Energy consumption balance calculation method based on indoor environment big data

Abstract

The present application relates to the field of indoor environment monitoring and building energy consumption control, in particular to an energy consumption balance calculation method based on indoor environment big data, comprising: an indoor environment monitoring node collects an environmental temperature relative change amount and generates an asynchronous event data packet containing an event generation timestamp when a dynamic trigger threshold is reached; a bottom layer regulating device forms an actual running state data packet containing a device sampling timestamp according to a polling period; a space area control node establishes a demand time sequence queue according to a space area mapping, constructs a heat load demand record according to an event generation timestamp and a timestamp, and calculates an initial heat load demand value; the space area effective energy supply is calculated according to a device actual running power, a polling period length and a device performance coefficient, and supply-demand matching and effective period verification are sequentially performed, residual demand values and overtime demand values are counted, device adjustment instructions are generated, and correction parameters are fed back; the present application realizes more timely identification and disposal of overtime demand and abnormal states.

Description

Technical Field

[0001] This invention relates to the field of indoor environmental monitoring and building energy consumption control, specifically to an energy balance calculation method based on big data of indoor environment. Background Technology

[0002] Existing indoor environmental energy consumption balance control systems typically include indoor environmental monitoring nodes, spatial area control nodes, and underlying regulation equipment. In existing indoor environmental change and equipment power supply status linkage control technologies, it is usually necessary to collect, sort, and match monitoring data and equipment operation data, and output regulation commands accordingly. However, when indoor environmental monitoring adopts event triggering, equipment side adopts periodic polling, and network transmission has delays, it is difficult to keep the data arrival order consistent, and demand records will also decay during the waiting process, resulting in data deviation in the supply and demand matching process, and it is impossible to execute the response logic for timed-out demands and abnormal states within the preset communication cycle. Summary of the Invention

[0003] The purpose of this invention is to provide an energy balance calculation method based on big data of indoor environment, solving the problem of low supply-demand matching accuracy caused by data asynchrony and demand decay, improving matching accuracy, and handling overdue demands and abnormal states in a timely manner. Specifically, the technical solution of this invention includes:

[0004] An energy balance calculation method based on indoor environmental big data is applied to a target building environment equipped with an energy balance control system. The target building environment is divided into multiple spatial regions, and the energy balance control system includes indoor environmental monitoring nodes and underlying regulation devices corresponding to each spatial region.

[0005] Obtain the system configuration parameters of the energy consumption balance control system;

[0006] Acquire asynchronous event data packets from the indoor environmental monitoring node and actual operating status data packets from the underlying control device;

[0007] Obtain the mapping relationship between the indoor environment monitoring node, the underlying adjustment device, and the spatial area. Based on the mapping relationship, associate the asynchronous event data packet and the actual operating status data packet with the corresponding spatial area, and establish a demand time sequence queue according to the spatial area.

[0008] Based on the asynchronous event data packet, a hot load demand record is constructed and stored in the demand time sequence queue corresponding to the spatial region;

[0009] Based on the system configuration parameters and the actual operating status data packet, the effective energy supply of the spatial area is calculated, and the supply and demand matching calculation is performed according to the time sequence and the heat load demand record, and candidate matching records are output.

[0010] Perform time-series logic verification and validity period constraint verification on the candidate matching records to generate valid residual demand records and an exception pending processing queue;

[0011] Based on the effective residual demand record and the abnormal pending queue, the system energy consumption deviation is determined, and the equipment adjustment command is output according to the system energy consumption deviation.

[0012] The system collects actual operating status parameters generated by the device's adjustment commands and uses these parameters to correct the system configuration parameters.

[0013] Preferably, the system configuration parameters include: space thermal inertia compensation coefficient, effective time window of initial demand, dynamic triggering threshold, space environment physical parameters, space diffusion attenuation factor, equipment performance coefficient, energy distribution coefficient, power regulation threshold, and running time threshold.

[0014] Preferably, acquiring the asynchronous event data packets of the indoor environmental monitoring node and the actual operating status data packets of the underlying control device includes:

[0015] The asynchronous event data packet should at least include the indoor environmental monitoring node identifier and the event generation timestamp, while the actual operating status data packet should at least include the device identifier, actual operating power, polling cycle duration, and device sampling timestamp.

[0016] The indoor environment monitoring node collects the relative change in ambient temperature and generates the asynchronous event data packet when the relative change in ambient temperature is greater than the dynamic trigger threshold. The asynchronous event data packet also includes the relative change in ambient temperature. The event generation timestamp is a time series parameter generated by the indoor environment monitoring node based on a real-time clock.

[0017] The actual operating status data packet is obtained through the underlying adjustment device. The actual operating status data packet also includes the device sampling timestamp that increases monotonically on the device side.

[0018] Preferably, constructing a hot load demand record based on the asynchronous event data packet and storing it in the demand time-series queue corresponding to the spatial region includes:

[0019] The initial heat load demand value is calculated based on the asynchronous event data packet. The initial heat load demand value, the event generation timestamp, and the initial demand effective time window are combined to construct a heat load demand record containing the identifier of the corresponding spatial region. The heat load demand record is then stored in the demand time sequence queue of the corresponding spatial region.

[0020] The heat load demand is recorded in the demand time queue with a waiting time. Based on the spatial diffusion attenuation factor, the real-time heat load demand value is determined and dynamically updated with the waiting time.

[0021] Preferably, based on the system configuration parameters and the actual operating status data packet, the effective energy supply of the spatial region is calculated, and a supply-demand matching calculation is performed according to the time sequence and the heat load demand record, outputting candidate matching records, including:

[0022] Extract the device performance coefficient and energy distribution coefficient from the system configuration parameters;

[0023] Based on the actual operating power, the polling cycle duration, the equipment performance coefficient, and the energy allocation coefficient, energy supply calculation is performed to determine the effective energy supply for the corresponding spatial area.

[0024] Within the corresponding spatial area, the heat load demand records in the demand time sequence queue are read in ascending order according to the event generation timestamp; when multiple heat load demand records have the same event generation timestamp, they are read in ascending order according to the numerical value of the indoor environmental monitoring node identifier corresponding to the asynchronous event data packet.

[0025] Through supply and demand matching calculation, the effective energy supply of the spatial area is matched with the real-time heat load demand value, and the candidate matching record containing the residual demand value is output.

[0026] Preferably, sequential logic verification and validity period constraint verification are performed on the candidate matching records to generate valid residual demand records and an exception pending handling queue, including:

[0027] Perform a timing logic check on the candidate matching records to confirm that the device sampling timestamp is later than the event generation timestamp corresponding to the heat load demand record;

[0028] Establish validity period constraints based on the initial demand validity time window, and perform demand validity status analysis based on the waiting time of the heat load demand record corresponding to the candidate matching record;

[0029] When the waiting time of the heat load demand record corresponding to the candidate matching record exceeds the initial demand effective time window, the verification is determined to be unsuccessful, the corresponding heat load demand record is transferred to the abnormal pending queue, and the real-time heat load demand value corresponding to the transferred heat load demand record is recorded as the timed-out demand value. The supply and demand matching calculation is re-executed for the candidate matching records that have not timed out.

[0030] When the waiting time of the heat load demand record corresponding to the candidate matching record is less than or equal to the initial demand effective time window, a valid residual demand record containing residual demand value is generated.

[0031] Preferably, based on the effective residual demand record and the abnormal pending queue, the system energy consumption deviation is determined, and a device adjustment command is output according to the system energy consumption deviation, including:

[0032] Extract the sum of residual demand values ​​from the effective residual demand records and extract the sum of timeout demand values ​​from the abnormal pending queue. Determine the system energy consumption deviation based on the sum of residual demand values ​​and the sum of timeout demand values.

[0033] The number of valid residual demand records is counted as the number of unmatched demand items. The spatial area energy supply compliance rate within the preset historical time window is obtained. Combined with the spatial area energy supply compliance rate, the number of unmatched demand items, the spatial area energy supply compliance rate, and the system energy consumption deviation are used as control input parameters.

[0034] Target power increment and target duration increment are calculated based on the control input parameters. The target power increment and target duration increment are compared with preset power adjustment threshold and running time threshold, respectively. When the target power increment and target duration increment are within the threshold range, a device adjustment command containing the target power increment and target duration increment is output. When the target power increment or target duration increment exceeds the threshold range, an over-limit warning data packet is generated.

[0035] After outputting the device adjustment command, record the operation timestamp and control command identifier.

[0036] Preferably, the actual operating status parameters after the monitoring equipment adjustment command is issued include:

[0037] The data includes the actual operating power, actual operating time or switch status after the data acquisition equipment adjustment command is issued, as well as the target power supply achievement status.

[0038] Calculate the control execution deviation under the current state based on the difference between the actual operating power, actual operating time or switch status and the target operating parameters corresponding to the equipment adjustment command;

[0039] When the control execution deviation exceeds the preset alarm limit within a continuous preset quantity control cycle, the system is judged to be abnormal; when the length change ratio of the abnormal pending queue exceeds the preset percentage threshold, a performance alarm is triggered.

[0040] Preferably, correcting the system configuration parameters based on the feedback of the actual operating status parameters includes:

[0041] System malfunctions and performance alarms are encapsulated as feedback signals; the feedback signals are parsed, and when the feedback signal indicates a system malfunction, the dynamic trigger threshold is decreased by a first preset step size and the effective time window of the initial demand is extended; when the feedback signal indicates a performance alarm, the spatial thermal inertia compensation coefficient is increased by a second preset step size; the adjusted parameter values ​​are periodically written into the system configuration parameters.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] This application improves the matching accuracy between indoor environmental changes and equipment power supply status by performing serialization alignment, demand queue management and validity period verification on asynchronous event data packets and actual operating status data packets in the space area control node, and by combining the linkage adjustment of space thermal inertia compensation coefficient, initial demand validity time window and dynamic trigger threshold. This reduces supply and demand deviations caused by asynchronous arrival, polling delay and demand decay, and enables more timely identification and handling of time-out demands and abnormal states. Attached Figure Description

[0044] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0045] Figure 1 A flowchart illustrating the energy balance calculation method based on indoor environmental big data provided in this application embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0047] Please see Figure 1 An energy balance calculation method based on indoor environmental big data is applied to a target building environment equipped with an energy balance control system. The target building environment is divided into multiple spatial areas, and the energy balance control system includes indoor environmental monitoring nodes and underlying regulation equipment corresponding to each spatial area, including:

[0048] Obtain the system configuration parameters of the energy consumption balance control system;

[0049] Acquire asynchronous event data packets from indoor environmental monitoring nodes and actual operating status data packets from underlying control devices;

[0050] Obtain the mapping relationship between indoor environmental monitoring nodes and underlying control equipment and spatial areas. Based on the mapping relationship, associate asynchronous event data packets and actual operating status data packets with the corresponding spatial areas, and establish demand time sequence queues according to spatial areas.

[0051] A hot load demand record is constructed based on asynchronous event data packets and stored in the demand time sequence queue of the corresponding spatial region;

[0052] Based on the system configuration parameters and actual operating status data packets, the effective energy supply of the spatial area is calculated, and the supply and demand matching calculation is performed according to the time sequence and heat load demand records, and candidate matching records are output.

[0053] Perform sequential logic verification and validity period constraint verification on candidate matching records to generate valid residual demand records and an exception pending handling queue;

[0054] Based on the effective residual demand record and the abnormal pending queue, the system energy consumption deviation is determined, and the equipment adjustment command is output according to the system energy consumption deviation.

[0055] The system collects actual operating status parameters generated by the device adjustment commands and uses these parameters to correct system configuration parameters.

[0056] The calculation method in this embodiment relies on a hardware architecture consisting of indoor environmental monitoring nodes, spatial area control nodes, and underlying adjustment devices.

[0057] System configuration parameters are pre-written into the spatial area control node during the deployment phase and are uniformly read and updated by the spatial area control node. Among them, the spatial thermal inertia compensation coefficient is used to correct the absorption and hysteresis effect of space on temperature changes, the initial demand effective time window is used to limit the effective range of heat load demand records that can participate in matching, and the dynamic trigger threshold is used to control when the indoor environment monitoring node generates asynchronous event data packets.

[0058] When the indoor environmental monitoring node collects the relative change of environmental parameters and the relative change reaches the dynamic trigger threshold, the indoor environmental monitoring node encapsulates the current node identifier, event generation timestamp, and change into an asynchronous event data packet and sends it to the spatial area control node.

[0059] Simultaneously, after the underlying regulating equipment completes the operation sampling within the polling cycle, the equipment-side control unit generates an actual operating status data packet, which includes at least the equipment identifier, actual operating power, polling cycle duration, and equipment sampling timestamp.

[0060] After receiving the two types of data packets, the spatial area control node assigns the indoor environmental monitoring nodes and the underlying regulation equipment to the corresponding spatial areas based on the mapping relationship between the indoor environmental monitoring nodes and the underlying regulation equipment. It also establishes a demand time sequence queue for each spatial area, so that the environmental demand records and equipment power supply records in the same spatial area are processed according to their respective event sequence numbers. The above association operation is represented as time sequence alignment based on sequence numbers at the data processing level.

[0061] The spatial area control node is equipped with a data alignment buffer pool. After receiving asynchronous event data packets and actual running status data packets, the node will store both types of data packets in this buffer pool.

[0062] The system configures a preset global alignment time window for the buffer pool. When the waiting time of any data packet in the pool reaches the global alignment time window, or when the system detects that the event generation timestamp and the device sampling timestamp form an alternating local closed loop in the time series, the system will forcibly trigger the dequeueing, sorting and matching processing of the data in the current buffer pool.

[0063] Based on the monotonically increasing properties of the event generation timestamp and the device sampling timestamp, the spatial area control node arranges the asynchronous event data packets and the actual running status data packets in sequence, thereby achieving alignment of the two types of data packets at the data processing logic level.

[0064] When constructing the heat load demand record, the space area control node reads the relative change in ambient temperature from the asynchronous event data packet and calculates the initial heat load demand value by combining the space environment physical parameters and the space thermal inertia compensation coefficient.

[0065] The node writes the event generation timestamp, initial heat load demand value, real-time heat load demand value, initial demand effective time window and spatial region identifier into the same heat load demand record, and stores the heat load demand record at the end of the demand time sequence queue of the corresponding spatial region.

[0066] The real-time heat load demand value is updated as the waiting time of the record in the queue changes. The real-time heat load demand value decreases as the waiting time in the queue increases, so as to reflect the demand change after the natural diffusion of heat in the space. When the space area control node obtains the actual operating status data packet, it extracts the actual operating power and polling cycle duration, and calculates the effective energy supply of the space area in combination with the system configuration parameters.

[0067] After the calculation results are generated, the system reads the records in the demand time sequence queue according to the event generation timestamps of the heat load demand records from smallest to largest; when the event generation timestamps are the same, the system reads the records according to the event generation timestamps in the asynchronous event data packets.

[0068] After the reading order is determined, the supply and demand matching calculation deducts the real-time heat load demand value in the heat load demand record from the effective energy supply of the space area one by one: if the effective energy supply of the space area is greater than or equal to the real-time heat load demand value of the heat load demand record, it is recorded as a fully matched record, and the fully matched record is marked as completed and removed from the demand time sequence queue; if the effective energy supply of the space area is less than the real-time heat load demand value of the heat load demand record, it is recorded as a partially matched record, and the difference between the real-time heat load demand value and the effective energy supply of the space area is recorded as the residual demand value. The partially matched record containing the residual demand value is converted into an effective residual demand record and retained.

[0069] During the validity period constraint verification phase, the spatial area control node checks the relationship between the device sampling timestamp and the event generation timestamp for each candidate matching record, and at the same time, combines the waiting time of the record in the queue with the initial demand validity time window to determine whether it is still within the validity range.

[0070] If the heat load demand record exceeds the initial demand validity time window, the verification is deemed to have failed, the corresponding heat load demand record is transferred to the abnormal pending queue, and the current real-time heat load demand value of the heat load demand record is recorded as the timeout demand value; if the record simultaneously meets the timing logic verification and validity period requirements, a valid residual demand record is formed.

[0071] The spatial area control node calculates the sum of residual demand values ​​in the effective residual demand records and the sum of timed-out demand values ​​in the abnormal pending queue. Both are used as the basis for calculating the system energy consumption deviation. Then, combined with the number of currently unmatched demand items and the spatial area energy supply compliance rate, the adjustment amount of equipment operating parameters is calculated, and corresponding equipment adjustment instructions are generated and sent to the underlying adjustment equipment.

[0072] After receiving the equipment adjustment command, the underlying adjustment equipment adjusts the actual operating power, actual operating time or switch status, and sends the execution results back to the space area control node. The space area control node corrects the space thermal inertia compensation coefficient, the effective time window of initial demand and the dynamic trigger threshold based on the feedback results, so that subsequent event triggering, demand formation and supply and demand matching can fit the current space status.

[0073] Acquire asynchronous event data packets from indoor environmental monitoring nodes and actual operating status data packets from underlying control equipment, including:

[0074] The asynchronous event data packet should at least include the indoor environmental monitoring node identifier and the event generation timestamp, while the actual operating status data packet should at least include the device identifier, actual operating power, polling cycle duration, and device sampling timestamp.

[0075] The relative change in ambient temperature is collected by the indoor environmental monitoring node, and an asynchronous event data packet is generated when the relative change in ambient temperature is greater than the dynamic trigger threshold. The asynchronous event data packet also contains the relative change in ambient temperature. The event generation timestamp is a timing parameter generated by the indoor environmental monitoring node based on a real-time clock.

[0076] The actual operating status data packet is obtained through the underlying adjustment device. The actual operating status data packet also includes the device sampling timestamp that increases monotonically on the device side.

[0077] In this embodiment, the indoor environment monitoring node consists of a sensor acquisition unit and an event triggering unit; the sensor acquisition unit continuously reads environmental parameters such as temperature and calculates the change between adjacent sampling results as a relative change.

[0078] The event triggering unit compares the relative change with the dynamic triggering threshold: when the relative change is less than or equal to the dynamic triggering threshold, the node only retains the current sampled value and does not generate an event data packet; when the relative change is greater than the dynamic triggering threshold, the node immediately encapsulates the change into an asynchronous event data packet.

[0079] The asynchronous event data packet contains the indoor environmental monitoring node identifier, the relative change in ambient temperature, and the event generation timestamp. The event generation timestamp is generated monotonically by the indoor environmental monitoring node according to the sampling trigger order, and together with the event generation timestamp, it is used to indicate the chronological relationship of the event in the global time sequence.

[0080] On the underlying regulating equipment side, the equipment controller collects the actual operating power and operating status during its polling cycle, forming an actual operating status data packet. In addition to the equipment identifier, actual operating power, and polling cycle duration, this data packet also contains a monotonically increasing equipment sampling timestamp from the equipment side, which is used to indicate the sequence of equipment power supply events.

[0081] After receiving the two types of data packets, the spatial area control node assigns the indoor environmental monitoring node identifier and the device identifier to the corresponding spatial area according to the preset mapping relationship between the indoor environmental monitoring node and the underlying regulation equipment and the spatial area. It also supplements the spatial area identifier for the two types of data packets, so that the subsequent demand records and energy supply records are processed in the queue of the same spatial area. This allows the indoor environmental change parameters and the equipment operating status parameters to establish a data association in the same spatial area based on the corresponding sequence number.

[0082] A hot load demand record is constructed based on asynchronous event data packets and stored in the demand time-series queue of the corresponding spatial region, including:

[0083] The initial heat load demand value is calculated based on asynchronous event data packets. The initial heat load demand value, event generation timestamp, and initial demand effective time window are combined to construct a heat load demand record containing the corresponding spatial region identifier. The heat load demand record is then stored in the demand time sequence queue of the corresponding spatial region.

[0084] Heat load demand is recorded in the demand time sequence queue with a waiting time. Based on the spatial diffusion attenuation factor, the real-time heat load demand value is determined and dynamically updated with the waiting time.

[0085] In this embodiment, after receiving the asynchronous event data packet, the space area control node calculates the initial heat load demand value using the following formula:

[0086]

[0087] in, This represents the initial heat load demand, indicating the basic energy consumption, in J. This represents the relative change in ambient temperature, expressed in Kelvin (K). This is the specific heat capacity of air, expressed in J / (kg·K). This refers to air density, expressed in kg / m³. This refers to the volume of space, expressed in m³. The thermal inertia compensation coefficient is dimensionless and used to correct for the effects of heat absorption and release from walls, furniture, etc.; specific heat capacity of air. air density and space volume Together, they constitute the spatial environment physical parameters in the system configuration parameters;

[0088] The system quantifies the base energy consumption caused by current environmental changes based on the multiplication of the above parameters. The spatial thermal inertia compensation coefficient is used to correct for the impact of interior walls, furniture, etc., on heat absorption and release. The calculation logic is as follows:

[0089]

[0090] in, This represents the total number of building materials used in the space. For the first Weighting coefficients for different building materials; This represents the ratio of the specific heat capacity of the corresponding material to the specific heat capacity of the standard reference air; it is a dimensionless relative specific heat parameter. For furniture space coverage; The preset coverage influence factor is set to a value range of 0.1 to 0.3, and is obtained by calibrating the average heat absorption coefficient of the furnishings in the target space. The system uses this formula to quantify the heat storage of the walls and the blocking effect of the furniture so that the initial heat load demand value can characterize the exact basic energy consumption compensation of the space.

[0091] The spatial area control node combines the initial heat load demand value, the event generation timestamp, the spatial diffusion attenuation factor, and the effective time window of the initial demand to form a heat load demand record. This heat load demand record includes at least the event generation timestamp, the initial heat load demand value, the real-time heat load demand value, the effective time window of the initial demand, and the spatial area identifier.

[0092] After the heat load demand record enters the corresponding spatial region, it is dynamically updated. The system calculates the real-time heat load demand value that changes with the queue waiting time based on the spatial diffusion attenuation factor, and stores the heat load demand record in the demand time sequence queue of the corresponding spatial region.

[0093] Specifically, the queue management unit recalculates the current heat load demand that still needs to be compensated based on the spatial diffusion attenuation factor and the waiting time recorded in the queue, using the following formula:

[0094]

[0095] in, This represents the real-time heat load demand value. This represents the initial heat load demand value. It is a natural constant. The pre-configured spatial diffusion attenuation factor static constant is obtained by fitting and calibrating the historical heat dissipation rate curve of the target space with the ambient temperature difference. The waiting time is used to establish a negative correlation between the real-time heat load demand and the waiting time.

[0096] The update result is written back to the record and, together with the event generation timestamp, serves as the basis for subsequent supply and demand matching. The heat load demand record synchronously represents the timing of event occurrence and the dynamic decay of demand, serving as the data input for subsequent equipment energy supply matching.

[0097] Based on the system configuration parameters and the actual operating status data packet, the effective energy supply of the spatial area is calculated, and the supply and demand matching calculation is performed according to the time sequence and heat load demand record. The candidate matching record is output, including: the equipment performance coefficient and energy supply allocation coefficient extracted from the system configuration parameters.

[0098] Energy supply calculations are performed based on actual operating power, polling cycle duration, equipment performance coefficient, and energy distribution coefficient to determine the effective energy supply for the corresponding spatial area.

[0099] Within the corresponding spatial area, the heat load demand records in the demand time sequence queue are read in ascending order of event generation timestamp; when multiple heat load demand records have the same event generation timestamp, they are read in ascending order of the value of the indoor environmental monitoring node identifier corresponding to the asynchronous event data packet.

[0100] Through supply and demand matching calculations, the effective energy supply of a spatial area is matched with the real-time heat load demand value, and candidate matching records containing residual demand values ​​are output.

[0101] In this embodiment, after receiving the actual operating status data packet, the space area control node extracts the actual operating power and polling cycle duration, and retrieves the equipment performance coefficient and power allocation coefficient from the pre-stored system configuration parameters. The effective power supply to the space area is then calculated using the following formula:

[0102]

[0103] in, The effective energy supply for the space region is the actual effective energy that can be used for the space region within the corresponding polling period, expressed in J. This represents the actual operating power, expressed in W (watts), or J / s. The polling cycle duration is expressed in seconds (s). The equipment performance coefficient is dimensionless and reflects the effective conversion capability under the current operating conditions. The value of the energy distribution coefficient is based on the pre-set ratio of the physical distance between the air outlet or heat source distribution location of the underlying regulating equipment and the corresponding spatial area. It is dimensionless and reflects the proportion of energy supplied by the equipment to the current spatial area.

[0104] During the time-series sorting process, the sorting unit reads the hot load demand records in the demand time-series queue only within the same spatial area and retrieves them in ascending order of event generation timestamps. When two hot load demand records have the same event generation timestamp, the order is determined by the event generation timestamp in the asynchronous event data packet.

[0105] After sorting, the supply and demand matching calculation unit uses the effective energy supply of the current spatial area as the initial supply value and deducts the real-time heat load demand value of each heat load demand record in turn;

[0106] If there is still remaining energy supply after deduction, continue processing the next record; if the effective energy supply of the current space area is less than the real-time heat load demand value of the heat load demand record, the insufficient part is recorded as the residual demand value, and the record is marked as a partially matched record and output to the candidate matched records; if the effective energy supply of the space area is greater than or equal to the corresponding real-time heat load demand value, the record is recorded as a fully matched record.

[0107] The candidate matching record contains information on the status of requirements that have been met and those that have not been fully met. The candidate matching record is then input into the timing logic verification unit and the validity period constraint verification unit to perform subsequent verification.

[0108] Perform timing logic verification and validity period constraint verification on candidate matching records to generate valid residual requirement records and an exception pending handling queue, including:

[0109] Perform timing logic verification on candidate matching records to confirm that the device sampling timestamp is later than the event generation timestamp corresponding to the heat load demand record;

[0110] Establish validity period constraints based on the initial demand validity time window, and perform demand validity status analysis based on the waiting time of the heat load demand record corresponding to the candidate matching record;

[0111] When the waiting time of the heat load demand record corresponding to the candidate matching record exceeds the initial demand effective time window, the verification is determined to be unsuccessful, the corresponding heat load demand record is transferred to the abnormal pending queue, and the real-time heat load demand value corresponding to the transferred heat load demand record is recorded as the timed-out demand value. The supply and demand matching calculation is re-executed for the candidate matching records that have not timed out.

[0112] When the waiting time of the heat load demand record corresponding to the candidate matching record is less than or equal to the initial demand effective time window, a valid residual demand record containing residual demand value is generated.

[0113] In this embodiment, the spatial area control node verifies each candidate matching record; the timing logic verification unit extracts the device sampling timestamp from the actual operating status data packet of the device and compares it with the event generation timestamp in the corresponding heat load demand record. When the underlying sampling timestamp of the device is later than the event generation timestamp of the demand record, it is determined that the device's power supply occurred after the demand, and the record has the condition to continue participating in the matching; if the order is not met, the candidate matching record is marked as a timing failure record and transferred to the preset waiting state pool in the memory of the spatial area control node; if no matching timing data is obtained within the preset completion time window, the timing failure record is discarded.

[0114] The demand validity status analysis unit calculates the waiting time of the heat load demand record in the queue based on the initial demand validity time window, and determines whether the waiting time has exceeded the initial demand validity time window; if it has not exceeded the window, the record remains valid and a valid residual demand record is generated; if it exceeds the window, the verification is deemed to have failed, the corresponding heat load demand record is transferred to the abnormal pending processing queue, and the current real-time heat load demand value of the record is recorded as the timeout demand value.

[0115] The abnormal pending queue is used to centrally store demand records that have exceeded their validity period for subsequent diagnosis and control adjustments. At the same time, the system will send the remaining demand records that are still within their validity period back to the supply and demand matching calculation unit to continue matching according to the effective energy supply of the spatial area, so that the effective energy supply of the remaining spatial area will be prioritized to match the heat load demand records that have not expired.

[0116] Based on the effective residual demand record and the abnormal pending queue, the system energy consumption deviation is determined, and equipment adjustment instructions are output according to the system energy consumption deviation, including:

[0117] Extract the sum of residual demand values ​​from the valid residual demand records and extract the sum of timeout demand values ​​from the abnormal pending queue. Determine the system energy consumption deviation based on the sum of residual demand values ​​and the sum of timeout demand values.

[0118] The number of valid residual demand records is counted as the number of unmatched demand items. The spatial area energy supply compliance rate within the preset historical time window is obtained. Combined with the spatial area energy supply compliance rate, the number of unmatched demand items, the spatial area energy supply compliance rate, and the system energy consumption deviation are used as control input parameters.

[0119] The target power increment and target duration increment are calculated based on the control input parameters. The target power increment and target duration increment are compared with the preset power adjustment threshold and running time threshold, respectively. When the target power increment and target duration increment are within the threshold range, the device adjustment command containing the target power increment and target duration increment is output. When the target power increment or target duration increment exceeds the threshold range, an over-limit warning data packet is generated. After outputting the device adjustment command, the operation timestamp and control command identifier are recorded.

[0120] In this embodiment, after completing the recording of valid residual demand and the processing of the abnormal pending queue, the spatial area control node first extracts the sum of residual demand values ​​from the valid residual demand records and the sum of timeout demand values ​​from the abnormal pending queue. The node then calculates the system energy consumption deviation using the following formula:

[0121]

[0122] in, This represents the system energy consumption deviation, expressed in J. This represents the sum of residual demand values ​​in the effective residual demand record, expressed in J. This represents the total timeout demand value in the exception handling queue, expressed in J. The weighting coefficient for residual demand is dimensionless. The weighting coefficient for the timeout requirement value is dimensionless and is set as follows: Greater than ;

[0123] When calculating the system energy consumption deviation, the weighting factor of the sum of overdue demand values ​​is set to be greater than the weighting factor of the sum of residual demand values; this deviation represents the amount of unmet demand and unprocessed demand that has exceeded its validity period within the current spatial region.

[0124] Among them, the number of unmatched demand items is the total number of records with residual demand values ​​in the current valid residual demand records; the spatial area energy supply compliance rate is the ratio of the number of polling cycles in which the actual energy supply is greater than or equal to the target energy supply to the total number of polling cycles within the preset historical time window; based on this, the control unit uses the system energy consumption deviation, the number of valid residual demand records containing residual demand values ​​currently counted, and the spatial area energy supply compliance rate currently counted as control input parameters;

[0125] The control unit compares these input parameters with a power regulation threshold and a preset runtime threshold, respectively: when the target power increment in the control input parameters... Less than or equal to the power regulation threshold, and the target duration increment When the value is less than or equal to the runtime threshold, the control input parameter is used as the adjustment amount of the device operating parameter, and the device adjustment command is output and sent to the underlying adjustment device.

[0126] When the control input parameters are outside the allowed power range or the allowed duration range, an over-limit warning data packet is generated. The over-limit warning data packet contains the over-limit parameter identifier, the current timestamp, and the difference data exceeding the threshold. The over-limit warning data packet is sent to the central control terminal to trigger the manual intervention process, indicating that the current equipment operating parameters need further processing. After outputting the equipment adjustment command, the control node also records the operation timestamp and control command identifier to correspond with subsequent equipment operation responses.

[0127] In the above process of comparing multi-dimensional control input parameters with adjustment thresholds, since the system energy consumption deviation is in the physical energy dimension of joules, the number of unmatched demand items is a dimensionless integer value, and the spatial area energy supply compliance rate is a percentage, the system performs dimensional transformation and decoupling on the control input parameters:

[0128] The control unit converts the energy dimension into a basic compensation power value that can be directly aligned with the power adjustment threshold dimension using the following formula:

[0129]

[0130] in, The basic compensation power value is expressed in W, or J / s. This represents the system energy consumption deviation, expressed in J. The polling time period is in seconds; using a preset nonlinear function mapping mechanism, the number of unmatched demand items in the spatial region is converted into a multiplicative factor that affects the increment of equipment runtime, and the energy supply compliance rate is converted into a penalty operator that constrains power fluctuation.

[0131] Specifically, the mapping rule of the preset nonlinear function mapping mechanism is: using a multiplication coefficient. and punishment operator The base compensation power value is corrected, and the target power increment in the output device operating parameter adjustment is calculated as follows:

[0132]

[0133] in, The basic compensation power value is expressed in watts (W).

[0134] When the number of unmatched requirements When the value is less than 10, the multiplication factor is defined. ;when When defining the multiplication factor. ;when When defining the multiplication factor. ,in, It is a natural constant; at the same time, when the energy supply compliance rate of the spatial region... When the percentage is greater than or equal to 80%, i.e., when a safety threshold is set, the penalty operator... ;when When the percentage is less than 80%, the corresponding output is a non-linearly increasing penalty operator:

[0135]

[0136] in, The penalty operator is measured in W. The energy supply compliance rate for a given spatial area is dimensionless. The preset power penalty coefficient has its physical dimension set to W. Under the condition that the underlying numerical calculation satisfies the homogeneity of dimensions, the output parameter range of the power adjustment command is limited based on the preset nonlinear function mapping rule.

[0137] Simultaneously, the system uses the number of unmatched demand items and the control cycle duration to perform quantitative adjustment calculations on the pure duration dimension using the following formula, and outputs the target duration increment:

[0138]

[0139] in, The increment for the target duration is expressed in seconds (s). The duration of the current control cycle is expressed in seconds (s). This is a preset duration scaling limit coefficient, dimensionless; The boundary constant of the maximum permissible mismatch term in a single cycle is calibrated for the system. It is dimensionless and its value is set based on the extreme value of the mismatch record under the historical peak load state in the target space. It is used to compensate for the power supply deviation under discontinuous operating conditions. Thus, the heterogeneous input parameters are converted to the power and duration dimensions.

[0140] The actual operating status parameters after the monitoring equipment adjustment command is issued include: the actual operating power, actual running time or switch status after the equipment adjustment command is issued, and the target power supply status.

[0141] Calculate the control execution deviation under the current state based on the difference between the actual operating power, actual operating time or switch status and the target operating parameters corresponding to the equipment adjustment command;

[0142] If the control execution deviation exceeds the preset alarm limit within a continuous preset quantity control cycle, the system is deemed to be malfunctioning.

[0143] A performance alert is triggered when the length of the exception queue changes by more than a preset percentage threshold.

[0144] In this embodiment, after receiving the device adjustment command, the underlying adjustment device will continuously report the actual operating power, actual running time or switch status, and target power supply achievement status in subsequent control cycles; the space area control node compares these measured values ​​with the target operating parameters corresponding to the device adjustment command item by item, and calculates the control execution deviation in the current state; the control execution deviation is used to characterize the numerical difference between the objective execution parameters of the device and the target operating parameters;

[0145] When the control execution deviation exceeds the preset alarm limit within a consecutive preset number of control cycles, the system is judged to be in abnormal operation. At the same time, the node will also count the changes in the length of the abnormal waiting queue. When the change ratio of the length of the abnormal waiting queue exceeds the preset percentage threshold, the system triggers a performance alarm. The spatial area control node synchronously checks the evolution trend of the control execution deviation and the abnormal demand queue length, and outputs a reference dataset for correcting the system configuration parameters.

[0146] The nodes execute a standardized deviation ratio mapping rule to calculate the control execution deviation: the system uses a unified underlying mapping operation to aggregate heterogeneous sampled parameters into a scalar form of control execution deviation using the following formula:

[0147]

[0148] in, Dimensionless scalars are used to control execution deviations. This refers to the actual operating power, expressed in watts (W). Target operating power, in watts (W); The maximum rated power of the equipment, expressed in watts (W). This refers to the actual runtime, expressed in seconds (s). The target runtime is in seconds. The duration of the current control cycle is expressed in seconds (s).

[0149] For discrete, actual acquisition switch signals, Boolean values, 0 or 1; The target status code, a boolean value, 0 or 1; This is an XOR logical operator; it outputs 0 if the states are consistent and 1 if they are divergent. The normalized weights for the power deviation rate are... The normalized weights for the duration deviation rate, Let be the normalized weights of the state deviations, which are dimensionless and satisfy . Based on the above mapping operation, the heterogeneous sampling parameters are converted and compared with the preset alarm limit values ​​of one dimension and dimensionlessness.

[0150] The system configuration parameters are corrected based on feedback from actual operating status parameters, including: encapsulating system operation anomalies and performance alarms into feedback signals; parsing the feedback signals; when the feedback signal indicates a system operation anomaly, decreasing the dynamic trigger threshold and extending the effective time window of the initial demand by a first preset step size; when the feedback signal indicates a performance alarm, increasing the spatial thermal inertia compensation coefficient by a second preset step size; and periodically writing the adjusted parameter values ​​into the system configuration parameters.

[0151] In this embodiment, the space area control node encapsulates system operation anomalies and performance alarms into a unified feedback signal and sends the feedback signal to the parameter adjustment unit;

[0152] The parameter adjustment unit adjusts the system configuration parameters according to the anomaly type or alarm level reflected by the feedback signal, through defined control logic:

[0153] If the system malfunctions due to insufficient device response, i.e., the control execution deviation exceeds the preset alarm limit for five consecutive control cycles, the dynamic trigger threshold will be reduced by 10% according to the preset step size, and the initial demand effective time window will be fixedly extended by 10 seconds to achieve the reduction of the dynamic trigger threshold and the expansion of the initial demand effective time window.

[0154] If the performance alarm level is high, that is, the length change ratio of the abnormal pending queue exceeds 20% in a single instance, the spatial thermal inertia compensation coefficient will be increased by 0.05 according to the preset step size to increase the system's compensation margin for environmental hysteresis response.

[0155] If the abnormal behavior manifests as a continuous expansion of the abnormal pending queue, the dynamic trigger threshold and the effective time window of the initial demand will be adjusted accordingly to match the rules for subsequent event generation and demand retention with the current space operation conditions.

[0156] After the adjustment is completed, the new parameter values ​​will be periodically written into the system configuration parameters and directly read by the spatial area control node during the next round of event collection and supply-demand matching, so that the aforementioned triggering, modeling, matching and adjustment processes can continue to be executed according to the current spatial area status.

[0157] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An energy consumption balance calculation method based on indoor environmental big data, applied to a target building environment equipped with an energy consumption balance control system, wherein the target building environment is divided into multiple spatial areas, and the energy consumption balance control system includes indoor environmental monitoring nodes and underlying regulation devices corresponding to the spatial areas respectively, characterized in that, include: Obtain the system configuration parameters of the energy consumption balance control system; Acquire asynchronous event data packets from the indoor environmental monitoring node and actual operating status data packets from the underlying control device; Obtain the mapping relationship between the indoor environment monitoring node, the underlying adjustment device, and the spatial area. Based on the mapping relationship, associate the asynchronous event data packet and the actual operating status data packet with the corresponding spatial area, and establish a demand time sequence queue according to the spatial area. Based on the asynchronous event data packet, a hot load demand record is constructed and stored in the demand time sequence queue corresponding to the spatial region; Based on the system configuration parameters and the actual operating status data packet, the effective energy supply of the spatial area is calculated, and the supply and demand matching calculation is performed according to the time sequence and the heat load demand record, and candidate matching records are output. Perform time-series logic verification and validity period constraint verification on the candidate matching records to generate valid residual demand records and an exception pending processing queue; Based on the effective residual demand record and the abnormal pending queue, the system energy consumption deviation is determined, and the equipment adjustment command is output according to the system energy consumption deviation. The system collects actual operating status parameters generated by the device's adjustment commands and uses these parameters to correct the system configuration parameters.

2. The energy consumption balance calculation method based on indoor environmental big data according to claim 1, characterized in that, The system configuration parameters include: space thermal inertia compensation coefficient, initial demand effective time window, dynamic triggering threshold, space environment physical parameters, space diffusion attenuation factor, equipment performance coefficient, energy supply distribution coefficient, power regulation threshold, and operating time threshold.

3. The energy consumption balance calculation method based on indoor environmental big data according to claim 2, characterized in that, Acquiring asynchronous event data packets from the indoor environmental monitoring node and actual operating status data packets from the underlying control equipment includes: The asynchronous event data packet includes at least the indoor environment monitoring node identifier and the event generation timestamp, and the actual operating status data packet includes at least the device identifier, actual operating power, polling cycle duration, and device sampling timestamp; The indoor environment monitoring node collects the relative change in ambient temperature and generates the asynchronous event data packet when the relative change in ambient temperature is greater than the dynamic trigger threshold. The asynchronous event data packet also includes the relative change in ambient temperature. The event generation timestamp is a time series parameter generated by the indoor environment monitoring node based on a real-time clock. The actual operating status data packet is obtained through the underlying adjustment device, and the actual operating status data packet also includes a device sampling timestamp that is monotonically increasing on the device side.

4. The energy consumption balance calculation method based on indoor environmental big data according to claim 3, characterized in that, Based on the asynchronous event data packet, a hot load demand record is constructed and stored in the demand time-series queue corresponding to the spatial region, including: The initial heat load demand value is calculated based on the asynchronous event data packet. The initial heat load demand value, the event generation timestamp, and the initial demand effective time window are combined to construct a heat load demand record containing the identifier of the corresponding spatial region. The heat load demand record is then stored in the demand time sequence queue of the corresponding spatial region. The heat load demand is recorded in the demand time queue with a waiting time. Based on the spatial diffusion attenuation factor, the real-time heat load demand value is determined and dynamically updated with the waiting time.

5. The energy consumption balance calculation method based on indoor environmental big data according to claim 4, characterized in that, Based on the system configuration parameters and the actual operating status data packet, the effective energy supply of the spatial area is calculated, and a supply-demand matching calculation is performed according to the time sequence and the heat load demand record. Candidate matching records are output, including: Extract the device performance coefficient and energy distribution coefficient from the system configuration parameters; Based on the actual operating power, the polling cycle duration, the equipment performance coefficient, and the energy allocation coefficient, energy supply calculation is performed to determine the effective energy supply for the corresponding spatial area. Within the corresponding spatial area, the heat load demand records in the demand time sequence queue are read in ascending order according to the event generation timestamp; when multiple heat load demand records have the same event generation timestamp, they are read in ascending order according to the numerical value of the indoor environmental monitoring node identifier corresponding to the asynchronous event data packet. Through supply and demand matching calculation, the effective energy supply of the spatial area is matched with the real-time heat load demand value, and the candidate matching record containing the residual demand value is output.

6. The energy consumption balance calculation method based on indoor environmental big data according to claim 5, characterized in that, Perform time-series logic verification and validity period constraint verification on the candidate matching records to generate valid residual demand records and an exception pending handling queue, including: Perform a timing logic check on the candidate matching records to confirm that the device sampling timestamp is later than the event generation timestamp corresponding to the heat load demand record; Establish validity period constraints based on the initial demand validity time window, and perform demand validity status analysis based on the waiting time of the heat load demand record corresponding to the candidate matching record; When the waiting time of the heat load demand record corresponding to the candidate matching record is greater than the initial demand effective time window, the verification is determined to be unsuccessful, the corresponding heat load demand record is transferred to the abnormal pending queue, and the real-time heat load demand value corresponding to the transferred heat load demand record is recorded as the timeout demand value. The supply and demand matching calculation is re-executed for the candidate matching record that has not timed out. When the waiting time of the heat load demand record corresponding to the candidate matching record is less than or equal to the initial demand effective time window, a valid residual demand record containing residual demand value is generated.

7. The energy consumption balance calculation method based on indoor environmental big data according to claim 6, characterized in that, Based on the effective residual demand record and the abnormal pending queue, the system energy consumption deviation is determined, and equipment adjustment instructions are output according to the system energy consumption deviation, including: Extract the sum of residual demand values ​​from the effective residual demand records and extract the sum of timeout demand values ​​from the abnormal pending queue. Determine the system energy consumption deviation based on the sum of residual demand values ​​and the sum of timeout demand values. The number of valid residual demand records is counted as the number of unmatched demand items. The spatial area energy supply compliance rate within a preset historical time window is obtained. Combined with the spatial area energy supply compliance rate, the number of unmatched demand items, the spatial area energy supply compliance rate, and the system energy consumption deviation are used as control input parameters. Based on the control input parameters, the target power increment and target duration increment are calculated. The target power increment and target duration increment are compared with preset power adjustment thresholds and running time thresholds, respectively. When the target power increment and target duration increment are within the threshold range, a device adjustment command containing the target power increment and target duration increment is output. When the target power increment or target duration increment exceeds the threshold range, an over-limit warning data packet is generated. After outputting the device adjustment command, record the operation timestamp and control command identifier.

8. The energy consumption balance calculation method based on indoor environmental big data according to claim 7, characterized in that, Collect actual operating status parameters generated based on the device's adjustment commands, including: The system collects the actual operating power, actual operating time or switch status, and target power supply achievement status of the device after the adjustment command is issued. Based on the difference between the actual operating power, actual operating time or switch status and the target operating parameters corresponding to the device adjustment command, calculate the control execution deviation in the current state; If the control execution deviation exceeds a preset alarm limit within a continuous preset quantity control cycle, the system is determined to be malfunctioning. When the length change ratio of the abnormal pending queue exceeds a preset percentage threshold, a performance alarm is triggered.

9. The energy consumption balance calculation method based on indoor environmental big data according to claim 8, characterized in that, The system configuration parameters are corrected based on the feedback of the actual operating status parameters, including: The system malfunction and the performance alarm are encapsulated into a feedback signal; Analyzing the feedback signal, when the feedback signal indicates an abnormal system operation, the dynamic trigger threshold is reduced by a first preset step size and the effective time window of the initial demand is extended; when the feedback signal indicates a performance alarm, the spatial thermal inertia compensation coefficient is increased by a second preset step size. The adjusted parameter values ​​are periodically written into the system configuration parameters.

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