HVAC devices in the target area at that moment, and calibrate the monitoring parameters corresponding to the monitoring values determined to be abnormal as the abnormal monitoring parameters of the corresponding abnormal HVAC devices in the target area at that moment;

[0043] And extract all the monitoring values of the energy consumption of the remaining HVAC devices except all the abnormal HVAC devices in a backtracking period from the monitoring values of all monitoring parameters of all HVAC devices in the target area at that moment to obtain the abnormal analysis data of the target area at that moment. The abnormal analysis data also includes all the abnormal HVAC devices in the target area at that moment, their corresponding abnormal monitoring parameters, and the environmental adjustment indicators at that moment;

[0044] In this application, a backtracking period is when tracing back B1 time from a previous moment, where B1 is a preset standard traceability duration.

[0045] The convection optimization analysis unit stores the obtained abnormal analysis data of the target area at the moment.

[0046] The convection optimization analysis unit extracts all the abnormal analysis data that contains exactly one abnormal HVAC device from the stored abnormal analysis data of all target areas, and analyzes the extracted abnormal analysis data according to the preset analysis rules. The analysis rules are as follows:

[0047] S21: Traverse all the extracted abnormal analysis data, extract all the abnormal HVAC devices contained therein and remove duplicates from the extracted abnormal HVAC devices. Mark the remaining abnormal HVAC devices after duplicate removal as C1, C2,..., Cc, where c≥1.

[0048] S22: Obtain all the abnormal analysis data with the abnormal HVAC device being C1 from all the extracted abnormal analysis data, and mark them as D1, D2,..., Dd, where d≥1.

[0049] Then, extract all the abnormal monitoring parameters contained therein from all the abnormal analysis data with the abnormal HVAC device being C1 and remove duplicates from them. Mark the remaining abnormal monitoring parameters after duplicate removal as Z1, Z2,..., Zz, where z≥1.

[0050] S23: Traverse the abnormal analysis data D1, D2,..., Dd, extract all the HVAC devices contained therein and remove duplicates from the extracted HVAC devices. Mark the remaining HVAC devices after duplicate removal as E1, E2,..., Ee, where e≥1.

[0051] S24: Obtain all the abnormal analysis data that simultaneously contains the monitored value of the energy consumption of the HVAC device E1 and the abnormal monitoring parameter Z1 from the abnormal analysis data D1, D2,..., Dd, and re-mark the obtained abnormal analysis data as F1, F2,..., Ff in the order of the abnormal analysis data D1, D2,..., Dd, where 1≤f≤d.

[0052] S25: Determine whether there is an associated anomaly between the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 according to the preset associated determination rule. If it is determined that there is an associated anomaly, calibrate the HVAC equipment E1 as the energy consumption associated equipment of the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 and calculate the associated energy consumption value of the HVAC equipment E1 compared with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1. Otherwise, do nothing. The associated determination rule is as follows:

[0053] S251: Mark the monitoring values of the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 as G1, G2,..., Gg in the order of collection time, where 1 ≤ g ≤ B1;

[0054] S252: Compare the monitoring values G1, G2,..., Gg with G in turn, where G is the preset healthy energy consumption value for the HVAC equipment E1 based on the environmental regulation index included in the abnormal analysis data F1:

[0055] If there is a monitoring value greater than or equal to G among the monitoring values G1, G2,..., Gg, it is determined that there is an associated anomaly between the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1. At this time, mark this monitoring value as the abnormal monitoring value and re-mark it as H1. Use the formula to calculate the associated energy consumption change value J1 of the HVAC equipment E1 in the abnormal analysis data F1 compared with the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1. In the formula, I1 and I2 are the collection times corresponding to the monitoring values G1 and the abnormal monitoring value H1 respectively, and ɑ1 and ɑ2 are the preset first balance factor and second balance factor respectively, which are used to adjust different dimensional features to the same calculation dimension for numerical calculation;

[0056] If there is no monitoring value greater than or equal to G among the monitoring values G1, G2,..., Gg, it is determined that there is no associated anomaly between the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, and do nothing;

[0057] S253: Determine in sequence according to S251 to S252 whether there are associated anomalies between the energy consumption of the HVAC equipment E1 in the anomaly analysis data F2, F3, ..., Ff and the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1. After all the determinations are completed, if there are associated anomalies between the energy consumption of the HVAC equipment E1 in f - P2 anomaly analysis data and the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1, then use the sum and average formula to calculate the average value of the sum of the associated energy consumption change values of the HVAC equipment E1 compared to the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1 in the f - P2 anomaly analysis data, recalibrate the average value as the associated energy consumption value of the HVAC equipment E1 compared to the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1, and calibrate the HVAC equipment E1 as the energy consumption associated equipment of the abnormal HVAC equipment C1 related to the anomaly monitoring parameter Z1, where P2 is a preset standard determination anomaly quantity threshold;

[0058] If there are no associated anomalies between the energy consumption of the HVAC equipment E1 in f - P2 anomaly analysis data and the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1, then do not perform any processing;

[0059] S26: Determine in sequence according to S22 to S25 whether there are associated anomalies between the energy consumption of the HVAC equipment E1 and the anomaly monitoring parameters Z2, Z3, ..., Zz of the abnormal HVAC equipment C1. Based on the determination results, obtain several energy consumption associated equipment and their associated energy consumption values of several anomaly monitoring parameters of the abnormal HVAC equipment C1;

[0060] S27: Determine in sequence according to 23 to S26 whether there are associated anomalies between the energy consumption of the HVAC equipment E2, E3, ..., Ee and the anomaly monitoring parameters Z1, Z2, ..., Zz of the abnormal HVAC equipment C1. Based on the determination results, obtain several energy consumption associated equipment and their associated energy consumption values of several anomaly monitoring parameters of the abnormal HVAC equipment C1;

[0061] S28: Calculate and obtain in sequence according to S21 to S27 several energy consumption associated equipment and their associated energy consumption values of several anomaly monitoring parameters of the abnormal HVAC equipment C2, C3, ..., Cc;

[0062] The convection optimization analysis unit generates convection optimization analysis data for the target area according to the calculated several energy consumption associated equipment and their associated energy consumption values of several anomaly monitoring parameters of the abnormal HVAC equipment C1, C2, ..., Cc, and transmits the convection optimization analysis data to the local - side monitoring terminal;

[0063] The local monitoring terminal is used to manage the monitoring values of several HVAC devices in the target area. In this application, the HVAC devices include chillers, cooling towers, air handling units, fan coil units, thermostats, filters, dampers, mufflers, humidifiers, and heat recovery devices;

[0064] The local monitoring terminal includes a transmission management unit, an adjustment index unit, and several device monitoring units. In the transmission management unit, the transmission key weights of all monitoring parameters of all HVAC devices in the target area are pre-stored. The initial transmission key weights are preset by the management personnel according to the importance of the HVAC devices and the data volume of each monitoring parameter;

[0065] After receiving the transmitted convective optimization analysis data, the local monitoring terminal transmits it to the transmission management unit. After receiving the transmitted convective optimization analysis data, the transmission management unit optimizes and updates the transmission key weights of all monitoring parameters of all HVAC devices in the target area stored in the transmission management unit according to the preset optimization update rules. The optimization update steps are as follows:

[0066] S31: Randomly select an HVAC device from all HVAC devices in the target area as the device to be optimized, and randomly select a monitoring parameter from all monitoring parameters of the device to be optimized as the parameter to be optimized;

[0067] S32: Extract from the convective optimization analysis data the associated energy consumption values L1, L2,..., Lk of all energy consumption associated devices of the parameter to be optimized of the device to be optimized compared to the parameter to be optimized of the device to be optimized, where k≥1;

[0068] Obtain the transmission key weight M1 of the parameter to be optimized of the device to be optimized currently stored in the transmission management unit;

[0069] S33: Use the formula to calculate and obtain the new transmission key weight N1 of the parameter to be optimized 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 a preset standard weight balance fraction, and β1 and β2 are preset first optimization proportion factors and second optimization proportion factors respectively;

[0070] It should be noted here that if the energy consumption associated device of the monitoring parameter of a certain HVAC device is 0, the value of λ1 is 0;

[0071] S34: Sequentially select all monitoring parameters of the device to be optimized as the parameters to be optimized, and calculate and obtain the new transmission key weights of all monitoring parameters of the device to be optimized after iterative optimization according to S32 to S33;

[0072] S35: Select all HVAC equipment in the target area as the equipment to be optimized in sequence according to S31 to S34, and calculate and obtain the new transmission critical weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization.

[0073] After calculating the new transmission critical weights of all monitoring parameters of all HVAC equipment in the target area after iterative optimization, the transmission management unit updates the transmission critical weights of all monitoring parameters of all HVAC equipment stored in the transmission management unit.

[0074] The adjustment index unit collects the environmental adjustment indexes in the target area in real time and transmits them to the transmission management unit, where the environmental adjustment indexes include the collected values of several environmental parameters in the target area during pre-adjustment and the adjustment values of several environmental parameters in the target area, and the environmental parameters include temperature, humidity, air velocity, noise, oxygen content, harmful gas concentration, and particle concentration.

[0075] In this application, the adjustment values of several environmental parameters in the target area refer to the target values to be achieved after the HVAC equipment operates.

[0076] One equipment monitoring unit corresponds to monitoring one HVAC equipment in the 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. The selection of the monitoring parameters is made by the management personnel according to the functions of the HVAC equipment. For example, the function of a chiller is to produce chilled water and provide a cold source. Therefore, the monitoring parameters selected by the management personnel based on it include chilled water outlet temperature, chilled water return temperature, refrigerant pressure, and energy consumption.

[0077] In this application, among all the selected monitoring parameters of any HVAC equipment, there is one monitoring parameter that is energy consumption.

[0078] After receiving the monitoring values of all monitoring parameters of all HVAC equipment in the real-time target area and the environmental adjustment indexes transmitted, the transmission management unit transmits them together to the transmission management unit.

[0079] After receiving the monitoring values of all monitoring parameters of all HVAC equipment in the real-time target area and the environmental adjustment indexes transmitted, the transmission management unit transmits the monitoring values of all monitoring parameters of all HVAC equipment in the real-time target area to the cloud-side data convection unit in the order from largest to smallest according to the transmission critical weights of all monitoring parameters of all HVAC equipment stored in the transmission management unit at present, and synchronously transmits the environmental adjustment indexes to the cloud-side data convection unit.

[0080] Some of the data in the above formula are numerically calculated after removing their dimensions. At the same time, the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0081] 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 of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A heating, ventilation, and air conditioning (HVAC) monitoring system based on a cloud platform, characterized in that, Including: A convection optimization analysis unit, which is used to store the abnormal analysis data of the target area at several moments. The abnormal analysis data includes several abnormal HVAC devices and their corresponding abnormal monitoring parameters, environmental regulation indicators, and all the monitoring values of the energy consumption of several HVAC devices in a backtracking period; The convection optimization analysis unit is also used to extract all the abnormal analysis data containing 1 abnormal HVAC device from all the stored abnormal analysis data, and analyze the extracted all abnormal analysis data according to the preset analysis rules to obtain the convection optimization analysis data of the target area; A transmission management unit, which is used to optimize and update the transmission key weights of all the monitoring parameters of all the HVAC devices in the target area stored in the transmission management unit according to the preset optimization update rules after receiving the convection optimization analysis data of the target area; The transmission management unit is also used to transmit the monitoring values of all the monitoring parameters of all the HVAC devices in the real-time target area according to the transmission key weights of all the monitoring parameters of all the HVAC devices in the target area currently stored in the transmission management unit and the environmental regulation indicators, and transmit the monitoring values of all the monitoring parameters of all the HVAC devices in the real-time target area in the order from large to small according to the transmission key weights; The analysis rules for obtaining the convection optimization analysis data of the target area are as follows: S21: Traverse all the extracted abnormal analysis data, extract all the abnormal HVAC devices contained therein and remove duplicates, and mark the remaining abnormal HVAC devices after removing duplicates as C1, C2,..., Cc, c≥1; S22: Obtain all the abnormal analysis data with the abnormal HVAC device C1 from all the extracted abnormal analysis data, mark them as D1, D2,..., Dd, d≥1 respectively, then extract all the abnormal monitoring parameters contained therein from the obtained abnormal analysis data with the abnormal HVAC device C1 and remove duplicates, and mark the remaining abnormal monitoring parameters after removing duplicates as Z1, Z2,..., Zz, z≥1 respectively; S23: Traverse the abnormal analysis data D1, D2,..., Dd, extract all the HVAC devices contained therein and remove duplicates, and mark the remaining HVAC devices after removing duplicates as E1, E2,..., Ee, e≥1 respectively; S24: Obtain all the abnormal analysis data that simultaneously contain the monitoring value of the energy consumption of the HVAC device E1 and the abnormal monitoring parameter Z1 from the abnormal analysis data D1, D2,..., Dd, and re-mark the obtained all the abnormal analysis data as F1, F2,..., Ff in the order of the abnormal analysis data D1, D2,..., Dd, 1≤f≤d; S25: Determine whether there is an associated anomaly between the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 according to the preset associated determination rule. If it is determined that there is an associated anomaly, label the HVAC equipment E1 as the energy consumption associated equipment of the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 and calculate and obtain the associated energy consumption value of the HVAC equipment E1 compared to the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1. Otherwise, do nothing. The associated determination rule is as follows: S251: Mark the monitoring values of the energy consumption of the HVAC equipment E1 in the abnormal analysis data F1 in the order of collection time as G1, G2,..., Gg, where 1 ≤ g ≤ B1; S252: Compare the monitoring values G1, G2, ..., Gg and G in sequence. G is the preset healthy energy consumption value for the HVAC equipment E1 based on the environmental regulation indicators included in the anomaly analysis data F1. If any one of the monitoring values G1, G2, ..., Gg is greater than or equal to G, it is determined that there is an associated anomaly in the energy consumption of the HVAC equipment E1 in the anomaly analysis data F1 compared to the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1. At this time, mark this monitoring value as an abnormal monitoring value and re-mark it as H1, and use the formula Calculate the associated energy consumption change value J1 of the HVAC equipment E1 in the anomaly analysis data F1 compared to the anomaly monitoring parameter Z1 of the abnormal HVAC equipment C1. In the formula, I1 and I2 are the acquisition times corresponding to the monitoring value G1 and the abnormal monitoring value H1 respectively, and ɑ1 and ɑ2 are the preset first balance factor and second balance factor respectively. Otherwise, no processing is performed; S253: Determine whether there is an associated anomaly 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 in sequence according to S251 to S252. After all determinations are completed, if there are f - P2 abnormal analysis data in which the energy consumption of the HVAC equipment E1 compared to the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 all have associated anomalies, then use the sum and average formula to calculate the average value of the sum of the associated energy consumption change values of the HVAC equipment E1 compared to the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1 in the f - P2 abnormal analysis data. Re-label the average value as the associated energy consumption value of the HVAC equipment E1 compared to the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, and label the HVAC equipment E1 as the energy consumption associated equipment related to the abnormal monitoring parameter Z1 of the abnormal HVAC equipment C1, where P2 is the preset standard determination abnormal quantity threshold; S26: Determine whether there is an associated anomaly between the energy consumption of the HVAC equipment E1 and the abnormal monitoring parameters Z2, Z3,..., Zz of the abnormal HVAC equipment C1 in sequence according to S22 to S25, and obtain several energy consumption associated equipment and their associated energy consumption values of several abnormal monitoring parameters of the abnormal HVAC equipment C1 based on the determination results; S27: Determine whether there is an associated anomaly between the energy consumption of the HVAC equipment E2, E3,..., Ee and the abnormal monitoring parameters Z1, Z2,..., Zz of the abnormal HVAC equipment C1 in sequence according to 23 to S26, and obtain several energy consumption associated equipment and their associated energy consumption values of several abnormal monitoring parameters of the abnormal HVAC equipment C1 based on the determination results; S28: Calculate and obtain several energy consumption associated equipment and their associated energy consumption values of several abnormal monitoring parameters of the abnormal HVAC equipment C2, C3,..., Cc in sequence according to S21 to S27. The convection optimization analysis unit generates the convection optimization analysis data of the target area based on the calculated several energy consumption associated equipment and their associated energy consumption values of several abnormal monitoring parameters of the abnormal HVAC equipment C1, C2,..., Cc.

2. The HVAC monitoring system based on a cloud platform according to claim 1, characterized in that, The convection optimization analysis unit is also used to determine the monitoring values of all monitoring parameters of all HVAC devices in the real-time target area according to a preset determination rule after receiving the monitoring values of all monitoring parameters of all HVAC devices in the real-time target area and the environmental regulation indicators, so as to obtain the abnormal analysis data of the target area at several moments.

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

4. The HVAC monitoring system based on the cloud platform according to claim 1, characterized in that, The environmental regulation indicators include the collected values of several environmental parameters in the target area during pre-regulation and the regulated values of several environmental parameters in the target area. The environmental parameters include temperature, humidity, air velocity, noise, oxygen content, harmful gas concentration, and particle concentration.

5. The HVAC monitoring system based on a cloud platform according to claim 1, characterized in that, For any one HVAC device, one of the monitored parameters among all the selected monitored parameters is energy consumption.

6. The HVAC monitoring system based on a cloud platform according to claim 1, wherein The optimization update rule for optimizing and updating the transmission key weights of all monitoring parameters of all HVAC devices in the target area stored in the transmission management unit is as follows: S31: Randomly select an HVAC device from all HVAC devices in the target area as the device to be optimized, and randomly select a monitoring parameter from all the monitoring parameters of the device to be optimized as the parameter to be optimized; S32: Extract from the convection optimization analysis data the associated energy consumption values L1, L2,..., Lk of all the energy consumption associated devices of the parameter to be optimized of the device to be optimized compared with the parameter to be optimized of the device to be optimized, where k≥1, and obtain the transmission key weight M1 of the parameter to be optimized of the device to be optimized currently stored in the transmission management unit; S33: Use the formula to calculate and obtain the new transmission key weight N1 after iterative optimization of the parameter to be optimized of the device to be optimized. In the formula, Ll represents each of the associated energy consumption values L1, L2, ..., Lk, λ1 is a preset standard weight balance fraction, and β1 and β2 are respectively a preset first optimization proportion factor and a second optimization proportion factor; It should be noted here that if the energy consumption associated device of the monitoring parameter of a certain HVAC device is 0, the value of λ1 is 0; S34: Sequentially select all the monitoring parameters of the device to be optimized as the parameter to be optimized, and calculate and obtain the new transmission key weights of all the monitoring parameters of the device to be optimized after iterative optimization according to S32 to S33; S35: Sequentially select all HVAC devices in the target area as the device to be optimized according to S31 to S34, and calculate and obtain the new transmission key weights of all the monitoring parameters of all HVAC devices in the target area after iterative optimization; After calculating the new transmission key weights of all the monitoring parameters of all HVAC devices in the target area after iterative optimization, the transmission management unit updates the transmission key weights of all the monitoring parameters of all HVAC devices stored in the transmission management unit.

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