Heat supply unit remote monitoring system based on big data analysis

Abstract

The invention provides a heat supply unit remote monitoring system based on big data analysis, and relates to the technical field of heat supply monitoring, and the system comprises a data collection module which collects the operation data of a target heat supply unit in real time, and carries out the real-time transmission, and obtains the first operation data; the data analysis module is used for performing data processing and data analysis on the first operation data based on a big data analysis technology to obtain a first analysis result; the remote monitoring module is used for optimizing the operation of the target heat supply unit according to the first analysis result to realize the optimized operation of the target heat supply unit; and the monitoring interaction module is used for displaying the optimized operation parameters and the equipment state parameters of the target heat supply unit and carrying out remote control based on a display result and real-time user requirements. The operation data of the heat supply unit are processed and analyzed, operation optimization is carried out based on the analysis result, meanwhile, the state of unit equipment is monitored, accurate monitoring and real-time control over the heat supply unit are achieved, and the working efficiency of the heat supply unit is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heating monitoring, and in particular to a heating unit remote monitoring system based on big data analysis. Background Art

[0002] At present, with the acceleration of urbanization, the scale of heating systems continues to expand. Therefore, the role of intelligent and remote monitoring and control of heating units in the heating field is particularly prominent.

[0003] However, the existing monitoring system of heating units generally can only make judgments after analyzing a large amount of data, thereby reducing the timeliness of heating.

[0004] Therefore, the present invention provides a remote monitoring system for a heating unit based on big data analysis. Summary of the invention

[0005] The present invention provides a remote monitoring system for a heating unit based on big data analysis, which is used to process and analyze the operating data of the heating unit, optimize the operation based on the analysis results, and monitor the equipment status of the unit at the same time, so as to achieve accurate monitoring and real-time control of the heating unit and improve the working efficiency of the heating unit.

[0006] The present invention provides a remote monitoring system for a heating unit based on big data analysis, comprising:

[0007] Data acquisition module: used for real-time acquisition of operating data of the target heating unit, and real-time transmission to obtain first operating data;

[0008] Data analysis module: used for performing data processing and data analysis on the first operation data based on the big data analysis technology to obtain a first analysis result;

[0009] Remote monitoring module: used to optimize the operation of the target heating unit according to the first analysis result, so as to achieve the optimized operation of the target heating unit;

[0010] Monitoring and interaction module: used to display the optimized operating parameters and equipment status parameters of the target heating unit, and perform remote control based on the display results and real-time user needs.

[0011] The data acquisition module provided by the present invention comprises:

[0012] Data collection unit: used to determine the data detection points of the target heating unit, and collect operating data at each data detection point based on preset sensors;

[0013] Data classification unit: used to classify the collected operation data according to the corresponding data detection points to obtain first classification operation data;

[0014] Data transmission unit: used for transmitting the first classification operation data of each data detection point in real time, and obtaining the first operation data of the target heating unit based on the transmission result.

[0015] The data analysis module provided by the present invention includes:

[0016] Data processing unit: used for performing data cleaning and data standardization processing on the first operation data to obtain first processed data;

[0017] Initial analysis unit: used to analyze the first processed data based on big data analysis technology to obtain initial analysis results;

[0018] The first analysis unit is used to select the analysis data type with the highest degree of consistency with the target heating unit from the unit analysis database, and extract the corresponding analysis results in the initial analysis results based on the analysis data type to determine the first analysis result of the target heating unit.

[0019] The remote monitoring module provided by the present invention comprises:

[0020] A detection and determination unit: used to determine the data detection points and corresponding unit equipment that need to be optimized in the target heating unit based on the first analysis result;

[0021] A solution determination unit: used for determining a data optimization solution for a current data detection point based on a first analysis result of each data detection point;

[0022] The first optimization unit is used to integrate the data optimization schemes corresponding to the data detection points of the same unit equipment in the target heating unit to obtain the first equipment optimization scheme;

[0023] The second optimization unit is used to determine the feasibility of the first equipment optimization plan and optimize the plan to obtain the second equipment optimization plan;

[0024] The second judgment unit is used to obtain the device association degree of each unit equipment in the target heating unit, and judge whether the unit equipment with device association will affect each other when performing the second equipment optimization plan;

[0025] The third optimization unit is used to optimize the second equipment optimization plan based on the mutual influence results of the unit equipment to obtain the third equipment optimization plan;

[0026] The first comprehensive unit is used to integrate the third equipment optimization plan of each unit equipment in the target heating unit to obtain a first comprehensive optimization plan;

[0027] The second comprehensive unit is used to determine whether there is a scheme conflict in the first comprehensive optimization scheme, and adjust the corresponding equipment optimization scheme in the first comprehensive optimization scheme based on the conflicting scheme to obtain a second comprehensive optimization scheme;

[0028] Optimizing operation unit: used to optimize the operation of the target heating unit based on the second comprehensive optimization plan, so as to achieve optimized operation of the target heating unit.

[0029] The second judgment unit provided according to the present invention includes:

[0030] The first association subunit is used to determine the device association degree between each unit device based on the device type and device parameters of each unit device in the target heating unit, and obtain a first association degree set of each unit device;

[0031] A second association subunit: used for removing the first association degree corresponding to the unit equipment whose first association degree is less than the preset association degree from the first association degree set of each unit equipment, to obtain a second association degree set;

[0032] Equipment impact subunit: used to sort the first correlation levels in the second correlation level set to obtain an ordered third correlation level set, and determine the equipment impact index of the corresponding unit equipment and the current unit equipment based on each first correlation level in the third correlation level set.

[0033] The device influencing subunit provided according to the present invention comprises:

[0034] Determine the equipment impact index S of the corresponding unit equipment and the current unit equipment;

[0035] S is the equipment impact index between the unit equipment corresponding to each first correlation degree in the third correlation degree set and the current equipment, α i is the first correlation degree corresponding to the i-th unit equipment in the third correlation degree set, Q i is the real-time heat dissipation of the i-th unit equipment in the third association degree set, Q′ is the heat dissipation of the current unit equipment, and h i is the equipment distance between the i-th unit equipment and the current equipment in the third association degree set, γ i is the heat dissipation degree of the i-th unit equipment within a unit distance, β i is the equipment resonance index between the i-th unit equipment and the current equipment in the third correlation degree set, μ is the heat influence coefficient of the equipment resonance on the unit equipment, and m is the number of the first correlation degree in the third correlation degree set.

[0036] The monitoring interaction module provided by the present invention includes:

[0037] Operation display unit: used to display the optimized operation parameters and equipment status parameters of the target heating unit;

[0038] A first difference unit: used for obtaining the standard display result of the target heating unit and comparing it with the real-time display result, so as to determine the result difference between each real-time display result and the corresponding standard display result, and obtain a first difference table;

[0039] A first instruction unit: used for obtaining a difference type of each first difference in the first difference table, and filtering a control instruction matching each first difference in the first difference table from a difference-control database based on the difference type to obtain a first instruction set;

[0040] Instruction control unit: used to obtain real-time user needs and determine whether the first instruction set can meet the real-time user needs;

[0041] If satisfied, remotely control the target heating unit based on the first instruction set;

[0042] If not, the first instruction set is adjusted based on user needs, so that the target heating unit is remotely controlled based on the adjusted instruction set.

[0043] The instruction control unit provided according to the present invention includes:

[0044] When the first instruction set cannot meet the real-time user demand, split the real-time user demand to obtain a first demand set;

[0045] Determine a first sub-demand that cannot be satisfied in the first requirement set, and obtain a first instruction type related to the current first sub-demand;

[0046] Extracting a first instruction related to a first instruction type from a first instruction set, and performing instruction adjustment on the first instruction based on a corresponding first sub-demand;

[0047] The adjustment results of each instruction of the first instruction set are integrated to obtain a second instruction set, and the target heating unit is remotely controlled based on the second instruction set.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a remote monitoring system for a heating unit based on big data analysis, which processes and analyzes the operating data of the heating unit, optimizes the operation based on the analysis results, and monitors the equipment status of the unit at the same time, thereby achieving accurate monitoring and real-time control of the heating unit and improving the working efficiency of the heating unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 It is a structural diagram of a remote monitoring system for a heating unit based on big data analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Embodiment 1:

[0053] The embodiment of the present invention provides a remote monitoring system for a heating unit based on big data analysis, such as Figure 1 As shown, including:

[0054] Data acquisition module: used for real-time acquisition of operating data of the target heating unit, and real-time transmission to obtain first operating data;

[0055] Data analysis module: used for performing data processing and data analysis on the first operation data based on the big data analysis technology to obtain a first analysis result;

[0056] Remote monitoring module: used to optimize the operation of the target heating unit according to the first analysis result, so as to achieve the optimized operation of the target heating unit;

[0057] Monitoring and interaction module: used to display the optimized operating parameters and equipment status parameters of the target heating unit, and perform remote control based on the display results and real-time user needs.

[0058] In this embodiment, the target heating unit refers to a specific heating equipment or system that needs to be monitored and optimized. For example, the target heating unit may be a large coal-fired heating unit in a regional heating center in City A.

[0059] In this embodiment, the operating data refers to various parameters and data generated by the heating unit during operation, such as temperature, pressure, flow rate, rotation speed, etc.

[0060] In this embodiment, real-time transmission refers to immediately sending the collected data to a designated location or system for timely processing or analysis.

[0061] In this embodiment, the first operating data refers to operating data obtained after data preprocessing and real-time transmission.

[0062] In this embodiment, big data analysis technology refers to the technology of extracting useful information and patterns from large amounts of complex data using advanced statistical and analytical methods. For example, big data analysis technology can analyze data sets for machine learning, data mining, statistical analysis, etc., and identify the operating rules, energy consumption trends, fault warnings, etc. of the heating unit.

[0063] In this embodiment, the first analysis result refers to the result or conclusion obtained after processing the first operating data through big data analysis technology. For example, the analysis result may be that the combustion efficiency of the heating unit needs to be improved, or that a certain component may be worn.

[0064] In this embodiment, operation optimization refers to improving the operating efficiency, reducing energy consumption or extending the life of the equipment by adjusting the operating parameters or operation mode of the heating unit.

[0065] In this embodiment, the optimized operating parameters refer to the operating parameters generated by the target heating unit in the current operating state after optimization.

[0066] In this embodiment, remote control refers to remote operation or adjustment of the heating unit through a remote control system.

[0067] In this embodiment, the real-time user demand refers to the user's demand or expectation for heating services at the current moment, such as indoor temperature, heating time, etc.

[0068] The beneficial effects of the above technical solution are: by processing and analyzing the operating data of the heating unit, optimizing the operation based on the analysis results, and monitoring the equipment status of the unit, accurate monitoring and real-time control of the heating unit can be achieved, thereby improving the working efficiency of the heating unit.

[0069] Embodiment 2:

[0070] Based on the first embodiment, the data acquisition module includes:

[0071] Data collection unit: used to determine the data detection points of the target heating unit, and collect operating data at each data detection point based on preset sensors;

[0072] Data classification unit: used to classify the collected operation data according to the corresponding data detection points to obtain first classification operation data;

[0073] Data transmission unit: used for transmitting the first classification operation data of each data detection point in real time, and obtaining the first operation data of the target heating unit based on the transmission result.

[0074] In this embodiment, the target heating unit refers to a specific heating equipment or system that needs to be monitored and optimized. For example, the target heating unit may be a large coal-fired heating unit in a regional heating center in City A.

[0075] In this embodiment, the preset sensors include temperature sensors, flow sensors, pressure sensors, liquid level sensors, etc.

[0076] In this embodiment, the operating data refers to various parameters and data generated by the heating unit during operation, such as temperature, pressure, flow rate, rotation speed, etc.

[0077] In this embodiment, the first classification operation data refers to the operation data collected and classified according to different data detection points, wherein each first classification operation sub-data corresponds to different types of operation data of the same data detection point.

[0078] In this embodiment, real-time transmission refers to immediately sending the collected data to a designated location or system for timely processing or analysis.

[0079] In this embodiment, the first operating data refers to operating data obtained after data preprocessing and real-time transmission.

[0080] The beneficial effect of the above technical solution is that by classifying and transmitting the collected operating data, the processing and analysis of the operating data can be made more organized and clear, and the data analysis efficiency can be improved, thereby improving the working efficiency of the target heating unit.

[0081] Embodiment 3:

[0082] Based on Example 2, the data analysis module includes:

[0083] Data processing unit: used for performing data cleaning and data standardization processing on the first operation data to obtain first processed data;

[0084] Initial analysis unit: used to analyze the first processed data based on big data analysis technology to obtain initial analysis results;

[0085] The first analysis unit is used to select the analysis data type with the highest degree of consistency with the target heating unit from the unit analysis database, and extract the corresponding analysis results in the initial analysis results based on the analysis data type to determine the first analysis result of the target heating unit.

[0086] In this embodiment, data cleaning refers to the processing of errors, exceptions or duplicate data that may occur during data collection, entry, storage, etc. to ensure the accuracy, consistency, completeness and availability of the data. Data standardization refers to the process of data conversion and data integration of the collected data.

[0087] In this embodiment, the first processed data refers to the data obtained after data cleaning and data standardization processing are performed on the first operating data.

[0088] In this embodiment, big data analysis technology refers to the technology of extracting useful information and patterns from large amounts of complex data using advanced statistical and analytical methods. For example, big data analysis technology can analyze data sets for machine learning, data mining, statistical analysis, etc., and identify the operating rules, energy consumption trends, fault warnings, etc. of the heating unit.

[0089] In this embodiment, the initial analysis result refers to the result or conclusion obtained after processing the first operating data through big data analysis technology. For example, the analysis result may be that the combustion efficiency of the heating unit needs to be improved, or that a certain component may be worn.

[0090] In this embodiment, the analysis data types include running time analysis, energy consumption analysis, fault analysis, performance evaluation, user satisfaction analysis and the like.

[0091] In this embodiment, the first analysis result is obtained by extracting corresponding analysis results from the initial analysis results according to the analysis data type.

[0092] The beneficial effect of the above technical solution is: by analyzing the first processed data based on big data analysis technology, an initial analysis result is obtained, and the analysis results related to the unit heating of the target heating unit in the initial analysis result are extracted to form the first analysis result, thereby improving data analysis efficiency.

[0093] Embodiment 4:

[0094] Based on Example 3, the remote monitoring module includes:

[0095] A detection and determination unit: used to determine the data detection points and corresponding unit equipment that need to be optimized in the target heating unit based on the first analysis result;

[0096] A solution determination unit: used for determining a data optimization solution for a current data detection point based on a first analysis result of each data detection point;

[0097] The first optimization unit is used to integrate the data optimization schemes corresponding to the data detection points of the same unit equipment in the target heating unit to obtain the first equipment optimization scheme;

[0098] The second optimization unit is used to determine the feasibility of the first equipment optimization plan and optimize the plan to obtain the second equipment optimization plan;

[0099] The second judgment unit is used to obtain the device association degree of each unit equipment in the target heating unit, and judge whether the unit equipment with device association will affect each other when performing the second equipment optimization plan;

[0100] The third optimization unit is used to optimize the second equipment optimization plan based on the mutual influence results of the unit equipment to obtain the third equipment optimization plan;

[0101] The first comprehensive unit is used to integrate the third equipment optimization scheme of each unit equipment in the target heating unit to obtain a first comprehensive optimization scheme;

[0102] The second comprehensive unit is used to determine whether there is a scheme conflict in the first comprehensive optimization scheme, and adjust the corresponding equipment optimization scheme in the first comprehensive optimization scheme based on the conflicting scheme to obtain a second comprehensive optimization scheme;

[0103] Optimizing operation unit: used to optimize the operation of the target heating unit based on the second comprehensive optimization plan, so as to achieve optimized operation of the target heating unit.

[0104] In this embodiment, the data detection point refers to a measurement point set in the heating unit for monitoring and recording key parameters (such as temperature, pressure, flow, etc.).

[0105] In this embodiment, the data optimization plan refers to a plan formulated for each data detection point based on the first analysis result to improve data quality or to improve the operating efficiency of the unit by using data.

[0106] In this embodiment, the first equipment optimization solution refers to an overall optimization solution for the current unit equipment that is formed by integrating the data optimization solutions of all data detection points under the same unit equipment.

[0107] In this embodiment, the feasibility of the solution includes comprehensive feasibility in terms of technology, economy, safety, etc.

[0108] In this embodiment, the second equipment optimization solution refers to a more complete equipment optimization solution obtained after feasibility judgment and solution optimization based on the first equipment optimization solution.

[0109] In this embodiment, the degree of equipment association refers to the degree of mutual dependence or influence between different unit equipment in terms of function, operation or structure.

[0110] In this embodiment, the mutual influence result refers to the direct or indirect influence that an optimization solution of one device may have on another device when performing device optimization.

[0111] In this embodiment, the third equipment optimization plan is a plan obtained by further adjusting and optimizing the second equipment optimization plan based on the mutual influence results of the unit equipment.

[0112] In this embodiment, the first comprehensive optimization scheme refers to a comprehensive optimization scheme for the target heating unit obtained by integrating the third equipment optimization scheme of each unit equipment in the target heating unit.

[0113] In this embodiment, solution conflict refers to the contradiction or inconsistency between optimization solutions of different devices or systems.

[0114] In this embodiment, the second comprehensive optimization scheme refers to a comprehensive optimization scheme obtained by processing the conflicting sub-schemes in the first comprehensive optimization scheme.

[0115] The beneficial effect of the above technical solution is: by considering the influencing factors of various factors on the equipment optimization plan, the operation optimization of the operating parameters can better meet the real-time operation requirements of the target heating unit, thereby realizing more accurate monitoring and real-time control of the heating unit, and improving the working efficiency of the heating unit.

[0116] Embodiment 5:

[0117] Based on the fourth embodiment, the second judgment unit includes:

[0118] The first association subunit is used to determine the device association degree between each unit device based on the device type and device parameters of each unit device in the target heating unit, and obtain a first association degree set of each unit device;

[0119] A second association subunit: used for removing the first association degree corresponding to the unit equipment whose first association degree is less than the preset association degree from the first association degree set of each unit equipment, to obtain a second association degree set;

[0120] Equipment impact subunit: used to sort the first correlation levels in the second correlation level set to obtain an ordered third correlation level set, and determine the equipment impact index of the corresponding unit equipment and the current unit equipment based on each first correlation level in the third correlation level set.

[0121] In this embodiment, the equipment type refers to the type or classification of the unit equipment, such as combustion equipment, heat exchange equipment, transportation equipment, etc.

[0122] In this embodiment, the equipment parameter is a physical quantity or a numerical value used to describe the operating status of the unit equipment, such as temperature, pressure, flow rate, rotation speed, etc.

[0123] In this embodiment, the degree of equipment association refers to the degree of mutual dependence or influence between different equipment groups in terms of function, operation or structure. The degree of association determines the strength of the interaction between the equipment.

[0124] In this embodiment, the first correlation degree set is a set of correlation degrees between each unit device, which is preliminarily determined based on the device type and device parameters of each unit device in the target heating unit. This set includes the correlation degrees between all devices.

[0125] In this embodiment, the second association degree set refers to a set obtained by eliminating the first association degrees corresponding to the unit devices whose first association degrees are less than the preset association degree on the basis of the first association degree set. The second association degree set only includes the association degrees between the devices with important associations.

[0126] In this embodiment, the preset correlation degree is a correlation degree threshold value preset in order to screen important correlations.

[0127] In this embodiment, the third association degree set refers to an ordered association degree set obtained by sorting the first association degrees in the second association degree set.

[0128] In this embodiment, the device impact index is determined based on each first correlation degree in the third correlation degree set, and is an indicator describing the degree of mutual influence between the corresponding unit device and the current unit device. The high or low device impact index reflects the strength of the mutual influence between the devices.

[0129] The beneficial effect of the above technical solution is: by considering the influencing factors of various factors on the equipment optimization plan, the operation optimization of the operating parameters can better meet the real-time operation requirements of the target heating unit, thereby realizing more accurate monitoring and real-time control of the heating unit, and improving the working efficiency of the heating unit.

[0130] Embodiment 6:

[0131] Based on Example 5, the device impact subunit includes:

[0132] Determine the equipment impact index S of the corresponding unit equipment and the current unit equipment;

[0133] S is the equipment impact index between the unit equipment corresponding to each first correlation degree in the third correlation degree set and the current equipment, α i is the first correlation degree corresponding to the i-th unit equipment in the third correlation degree set, Q i is the real-time heat dissipation of the i-th unit equipment in the third association degree set, Q′ is the heat dissipation of the current unit equipment, and h iis the equipment distance between the i-th unit equipment and the current equipment in the third association degree set, γ i is the heat dissipation degree of the i-th unit equipment within a unit distance, β i is the equipment resonance index between the i-th unit equipment and the current equipment in the third correlation degree set, μ is the heat influence coefficient of the equipment resonance on the unit equipment, and m is the number of the first correlation degree in the third correlation degree set.

[0134] The beneficial effect of the above technical solution is: by calculating the comprehensive equipment influence index of each unit equipment in the target heating unit on the current unit equipment, thereby eliminating the mutual influence between equipment, the judgment of the target heating unit can be improved, thereby improving the working efficiency of the heating unit.

[0135] Embodiment 7:

[0136] Based on Example 4, the monitoring interaction module includes:

[0137] Operation display unit: used to display the optimized operation parameters and equipment status parameters of the target heating unit;

[0138] A first difference unit: used for obtaining the standard display result of the target heating unit and comparing it with the real-time display result, so as to determine the result difference between each real-time display result and the corresponding standard display result, and obtain a first difference table;

[0139] A first instruction unit: used for obtaining a difference type of each first difference in the first difference table, and filtering a control instruction matching each first difference in the first difference table from a difference-control database based on the difference type to obtain a first instruction set;

[0140] Instruction control unit: used to obtain real-time user needs and determine whether the first instruction set can meet the real-time user needs;

[0141] If satisfied, remotely control the target heating unit based on the first instruction set;

[0142] If not, the first instruction set is adjusted based on user needs, so that the target heating unit is remotely controlled based on the adjusted instruction set.

[0143] In this embodiment, the optimized operating parameters refer to the operating parameters obtained after optimizing the control of the target heating unit.

[0144] In this embodiment, the device state parameters refer to the device state parameters of each heating device in the target heating unit. For example, the device state parameters include temperature parameters, pressure parameters, power parameters, frequency conversion control parameters, etc.

[0145] In this embodiment, the standard display result refers to the standard operating parameters and standard equipment status parameters of the target heating unit at the current temperature.

[0146] In this embodiment, the real-time display result is the result of transformation and display based on the optimized operation parameters and the equipment status parameters.

[0147] In this embodiment, the first difference table refers to a difference table formed by the corresponding difference between the real-time display result and each sub-display result in the standard display result.

[0148] In this embodiment, the difference type is the data type corresponding to the real-time display result.

[0149] In this embodiment, the first instruction set refers to an instruction set obtained by synthesizing the control instructions of each first difference match in the first difference table.

[0150] In this embodiment, remote control refers to remote operation or adjustment of the heating unit through a remote control system.

[0151] In this embodiment, the real-time user demand refers to the user's demand or expectation for heating services at the current moment, such as indoor temperature, heating time, etc.

[0152] The beneficial effect of the above technical solution is that by optimizing the adjustment control instructions of the target heating unit in combination with real-time user needs, the monitoring and control of the target heating unit can better meet user needs.

[0153] Embodiment 8:

[0154] Based on the seventh embodiment, the instruction control unit includes:

[0155] When the first instruction set cannot meet the real-time user demand, split the real-time user demand to obtain a first demand set;

[0156] Determine a first sub-demand that cannot be satisfied in the first requirement set, and obtain a first instruction type related to the current first sub-demand;

[0157] Extracting a first instruction related to a first instruction type from a first instruction set, and performing instruction adjustment on the first instruction based on a corresponding first sub-demand;

[0158] The adjustment results of each instruction of the first instruction set are integrated to obtain a second instruction set, and the target heating unit is remotely controlled based on the second instruction set.

[0159] In this embodiment, the first demand set refers to a split set obtained by splitting the real-time user demands according to demand types.

[0160] In this embodiment, the first instruction type refers to an instruction type associated with each first sub-requirement in the first requirement set.

[0161] In this embodiment, the second instruction set refers to an instruction set obtained by extracting the first instruction related to the first instruction type from the first instruction set, adjusting the first instruction based on the corresponding first sub-demand, and synthesizing the adjustment results.

[0162] The beneficial effect of the above technical solution is that by optimizing the adjustment control instructions of the target heating unit in combination with real-time user needs, the monitoring and control of the target heating unit can better meet user needs.

[0163] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A remote monitoring system for heating units based on big data analysis, characterized in that: include: Data acquisition module: used for real-time acquisition of operating data of the target heating unit, and real-time transmission to obtain first operating data; Data analysis module: used for performing data processing and data analysis on the first operation data based on the big data analysis technology to obtain a first analysis result; Remote monitoring module: used to optimize the operation of the target heating unit according to the first analysis result, so as to achieve the optimized operation of the target heating unit; Monitoring and interaction module: used to display the optimized operating parameters and equipment status parameters of the target heating unit, and perform remote control based on the display results and real-time user needs.

2. A remote monitoring system for heating units based on big data analysis according to claim 1, characterized in that: Data acquisition module, including: Data collection unit: used to determine the data detection points of the target heating unit, and collect operating data at each data detection point based on preset sensors; Data classification unit: used to classify the collected operation data according to the corresponding data detection points to obtain first classification operation data; Data transmission unit: used for transmitting the first classification operation data of each data detection point in real time, and obtaining the first operation data of the target heating unit based on the transmission result.

3. A remote monitoring system for heating units based on big data analysis according to claim 2, characterized in that: Data analysis modules, including: Data processing unit: used for performing data cleaning and data standardization processing on the first operation data to obtain first processed data; Initial analysis unit: used to analyze the first processed data based on big data analysis technology to obtain initial analysis results; The first analysis unit is used to select the analysis data type with the highest degree of consistency with the target heating unit from the unit analysis database, and extract the corresponding analysis results in the initial analysis results based on the analysis data type to determine the first analysis result of the target heating unit.

4. A remote monitoring system for heating units based on big data analysis according to claim 3, characterized in that: Remote monitoring module, including: A detection and determination unit: used to determine the data detection points and corresponding unit equipment that need to be optimized in the target heating unit based on the first analysis result; A solution determination unit: used for determining a data optimization solution for a current data detection point based on a first analysis result of each data detection point; The first optimization unit is used to integrate the data optimization schemes corresponding to the data detection points of the same unit equipment in the target heating unit to obtain the first equipment optimization scheme; The second optimization unit is used to determine the feasibility of the first equipment optimization plan and optimize the plan to obtain the second equipment optimization plan; The second judgment unit is used to obtain the device association degree of each unit equipment in the target heating unit, and judge whether the unit equipment with device association will affect each other when performing the second equipment optimization plan; The third optimization unit is used to optimize the second equipment optimization plan based on the mutual influence results of the unit equipment to obtain the third equipment optimization plan; The first comprehensive unit is used to integrate the third equipment optimization scheme of each unit equipment in the target heating unit to obtain a first comprehensive optimization scheme; The second comprehensive unit is used to determine whether there is a scheme conflict in the first comprehensive optimization scheme, and adjust the corresponding equipment optimization scheme in the first comprehensive optimization scheme based on the conflicting scheme to obtain a second comprehensive optimization scheme; Optimizing operation unit: used to optimize the operation of the target heating unit based on the second comprehensive optimization plan, so as to achieve optimized operation of the target heating unit.

5. A remote monitoring system for heating units based on big data analysis according to claim 4, characterized in that: The second judging unit comprises: The first association subunit is used to determine the device association degree between each unit device based on the device type and device parameters of each unit device in the target heating unit, and obtain a first association degree set of each unit device; A second association subunit: used for removing the first association degree corresponding to the unit equipment whose first association degree is less than the preset association degree from the first association degree set of each unit equipment, to obtain a second association degree set; Equipment impact subunit: used to sort the first correlation levels in the second correlation level set to obtain an ordered third correlation level set, and determine the equipment impact index of the corresponding unit equipment and the current unit equipment based on each first correlation level in the third correlation level set.

6. A remote monitoring system for heating units based on big data analysis according to claim 5, characterized in that: The equipment affects the subunits, including: Determine the equipment impact index S of the corresponding unit equipment and the current unit equipment; S is the equipment impact index between the unit equipment corresponding to each first correlation degree in the third correlation degree set and the current equipment, α i is the first correlation degree corresponding to the i-th unit equipment in the third correlation degree set, Q i is the real-time heat dissipation of the i-th unit equipment in the third association degree set, Q′ is the heat dissipation of the current unit equipment, and h i is the equipment distance between the i-th unit equipment and the current equipment in the third association degree set, γ i is the heat dissipation degree of the i-th unit equipment within a unit distance, β i is the equipment resonance index between the i-th unit equipment and the current equipment in the third correlation degree set, μ is the heat influence coefficient of the equipment resonance on the unit equipment, and m is the number of the first correlation degree in the third correlation degree set.

7. A remote monitoring system for heating units based on big data analysis according to claim 4, characterized in that: Monitoring interaction module, including: Operation display unit: used to display the optimized operation parameters and equipment status parameters of the target heating unit; A first difference unit: used for obtaining the standard display result of the target heating unit and comparing it with the real-time display result, so as to determine the result difference between each real-time display result and the corresponding standard display result, and obtain a first difference table; A first instruction unit: used for obtaining a difference type of each first difference in the first difference table, and filtering a control instruction matching each first difference in the first difference table from a difference-control database based on the difference type to obtain a first instruction set; Instruction control unit: used to obtain real-time user needs and determine whether the first instruction set can meet the real-time user needs; If satisfied, remotely control the target heating unit based on the first instruction set; If not, the first instruction set is adjusted based on user needs, so that the target heating unit is remotely controlled based on the adjusted instruction set.

8. A remote monitoring system for heating units based on big data analysis according to claim 7, characterized in that: Command control unit, including: When the first instruction set cannot meet the real-time user demand, split the real-time user demand to obtain a first demand set; Determine a first sub-demand that cannot be satisfied in the first requirement set, and obtain a first instruction type related to the current first sub-demand; Extracting a first instruction related to a first instruction type from a first instruction set, and performing instruction adjustment on the first instruction based on a corresponding first sub-demand; The adjustment results of each instruction of the first instruction set are integrated to obtain a second instruction set, and the target heating unit is remotely controlled based on the second instruction set.

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