A method for identifying abnormal operating conditions for smart heating

Abstract

The present invention provides a method for identifying abnormal operating conditions for smart heating, which belongs to the field of abnormal identification technology and specifically includes: obtaining the distances between different individual adjustment units in a sub-area and the thermal power station in the sub-area, and determining the comprehensive abnormal evaluation amounts of the abnormal operating conditions in different sub-areas in combination with the heat user data of the different individual adjustment units and the probability of occurrence of abnormal operating conditions in the sub-area, obtaining the operating data of the abnormal operating conditions of different individual adjustment units in the sub-area, and determining the historical abnormal evaluation amount of the sub-area in combination with the individual adjustment units with abnormal operating conditions in the sub-area, determining the identification frequency of the abnormal operating conditions in the sub-area based on the historical abnormal evaluation amount of the sub-area and the comprehensive abnormal evaluation amount of the abnormal operating conditions, and identifying the abnormal operating conditions through the identification frequency and the operating data of the heating pipe network in the sub-area, thereby further improving the accuracy of identifying the abnormal operating conditions.

Description

Technical Field

[0001] The present invention belongs to the field of abnormality identification technology, and in particular relates to a method for identifying abnormal operating conditions for smart heating. Background Art

[0002] When providing heat through a centralized heating system, abnormal operating conditions such as hydraulic imbalance and abnormal supply and return water temperatures are inevitable. Therefore, if the abnormal conditions cannot be identified and dealt with in a targeted manner, the reliability and safety of the heating system cannot be guaranteed.

[0003] To address the above technical issues, the existing technical solution in invention patent CN202311515645.4, "Smart Heating Abnormal Condition Identification Method and System Based on Federated Learning," extracts multi-dimensional heating data of the heating pipe network at the current timestamp as model input, identifies abnormal heating conditions at the current timestamp, and locates the abnormal heating conditions. However, this solution has the following technical issues:

[0004] Due to the large scale of the heating pipeline network, the use of a unified abnormal operating condition identification model cannot accurately and efficiently identify abnormal operating conditions, making it difficult to meet the requirements for the operational reliability and safety of the heating pipeline network.

[0005] In response to the above technical problems, the present invention provides a method for identifying abnormal operating conditions for smart heating. Summary of the Invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for identifying abnormal operating conditions for smart heating is provided.

[0008] A method for identifying abnormal operating conditions for smart heating, characterized by specifically comprising:

[0009] S1 divides the heating network into different sub-areas according to the heating area of the heating station of the heating network, and determines the probability of occurrence of abnormal operating conditions in different sub-areas based on the distance between different sub-areas and different heat sources and the heating capacity of different heat sources, and proceeds to the next step when the probability of occurrence of abnormal operating conditions in the sub-areas meets the requirements;

[0010] S2 obtains the distances between different individual regulation units in the sub-region and the thermal power station in the sub-region, and determines the comprehensive abnormality assessment amount of the abnormal operating conditions in the different sub-regions in combination with the heat user data of the different individual regulation units and the probability of occurrence of abnormal operating conditions in the sub-regions, and proceeds to the next step when the comprehensive abnormality assessment amount of the abnormal operating conditions in the sub-regions meets the requirements;

[0011] S3: Acquire the operating data of abnormal operating conditions of different independent adjustment units in the sub-region, and determine the historical abnormal evaluation amount of the sub-region based on the independent adjustment units with abnormal operating conditions in the sub-region. When the historical abnormal evaluation amount of the sub-region meets the requirements, proceed to the next step;

[0012] S4 determines the identification frequency of the abnormal operating condition of the sub-area based on the historical abnormal evaluation amount of the sub-area and the comprehensive abnormal evaluation amount of the abnormal operating condition, and identifies the abnormal operating condition through the identification frequency and the operating data of the heating pipe network of the sub-area.

[0013] The beneficial effects of the present invention are:

[0014] 1. The probability of abnormal operating conditions in different sub-areas is determined based on the distance between different sub-areas and different heat sources and the heating capacity of different heat sources. This fully takes into account the technical problem that the edge sub-areas far away from the heat source have a higher probability of abnormal conditions such as hydraulic imbalance. This enables an accurate assessment of the probability of abnormal conditions in the edge sub-areas from the perspective of the distance to the heat source and the heating capacity, thus laying the foundation for the differentiated identification of abnormal conditions in different edge sub-areas.

[0015] 2. The historical abnormal evaluation amount of the sub-region is determined by the operating data of the abnormal working conditions of the individual adjustment units and the individual adjustment units with abnormal working conditions in the sub-region, thereby realizing the screening of sub-regions with more abnormal working conditions from the operating data of abnormal working conditions, and also laying the foundation for further realizing the differentiated identification of abnormal working conditions in sub-regions with more abnormal working conditions.

[0016] 3. The identification frequency of abnormal working conditions in the sub-area is determined based on the historical abnormal working conditions of the sub-area and the comprehensive abnormal working conditions evaluation quantity. This realizes the accurate evaluation of the differences in abnormal working conditions in different sub-areas based on the historical abnormal working conditions of the sub-area and the abnormal evaluation quantity, and lays the foundation for the differentiated evaluation of the identification probability, thereby improving the efficiency of identifying abnormal working conditions.

[0017] A further technical solution is that the distance between the sub-area and different heat sources is determined according to the length of the connecting pipe network between the thermal power station in the sub-area and the different heat sources.

[0018] A further technical solution is that the abnormal operating conditions include hydraulic imbalance, imbalance in the primary pipe network, excessive resistance in the end-user pipe network, and large difference in pipe network resistance.

[0019] A further technical solution is that the individual adjustment units are determined according to heat users who use a unified heating adjustment device, and specifically, heat users who use a unified heating adjustment device within a preset area are used as individual adjustment units.

[0020] A further technical solution is that the distance between the individual regulating unit and the thermal power station of the sub-region is determined according to the length of the connecting pipe network between the individual regulating unit and the thermal power station of the sub-region.

[0021] A further technical solution is that the operating data of the abnormal operating condition includes the type of the abnormal operating condition, the number of occurrences of different types of abnormal operating conditions, and the duration of different occurrences.

[0022] A further technical solution is that the method for determining the identification frequency of the abnormal operating conditions in the sub-area is:

[0023] The abnormal operating condition assessment amount of the sub-area is determined by the historical abnormality assessment amount of the sub-area and the comprehensive abnormality assessment amount of the abnormal operating condition, and the identification frequency of the abnormal operating condition of the sub-area is determined according to the interval in which the abnormal operating condition assessment amount of the sub-area is located.

[0024] On the other hand, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for identifying abnormal operating conditions for smart heating.

[0025] Other features and advantages will be described in the following description, and in part will become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings.

[0028] Figure 1 It is a flow chart of an abnormal operating condition identification method for smart heating;

[0029] Figure 2 is a flow chart of a method for determining the probability of occurrence of an abnormal operating condition in a sub-region;

[0030] Figure 3It is a flow chart of a method for determining a comprehensive abnormality evaluation amount of an abnormal operating condition of a sub-region. DETAILED DESCRIPTION

[0031] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the figures represent like or similar structures, and thus their detailed description will be omitted.

[0032] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "including" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0033] Example 1

[0034] To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a method for identifying abnormal operating conditions for smart heating is provided, which is characterized by specifically comprising:

[0035] S1 divides the heating network into different sub-areas according to the heating area of the heating station of the heating network, and determines the probability of occurrence of abnormal operating conditions in different sub-areas based on the distance between different sub-areas and different heat sources and the heating capacity of different heat sources, and proceeds to the next step when the probability of occurrence of abnormal operating conditions in the sub-areas meets the requirements;

[0036] Furthermore, the distance between the sub-region and different heat sources is determined according to the length of the connecting pipe network between the thermal power station in the sub-region and the different heat sources.

[0037] Specifically, the abnormal operating conditions include hydraulic imbalance, imbalance in the primary pipe network, excessive resistance in the end-user pipe network, and large differences in pipe network resistance.

[0038] In one possible embodiment, Figure 2 As shown, the method for determining the probability of occurrence of the abnormal operating condition of the sub-area in the above step S1 is:

[0039] Determining the probability of occurrence of abnormal operating conditions in the sub-region under different heat sources according to the distance between the sub-region and different heat sources and the heat supply of different heat sources;

[0040] The probability of occurrence of the abnormal operating condition of the sub-region is determined based on the probability of occurrence of the abnormal operating condition of the sub-region under different heat sources.

[0041] In another possible embodiment, the method for determining the occurrence probability of the abnormal operating condition of the sub-area in step S1 is:

[0042] When the distances between the sub-region and different heat sources are all greater than the preset distances, it is determined that the sub-region has a probability of occurrence of an abnormal operating condition that does not meet the requirement;

[0043] When there is a heat source at a distance no greater than a preset distance in the sub-region, the heat sources are divided into adjacent heat sources and distal heat sources according to the distances between the sub-region and the different heat sources. When the heating capacity of the adjacent heat source does not meet the requirement, it is determined that the sub-region has a probability of occurrence of an abnormal operating condition that does not meet the requirement.

[0044] When the heating capacity of the adjacent heat source meets the requirement, the probability of occurrence of abnormal operating conditions in the sub-region under different adjacent heat sources is determined based on the distance between the sub-region and different adjacent heat sources and the heating capacity of different adjacent heat sources, and the probability of abnormal operating conditions in the sub-region under the adjacent heat sources is determined in combination with the number of adjacent heat sources;

[0045] When the probability of abnormal occurrence of the abnormal operating condition of the sub-region under the proximity to the heat source is greater than a preset probability, it is determined that the probability of occurrence of the abnormal operating condition of the sub-region does not meet the requirement;

[0046] When the probability of abnormal occurrence of the abnormal operating condition of the sub-region under the adjacent heat source is not greater than a preset value, the probability of abnormal occurrence of the abnormal operating condition of the sub-region under different remote heat sources is determined based on the distance between the sub-region and different remote heat sources and the heat supply of different remote heat sources, and the probability of abnormal occurrence of the abnormal operating condition of the sub-region under the remote heat source is determined in combination with the number of remote heat sources;

[0047] The probability of occurrence of the abnormal operating condition of the sub-area is determined based on the probability of abnormal occurrence of the abnormal operating condition of the sub-area under the remote heat source and the probability of abnormal occurrence of the abnormal operating condition of the sub-area under the nearby heat source.

[0048] In another possible embodiment, the method for determining the occurrence probability of the abnormal operating condition of the sub-area in step S1 is:

[0049] Dividing the heat sources into adjacent heat sources and distal heat sources based on the distances between the sub-regions and different heat sources, determining whether the heating supply of the adjacent heat sources is less than a preset heating supply, and if so, determining that the probability of occurrence of the abnormal operating condition of the sub-region does not meet the requirement, and if not, proceeding to the next step;

[0050] Determine the probability of occurrence of abnormal operating conditions in the sub-region under different adjacent heat sources based on the distance between the sub-region and different adjacent heat sources and the heating amounts of different adjacent heat sources, and determine the probability of abnormal operating conditions in the sub-region under the adjacent heat sources based on the number of adjacent heat sources, and determine whether the probability of abnormal operating conditions in the sub-region under the adjacent heat sources is greater than a preset probability. If so, determine that the probability of occurrence of abnormal operating conditions in the sub-region does not meet the requirement. If not, proceed to the next step.

[0051] Determining the probability of occurrence of abnormal operating conditions in the sub-region under different remote heat sources based on the distance between the sub-region and different remote heat sources and the heat supply of different remote heat sources, and determining the probability of occurrence of abnormal operating conditions in the sub-region under the remote heat sources based on the number of remote heat sources;

[0052] The probability of occurrence of the abnormal operating condition of the sub-area is determined based on the probability of abnormal occurrence of the abnormal operating condition of the sub-area under the remote heat source and the probability of abnormal occurrence of the abnormal operating condition of the sub-area under the nearby heat source.

[0053] S2 obtains the distances between different individual regulation units in the sub-region and the thermal power station in the sub-region, and determines the comprehensive abnormality assessment amount of the abnormal operating conditions in the different sub-regions in combination with the heat user data of the different individual regulation units and the probability of occurrence of abnormal operating conditions in the sub-regions, and proceeds to the next step when the comprehensive abnormality assessment amount of the abnormal operating conditions in the sub-regions meets the requirements;

[0054] Furthermore, the individual adjustment units are determined according to heat users who use a unified heating adjustment device, and specifically, heat users who use a unified heating adjustment device within a preset area are used as individual adjustment units.

[0055] It should be noted that the distance between the individual regulating unit and the thermal power station of the sub-region is determined according to the length of the connecting pipe network between the individual regulating unit and the thermal power station of the sub-region.

[0056] In one possible embodiment, Figure 3 As shown, the method for determining the comprehensive abnormality assessment value of the abnormal operating condition of the sub-region in the above step S2 is:

[0057] Determine the intra-region abnormal probability of abnormal operating conditions of different individual regulating units according to the distance between different individual regulating units and the thermal power station in the sub-region, and determine the comprehensive abnormal probability of abnormal operating conditions of different individual regulating units in combination with the probability of occurrence of abnormal operating conditions in the sub-region;

[0058] The comprehensive abnormality evaluation amount of the abnormal operating condition of the sub-area is determined based on the comprehensive abnormality probability of the abnormal operating conditions of different individual regulating units, the number of heat users of different individual regulating units, and the heat consumption of different heat users.

[0059] In another possible embodiment, the method for determining the comprehensive abnormality assessment value of the abnormal operating condition of the sub-region in step S2 is:

[0060] When the number of the individual regulating units in the sub-region whose distances from the thermal power station in the sub-region do not meet the requirement is greater than the preset number of units, it is determined that the comprehensive abnormality assessment amount of the abnormal operating condition of the sub-region does not meet the requirement;

[0061] When the number of the individual regulating units in the sub-region whose distances from the thermal power station in the sub-region do not meet the requirements is not greater than the preset number of units, determining the intra-region abnormal probabilities of abnormal operating conditions of different individual regulating units according to the distances between different individual regulating units and the thermal power station in the sub-region, and determining the comprehensive abnormal probabilities of abnormal operating conditions of different individual regulating units in combination with the occurrence probabilities of abnormal operating conditions in the sub-region;

[0062] When the number of individual adjustment units whose comprehensive abnormal probability does not meet the requirement is greater than the preset number of units, it is determined that the comprehensive abnormal evaluation amount of the abnormal operating condition of the sub-area does not meet the requirement;

[0063] When the number of individual adjustment units whose comprehensive abnormality probabilities do not meet the requirements is not greater than a preset number of units, abnormality assessment amounts of different individual adjustment units are determined based on the comprehensive abnormality probabilities of abnormal operating conditions of different individual adjustment units, the number of heat users of different individual adjustment units, and the amount of heat used by different heat users. When the number of individual adjustment units whose abnormality assessment amounts do not meet the requirements is greater than a preset number of units, it is determined that the comprehensive abnormality assessment amount of the abnormal operating condition of the sub-area does not meet the requirements.

[0064] When the number of individual adjustment units whose abnormal assessment amounts do not meet the requirements is not greater than the preset number of units, the comprehensive abnormal assessment amount of the abnormal operating condition of the sub-area is determined based on the abnormal assessment amounts of different individual adjustment units in the sub-area and the number of individual adjustment units whose abnormal assessment amounts do not meet the requirements.

[0065] In another possible embodiment, the method for determining the comprehensive abnormality assessment value of the abnormal operating condition of the sub-region in step S2 is:

[0066] S21 determines the intra-region abnormal probability of abnormal operating conditions of different individual regulating units based on the distance between the different individual regulating units and the thermal power station in the sub-region, and determines the comprehensive abnormal probability of abnormal operating conditions of the different individual regulating units in combination with the probability of occurrence of abnormal operating conditions in the sub-region, and determines whether the number of individual regulating units for which the comprehensive abnormal probability does not meet the requirement is greater than a preset number of units. If so, proceed to the next step; if not, proceed to step S24;

[0067] S22 determines whether the number of heat users of the individual adjustment units whose comprehensive abnormal probability does not meet the requirement is greater than a preset number of users. If so, it is determined that the comprehensive abnormality assessment amount of the abnormal operating condition of the sub-area does not meet the requirement. If not, proceed to the next step.

[0068] S23 determines whether the heat consumption of the heat users of the individual adjustment units whose comprehensive abnormal probability does not meet the requirement is greater than the preset heat consumption. If so, it is determined that the comprehensive abnormality assessment amount of the abnormal operating condition of the sub-area does not meet the requirement. If not, proceed to the next step.

[0069] S24 determines abnormality assessment amounts of different individual adjustment units based on the comprehensive abnormality probabilities of abnormal operating conditions of different individual adjustment units, the number of heat users of different individual adjustment units, and the amount of heat used by different heat users, and determines whether the number of individual adjustment units whose abnormality assessment amounts do not meet the requirements is greater than a preset number of units. If so, it is determined that the comprehensive abnormality assessment amount of the abnormal operating conditions of the sub-area does not meet the requirements. If not, proceed to the next step.

[0070] S25 determines a comprehensive abnormality assessment amount of the abnormal operating condition of the sub-region according to the abnormality assessment amounts of different individual adjustment units of the sub-region and the number of individual adjustment units whose abnormality assessment amounts do not meet the requirements.

[0071] S3: Acquire the operating data of abnormal operating conditions of different independent adjustment units in the sub-region, and determine the historical abnormal evaluation amount of the sub-region based on the independent adjustment units with abnormal operating conditions in the sub-region. When the historical abnormal evaluation amount of the sub-region meets the requirements, proceed to the next step;

[0072] Furthermore, the operation data of the abnormal operating condition includes the type of the abnormal operating condition, the number of occurrences of different types of abnormal operating conditions, and the duration of different occurrences.

[0073] In one possible embodiment, the method for determining the historical abnormality assessment amount of the sub-region in step S3 is:

[0074] Determining the type of the abnormal operating condition of the individual regulating unit based on the operation data of the abnormal operating condition of the individual regulating unit, and determining abnormal unit evaluation amounts of different individual regulating units in combination with the number of occurrences of different types of abnormal operating conditions and the durations of different occurrences;

[0075] The historical abnormality evaluation amount of the sub-region is determined by the abnormal unit evaluation amounts of different individual adjustment units and the number of individual adjustment units with abnormal operating conditions in the sub-region.

[0076] Furthermore, when the probability of occurrence of the abnormal operating condition in the sub-area does not meet the requirement, the abnormal operating condition is identified through a preset identification frequency and the operating data of the heating network in the sub-area.

[0077] Furthermore, when the comprehensive abnormality evaluation value of the abnormal operating condition of the sub-area does not meet the requirements, the abnormal operating condition is identified through a preset identification frequency and the operating data of the heating network of the sub-area.

[0078] Specifically, when the historical abnormality assessment amount of the sub-area does not meet the requirements, the abnormal operating condition is identified through a preset identification frequency and the operating data of the heating network of the sub-area.

[0079] In another possible embodiment, the method for determining the historical abnormality assessment amount of the sub-region in step S3 is:

[0080] When the number of the individual adjustment units with abnormal operating conditions in the sub-region does not meet the requirement, it is determined that the historical abnormality evaluation amount of the sub-region does not meet the requirement;

[0081] When the number of individual adjustment units with abnormal operating conditions in the sub-region meets the requirement, determining the number of occurrences of the abnormal operating conditions of the individual adjustment units based on the operation data of the abnormal operating conditions of the individual adjustment units; when the sum of the number of occurrences of abnormal operating conditions of different individual adjustment units does not meet the requirement, determining that the historical abnormality assessment amount of the sub-region does not meet the requirement;

[0082] When the sum of the number of occurrences of abnormal operating conditions of different individual adjustment units meets the requirement, the type of the abnormal operating condition of the individual adjustment unit is determined based on the operation data of the abnormal operating condition of the individual adjustment unit, and the abnormal unit evaluation amount of the different individual adjustment units is determined in combination with the number of occurrences of different types of abnormal operating conditions and the duration of different occurrences. When the number of individual adjustment units whose abnormal unit evaluation amount does not meet the requirement does not meet the requirement, it is determined that the historical abnormality evaluation amount of the sub-region does not meet the requirement;

[0083] When the number of individual adjustment units whose abnormal unit evaluation amount does not meet the requirement meets the requirement, the historical abnormal evaluation amount of the sub-area is determined by the abnormal unit evaluation amounts of different individual adjustment units and the number of individual adjustment units with abnormal working conditions in the sub-area.

[0084] S4 determines the identification frequency of the abnormal operating condition of the sub-area based on the historical abnormal evaluation amount of the sub-area and the comprehensive abnormal evaluation amount of the abnormal operating condition, and identifies the abnormal operating condition through the identification frequency and the operating data of the heating pipe network of the sub-area.

[0085] In one possible embodiment, the method for determining the identification frequency of the abnormal operating condition of the sub-area in step S4 is:

[0086] The abnormal operating condition assessment amount of the sub-area is determined by the historical abnormality assessment amount of the sub-area and the comprehensive abnormality assessment amount of the abnormal operating condition, and the identification frequency of the abnormal operating condition of the sub-area is determined according to the interval in which the abnormal operating condition assessment amount of the sub-area is located.

[0087] Example 2

[0088] On the other hand, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned method for identifying abnormal operating conditions for smart heating.

[0089] Through the above embodiments, the present application achieves the following technical effects:

[0090] 1. The probability of abnormal operating conditions in different sub-areas is determined based on the distance between different sub-areas and different heat sources and the heating capacity of different heat sources. This fully takes into account the technical problem that the edge sub-areas far away from the heat source have a higher probability of abnormal conditions such as hydraulic imbalance. This enables an accurate assessment of the probability of abnormal conditions in the edge sub-areas from the perspective of the distance to the heat source and the heating capacity, thus laying the foundation for the differentiated identification of abnormal conditions in different edge sub-areas.

[0091] 2. The historical abnormal evaluation amount of the sub-region is determined by the operating data of the abnormal working conditions of the individual adjustment units and the individual adjustment units with abnormal working conditions in the sub-region, thereby realizing the screening of sub-regions with more abnormal working conditions from the operating data of abnormal working conditions, and also laying the foundation for further realizing the differentiated identification of abnormal working conditions in sub-regions with more abnormal working conditions.

[0092] 3. The identification frequency of abnormal working conditions in the sub-area is determined based on the historical abnormal working conditions of the sub-area and the comprehensive abnormal working conditions evaluation quantity. This realizes the accurate evaluation of the differences in abnormal working conditions in different sub-areas based on the historical abnormal working conditions of the sub-area and the abnormal evaluation quantity, and lays the foundation for the differentiated evaluation of the identification probability, thereby improving the efficiency of identifying abnormal working conditions.

[0093] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0094] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A method for identifying abnormal operating conditions for smart heating, characterized in that: Specifically include: Dividing the heating network into different sub-areas according to the heating areas of the heating stations of the heating network, and determining the probability of occurrence of abnormal operating conditions in different sub-areas based on the distances between different sub-areas and different heat sources and the heating amounts of different heat sources, and proceeding to the next step when the probability of occurrence of the abnormal operating conditions in the sub-areas meets the requirements; Obtaining the distances between different individual regulation units in the sub-region and the thermal power station in the sub-region, and determining comprehensive abnormality assessment quantities of abnormal operating conditions in different sub-regions based on the heat user data of the different individual regulation units and the probability of occurrence of abnormal operating conditions in the sub-regions, and proceeding to the next step when the comprehensive abnormality assessment quantities of the abnormal operating conditions in the sub-regions meet the requirements; Acquiring operation data of abnormal operating conditions of different independent adjustment units in the sub-region, and determining a historical abnormality assessment amount of the sub-region in combination with the independent adjustment units with abnormal operating conditions in the sub-region, and proceeding to the next step when the historical abnormality assessment amount of the sub-region meets the requirements; The identification frequency of the abnormal operating condition in the sub-area is determined based on the historical abnormal evaluation amount of the sub-area and the comprehensive abnormal evaluation amount of the abnormal operating condition, and the abnormal operating condition is identified through the identification frequency and the operating data of the heating pipe network in the sub-area.

2. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: The distance between the sub-region and different heat sources is determined according to the length of the connecting pipe network between the thermal power station in the sub-region and the different heat sources.

3. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: The abnormal operating conditions include hydraulic imbalance, imbalance in the primary pipe network, excessive resistance in the end-user pipe network, and large gap in pipe network resistance.

4. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: The method for determining the occurrence probability of abnormal operating conditions in the sub-area is: Determining the probability of occurrence of abnormal operating conditions in the sub-region under different heat sources according to the distance between the sub-region and different heat sources and the heat supply of different heat sources; The probability of occurrence of the abnormal operating condition of the sub-region is determined based on the probability of occurrence of the abnormal operating condition of the sub-region under different heat sources.

5. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: The individual regulating units are determined according to the heat users using a unified heating regulating device.

6. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: The method for determining the comprehensive abnormality evaluation value of the abnormal operating conditions of the sub-area is: Determine the intra-region abnormal probability of abnormal operating conditions of different individual regulating units according to the distance between different individual regulating units and the thermal power station in the sub-region, and determine the comprehensive abnormal probability of abnormal operating conditions of different individual regulating units in combination with the probability of occurrence of abnormal operating conditions in the sub-region; The comprehensive abnormality evaluation amount of the abnormal operating condition of the sub-area is determined based on the comprehensive abnormality probability of the abnormal operating conditions of different individual regulating units, the number of heat users of different individual regulating units, and the heat consumption of different heat users.

7. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: When the probability of occurrence of the abnormal operating condition in the sub-area does not meet the requirement, the abnormal operating condition is identified by using a preset identification frequency and the operating data of the heating pipe network in the sub-area.

8. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: When the historical abnormality assessment amount of the sub-area does not meet the requirements, the abnormal operating condition is identified through the preset identification frequency and the operating data of the heating pipe network of the sub-area.

9. The abnormal operating condition identification method for smart heating according to claim 1, characterized in that: The method for determining the historical anomaly assessment amount of the sub-region is: When the number of the individual adjustment units with abnormal operating conditions in the sub-region does not meet the requirement, it is determined that the historical abnormality evaluation amount of the sub-region does not meet the requirement; When the number of individual adjustment units with abnormal operating conditions in the sub-region meets the requirement, determining the number of occurrences of the abnormal operating conditions of the individual adjustment units based on the operation data of the abnormal operating conditions of the individual adjustment units; when the sum of the number of occurrences of abnormal operating conditions of different individual adjustment units does not meet the requirement, determining that the historical abnormality assessment amount of the sub-region does not meet the requirement; When the sum of the number of occurrences of abnormal operating conditions of different individual adjustment units meets the requirement, the type of the abnormal operating condition of the individual adjustment unit is determined based on the operation data of the abnormal operating condition of the individual adjustment unit, and the abnormal unit evaluation amount of the different individual adjustment units is determined in combination with the number of occurrences of different types of abnormal operating conditions and the duration of different occurrences. When the number of individual adjustment units whose abnormal unit evaluation amount does not meet the requirement does not meet the requirement, it is determined that the historical abnormality evaluation amount of the sub-region does not meet the requirement; When the number of individual adjustment units whose abnormal unit evaluation amount does not meet the requirement meets the requirement, the historical abnormal evaluation amount of the sub-area is determined by the abnormal unit evaluation amounts of different individual adjustment units and the number of individual adjustment units with abnormal working conditions in the sub-area.

10. A computer system comprising: A memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes the abnormal operating condition identification method for smart heating described in any one of claims 1-9.

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