Heat supply pipe network reliability evaluation method considering extreme weather

Abstract

The invention provides a heat supply pipe network reliability evaluation method considering extreme weather, and belongs to the technical field of central heating, and the method specifically comprises the steps: determining snowfall intervals corresponding to the extreme weather according to the extreme weather, and determining the occurrence probability of a designated area in different snowfall intervals based on historical weather data, determining a comprehensive demand reliability coefficient based on the demand reliability coefficients under the different snowfall intervals and the occurrence probability under the snowfall intervals, and determining a comprehensive heat supply reliability coefficient based on the heat supply reliability coefficients under the different snowfall intervals and the occurrence probability under the snowfall intervals; and the comprehensive reliability coefficient is determined based on the comprehensive heat supply reliability coefficient and the comprehensive demand reliability coefficient, and the maintenance suggestion of the heat supply pipe network is output based on the comprehensive reliability coefficient, so that the heat supply reliability is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of centralized heating, and in particular relates to a method for evaluating the reliability of a heating network taking extreme weather into consideration. Background Art

[0002] Extreme weather is a general term for rare weather phenomena that are destructive to human society and the ecosystem. In extreme weather, the weather and climate state deviate seriously from the average state, and it is a low-probability event that occurs once every few decades or even once every hundred years. In layman's terms, extreme weather and climate events refer to low-probability events that occur once every 50 years or 100 years. Due to the lack of corresponding data, how to evaluate the reliability of heating pipe networks under extreme weather has become a technical problem that needs to be solved urgently.

[0003] The existing technical solutions for evaluating the heating reliability of the heating network under extreme weather conditions are still lacking. In response to the above technical problems, in order to improve the operational reliability of the heating network, the present invention provides a method for evaluating the reliability of the heating network taking extreme weather conditions into consideration. Summary of the invention

[0004] A method for evaluating the reliability of a heating network considering extreme weather conditions, characterized by comprising: S1 determines the snowfall range corresponding to the extreme weather according to the extreme weather, and determines the occurrence probability of the specified area in different snowfall ranges based on historical weather data; S2 determines the environmental predicted temperatures of different snowfall intervals based on different snowfall intervals, determines the heating demand of different thermal power stations under different snowfall intervals by using the environmental predicted temperatures, determines the demand reliability coefficients under different snowfall intervals in combination with the maximum heating supply of the heat source, and determines the comprehensive demand reliability coefficient based on the demand reliability coefficients under different snowfall intervals and the occurrence probability under the snowfall intervals; S3 determines the damage risk of different heating network areas under different snowfall intervals by using the predicted ambient temperature, and determines the heating reliability coefficient under different snowfall intervals in combination with the users affected by the failures in different heating network areas and the amount of heat consumed by the users affected by the failures, and determines the comprehensive heating reliability coefficient based on the heating reliability coefficient under different snowfall intervals and the probability of occurrence under the snowfall intervals; S4 determines a comprehensive reliability coefficient based on the comprehensive heating reliability coefficient and the comprehensive demand reliability coefficient, and outputs maintenance recommendations for the heating network based on the comprehensive reliability coefficient.

[0005] The beneficial effects of the present invention are: In the present application, the comprehensive heating reliability coefficient is determined based on the heating reliability coefficient under different snowfall ranges and the probability of occurrence under snowfall ranges. Not only the heating reliability under different snowfall ranges but also the probability of occurrence under different types of snowfall ranges are taken into account. This achieves a comprehensive evaluation of the comprehensive reliability coefficient of the heating network under extreme weather conditions, and also lays the foundation for the output of further differentiated maintenance recommendations for the heating network.

[0006] In the present application, maintenance recommendations for the heating network are output based on the comprehensive reliability coefficient, thereby avoiding the technical problem of insufficient heating reliability for heat users due to insufficient heating reliability of the heating network under extreme weather conditions, and achieving the output of optimization recommendations for the heating network's monitoring equipment, thermal power stations and heating network, thereby improving the reliability of heating treatment.

[0007] A further technical solution is that the snowfall range corresponding to the extreme weather is determined based on the historical weather data of the designated area.

[0008] A further technical solution is to determine the probability of occurrence of different snowfall ranges in a specified area based on historical weather data, including: Determine the historical snowfall times in the designated area under different snowfall ranges through the historical weather data, and use the historical snowfall times as the matching snowfall times; Determine the comprehensive snowfall credibility of different matching snowfall times according to the snowfall data of the matching snowfall times and the time interval between the snowfall time and the current time; The occurrence probability of different snowfall ranges is determined based on the comprehensive snowfall credibility of different matching snowfall times, the matching snowfall times and the interval time between different adjacent historical snowfall times.

[0009] A further technical solution is that the snowfall data includes snowfall duration and snowfall amount in different time periods within the snowfall duration.

[0010] A further technical solution is that the predicted ambient temperature is determined based on an average value of ambient temperatures of different historical snowfall times in the snowfall range.

[0011] A further technical solution is to output maintenance suggestions for the heating network based on the comprehensive reliability coefficient, specifically including: When the comprehensive reliability coefficient is greater than a preset reliability threshold, it is determined that there is no need to output a maintenance suggestion for the heating network; When the comprehensive reliability coefficient is not greater than the preset reliability threshold, the layout density of the thermal power station and the setting density of the Internet of Things monitoring equipment are determined according to the comprehensive reliability coefficient, and maintenance recommendations for the heating network are output based on the layout density of the thermal power station and the setting density of the Internet of Things monitoring equipment.

[0012] On the other hand, an embodiment of the present application provides a heat supply network reliability assessment system considering extreme weather, which adopts the above-mentioned heat supply network reliability assessment method considering extreme weather, and is characterized in that it specifically includes: Probabilistic assessment module, demand reliability assessment module, heating reliability assessment module, optimization module; The probability assessment module is responsible for determining the snowfall range corresponding to the extreme weather according to the extreme weather, and determining the probability of occurrence of the specified area in different snowfall ranges based on historical weather data; The demand reliability assessment module is responsible for determining the environmental forecast temperature of different snowfall intervals based on different snowfall intervals, determining the heating demand of different thermal power stations under different snowfall intervals through the environmental forecast temperature, and determining the demand reliability coefficient under different snowfall intervals in combination with the maximum heating supply of the heat source, and determining the comprehensive demand reliability coefficient based on the demand reliability coefficient under different snowfall intervals and the occurrence probability under the snowfall interval; The heating reliability assessment module is responsible for determining the damage risk of different heating network areas under different snowfall intervals by using the predicted ambient temperature, and determining the heating reliability coefficient under different snowfall intervals in combination with the users affected by the failures in different heating network areas and the amount of heat consumed by the users affected by the failures, and determining the comprehensive heating reliability coefficient based on the heating reliability coefficient under different snowfall intervals and the probability of occurrence under the snowfall intervals; The optimization module is responsible for determining a comprehensive reliability coefficient based on the comprehensive heating reliability coefficient and the comprehensive demand reliability coefficient, and outputting maintenance recommendations for the heating network based on the comprehensive reliability coefficient.

[0013] On the other hand, a computer-readable storage medium is provided in an embodiment of the present application, on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned method for evaluating the reliability of a heating network considering extreme weather.

[0014] On the other hand, a computer program product is provided in an embodiment of the present application, characterized in that the computer program product stores instructions, and when the instructions are executed by a computer, the computer implements the above-mentioned method for evaluating the reliability of a heating pipeline network considering extreme weather. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] 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 attached drawings.

[0016] Figure 1 It is a flow chart of a method for evaluating the reliability of a heating network considering extreme weather conditions.

[0017] Figure 2 It is a flowchart that determines the probability of occurrence of different snowfall ranges in a specified area based on historical weather data.

[0018] Figure 3 It is a flow chart of a method for determining the comprehensive demand reliability coefficient. DETAILED DESCRIPTION

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of ways and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0020] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" 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.

[0021] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a method for evaluating the reliability of a heating network considering extreme weather is provided, which is characterized by specifically including: S1 determines the snowfall range corresponding to the extreme weather according to the extreme weather, and determines the occurrence probability of the specified area in different snowfall ranges based on historical weather data; S2 determines the environmental predicted temperatures of different snowfall intervals based on different snowfall intervals, determines the heating demand of different thermal power stations under different snowfall intervals by using the environmental predicted temperatures, determines the demand reliability coefficients under different snowfall intervals in combination with the maximum heating supply of the heat source, and determines the comprehensive demand reliability coefficient based on the demand reliability coefficients under different snowfall intervals and the occurrence probability under the snowfall intervals; S3 determines the damage risk of different heating network areas under different snowfall intervals by using the predicted ambient temperature, and determines the heating reliability coefficient under different snowfall intervals in combination with the users affected by the failures in different heating network areas and the amount of heat consumed by the users affected by the failures, and determines the comprehensive heating reliability coefficient based on the heating reliability coefficient under different snowfall intervals and the probability of occurrence under the snowfall intervals; S4 determines a comprehensive reliability coefficient based on the comprehensive heating reliability coefficient and the comprehensive demand reliability coefficient, and outputs maintenance recommendations for the heating network based on the comprehensive reliability coefficient.

[0022] Furthermore, the snowfall range corresponding to the extreme weather is determined based on the historical weather data of the designated area.

[0023] Specifically, Figure 2 As shown in the figure, the probability of occurrence of different snowfall ranges in a specified area is determined based on historical weather data, including: Determine the historical snowfall times in the designated area under different snowfall ranges through the historical weather data, and use the historical snowfall times as the matching snowfall times; Determine the comprehensive snowfall credibility of different matching snowfall times according to the snowfall data of the matching snowfall times and the time interval between the snowfall time and the current time; The occurrence probability of different snowfall ranges is determined based on the comprehensive snowfall credibility of different matching snowfall times, the matching snowfall times and the interval time between different adjacent historical snowfall times.

[0024] Furthermore, the snowfall data includes snowfall duration and snowfall amount in different time periods within the snowfall duration.

[0025] Optionally, the predicted ambient temperature is determined based on an average value of ambient temperatures of different historical snowfall times in the snowfall range.

[0026] It should be noted that the method for determining the demand reliability coefficient under the snowfall range is: Determine the fluctuation range of the heating demand of different heating stations within the snowfall range based on the predicted ambient temperature, determine the historical probability of different heating demands within the fluctuation range according to the historical duration ratio of the heating demand within the fluctuation range, and determine the correction value of the heating demand of the heating station in combination with the fluctuation range of the heating demand; The demand reliability coefficient under different snowfall ranges is determined by the correction value of the heat demand and the maximum heat supply of the heat source.

[0027] Furthermore, the value range of the demand reliability coefficient in different snowfall intervals is between 0 and 1, wherein the larger the demand reliability coefficient is, the higher the heating reliability in the snowfall interval is.

[0028] Specifically, Figure 3 As shown, the method for determining the comprehensive demand reliability coefficient is: According to the occurrence probability of different snowfall ranges and the demand reliability coefficients of different snowfall ranges, the modified reliability coefficients of different snowfall ranges are determined; The comprehensive demand reliability coefficient is determined based on the modified reliability coefficients under different snowfall ranges and the number of snowfall ranges in which the modified reliability coefficients do not meet the requirements.

[0029] In another embodiment, the method for determining the comprehensive demand reliability coefficient is: Based on the demand reliability coefficients under different snowfall intervals, the snowfall interval in which the demand reliability coefficient does not meet the requirements is determined, and it is used as the problem snowfall interval, and it is judged whether the sum of the occurrence probabilities under the problem snowfall interval is greater than the preset probability. If so, the comprehensive demand reliability coefficient is determined by the demand reliability coefficient of the problem snowfall interval, if not, proceed to the next step; Obtain the number of the problem snowfall intervals, and determine the modified demand reliability coefficient of the problem snowfall interval in combination with the occurrence probabilities of different problem snowfall intervals and the demand reliability coefficients of different problem snowfall intervals, and judge whether the modified demand reliability coefficient of the problem snowfall interval meets the requirements, if so, proceed to the next step, if not, determine the comprehensive demand reliability coefficient through the modified demand reliability coefficient of the problem snowfall interval; The snowfall interval in which the demand reliability coefficient is greater than the preset reliability coefficient is taken as the reliable snowfall interval, and it is determined whether the sum of the occurrence probabilities of the reliable snowfall intervals is greater than the preset probability. If so, the comprehensive demand reliability coefficient is determined by the demand reliability coefficient of the reliable snowfall interval. If not, proceed to the next step. Obtain the number of the reliable snowfall intervals, and determine the modified demand reliability coefficient of the reliable snowfall interval in combination with the occurrence probabilities of different reliable snowfall intervals and the demand reliability coefficients of different reliable snowfall intervals, and determine the modified reliability coefficients under different snowfall intervals according to the occurrence probabilities under different snowfall intervals and the demand reliability coefficients under different snowfall intervals; The comprehensive demand reliability coefficient is determined based on the corrected reliability coefficients under different snowfall intervals, the corrected demand reliability coefficients of the reliable snowfall interval, and the corrected demand reliability coefficients of the problematic snowfall interval.

[0030] Optionally, the method for determining the comprehensive demand reliability coefficient is: Determine the snowfall interval whose demand reliability coefficient does not meet the requirements based on the demand reliability coefficient under different snowfall intervals, and use it as the problem snowfall interval, obtain the number of the problem snowfall intervals, and determine the corrected demand reliability coefficient of the problem snowfall interval in combination with the occurrence probabilities of different problem snowfall intervals and the demand reliability coefficients of different problem snowfall intervals; when the corrected demand reliability coefficient of the problem snowfall interval does not meet the requirements, determine the comprehensive demand reliability coefficient through the corrected demand reliability coefficient of the problem snowfall interval; When the corrected demand reliability coefficient of the problematic snowfall interval meets the requirements, the snowfall interval whose demand reliability coefficient is greater than the preset reliability coefficient is taken as the reliable snowfall interval, the number of the reliable snowfall intervals is obtained, and the corrected demand reliability coefficient of the reliable snowfall interval is determined in combination with the occurrence probabilities of different reliable snowfall intervals and the demand reliability coefficients of different reliable snowfall intervals. When the corrected demand reliability coefficient of the reliable snowfall interval is greater than the preset reliability coefficient, the comprehensive demand reliability coefficient is determined by the corrected demand reliability coefficient of the reliable snowfall interval. When the corrected demand reliability coefficient of the reliable snowfall interval is not greater than the preset reliability coefficient, the corrected reliability coefficients for different snowfall intervals are determined according to the occurrence probabilities of different snowfall intervals and the demand reliability coefficients for different snowfall intervals, and the comprehensive demand reliability coefficient is determined based on the corrected reliability coefficients for different snowfall intervals, the corrected demand reliability coefficient of the reliable snowfall interval and the corrected demand reliability coefficient of the problematic snowfall interval.

[0031] Furthermore, the method for determining the damage risk of the heating network area is: Based on the predicted ambient temperatures under different snowfall ranges, the number of leakage and damage times of different heating pipe network areas under different predicted ambient temperatures is determined, and the damage risks of different heating pipe network areas under different snowfall ranges are determined based on the leakage and damage times.

[0032] Specifically, the method for determining the heating reliability coefficient in the snowfall range is: Determine the heating reliability coefficient of different heating pipe network areas in the snowfall range according to the damage risk of different heating pipe network areas in the snowfall range, the number of users affected by the failure of different heating pipe network areas, and the amount of heat consumed by users affected by the failure; The number of heating pipe network areas with damage risks in the snowfall range is obtained, and the heating reliability coefficient in the snowfall range is determined in combination with the heating reliability coefficients of different heating pipe network areas in the snowfall range.

[0033] It should be noted that the method for determining the comprehensive heating reliability coefficient is: According to the occurrence probability of different snowfall ranges and the heat supply reliability coefficients in different snowfall ranges, the heat supply reliability correction coefficients in different snowfall ranges are determined; The comprehensive heating reliability coefficient is determined based on the heating reliability correction coefficients under different snowfall intervals and the number of snowfall intervals in which the heating reliability correction coefficients do not meet the requirements.

[0034] In another embodiment, the method for determining the comprehensive heating reliability coefficient is: S31 determines the snowfall interval in which the heating reliability coefficient does not meet the requirements based on the heating reliability coefficient under different snowfall intervals, and uses it as the deviation snowfall interval to determine whether there is the deviation snowfall interval, if yes, proceed to step S33, if not, proceed to the next step; S32: The snowfall interval in which the heating reliability coefficient is greater than the preset reliability coefficient is used as the heating reliable snowfall interval, and it is determined whether the sum of the occurrence probabilities of the heating reliable snowfall intervals is greater than the preset probability. If so, the comprehensive heating reliability coefficient is determined by the heating reliability coefficient of the heating reliable snowfall interval. If not, proceed to the next step. S33 obtains the number of the deviation snowfall intervals, and determines the reliability coefficient evaluation amount of the deviation snowfall interval in combination with the occurrence probabilities of different deviation snowfall intervals and the demand reliability coefficients of different deviation snowfall intervals, and judges whether the reliability coefficient evaluation amount of the deviation snowfall interval meets the requirements, if so, proceeds to the next step, if not, determines the comprehensive demand reliability coefficient through the reliability coefficient evaluation amount of the deviation snowfall interval; S34: obtaining the number of the reliable heating snowfall intervals, and determining the reliability coefficient evaluation amount of the reliable heating snowfall intervals in combination with the occurrence probabilities of different reliable heating snowfall intervals and the demand reliability coefficients of different reliable heating snowfall intervals, and determining the modified heating reliability coefficients under different snowfall intervals according to the occurrence probabilities under different snowfall intervals and the heating reliability coefficients under different snowfall intervals; S35 determines the comprehensive demand reliability coefficient based on the corrected heating reliability coefficient under different snowfall intervals, the reliability coefficient evaluation value of the heating reliable snowfall interval, and the reliability coefficient evaluation value of the problem snowfall interval.

[0035] Furthermore, outputting maintenance suggestions for the heating network based on the comprehensive reliability coefficient specifically includes: When the comprehensive reliability coefficient is greater than a preset reliability threshold, it is determined that there is no need to output a maintenance suggestion for the heating network; When the comprehensive reliability coefficient is greater than a preset reliability threshold, it is determined that there is no need to output a maintenance suggestion for the heating network; When the comprehensive reliability coefficient is not greater than the preset reliability threshold, the layout density of the thermal power station and the setting density of the Internet of Things monitoring equipment are determined according to the comprehensive reliability coefficient, and maintenance recommendations for the heating network are output based on the layout density of the thermal power station and the setting density of the Internet of Things monitoring equipment.

[0036] Example 2 On the other hand, an embodiment of the present application provides a heat supply network reliability assessment system considering extreme weather, which adopts the above-mentioned heat supply network reliability assessment method considering extreme weather, and is characterized in that it specifically includes: Probabilistic assessment module, demand reliability assessment module, heating reliability assessment module, optimization module; The probability assessment module is responsible for determining the snowfall range corresponding to the extreme weather according to the extreme weather, and determining the probability of occurrence of the specified area in different snowfall ranges based on historical weather data; The demand reliability assessment module is responsible for determining the environmental forecast temperature of different snowfall intervals based on different snowfall intervals, determining the heating demand of different thermal power stations under different snowfall intervals through the environmental forecast temperature, and determining the demand reliability coefficient under different snowfall intervals in combination with the maximum heating supply of the heat source, and determining the comprehensive demand reliability coefficient based on the demand reliability coefficient under different snowfall intervals and the occurrence probability under the snowfall interval; The heating reliability assessment module is responsible for determining the damage risk of different heating network areas under different snowfall intervals by using the predicted ambient temperature, and determining the heating reliability coefficient under different snowfall intervals in combination with the users affected by the failures in different heating network areas and the amount of heat consumed by the users affected by the failures, and determining the comprehensive heating reliability coefficient based on the heating reliability coefficient under different snowfall intervals and the probability of occurrence under the snowfall intervals; The optimization module is responsible for determining a comprehensive reliability coefficient based on the comprehensive heating reliability coefficient and the comprehensive demand reliability coefficient, and outputting maintenance recommendations for the heating network based on the comprehensive reliability coefficient.

[0037] Example 3 On the other hand, a computer-readable storage medium is provided in an embodiment of the present application, on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned method for evaluating the reliability of a heating network considering extreme weather.

[0038] Example 4 On the other hand, a computer program product is provided in an embodiment of the present application, characterized in that the computer program product stores instructions, and when the instructions are executed by a computer, the computer implements the above-mentioned method for evaluating the reliability of a heating pipeline network considering extreme weather.

[0039] In the embodiments of the present invention, the term "plurality" refers to two or more than two, unless otherwise clearly defined. The terms "installation", "connection", "fixation" and the like should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection. For those skilled in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0040] In the description of the embodiments of the present invention, it needs to be understood that the directions or positional relationships indicated by the terms "upper" and "lower" are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or unit referred to must have a specific direction, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the embodiments of the present invention.

[0041] In the description of this specification, the description of the terms "one embodiment", "a preferred embodiment", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the reliability of a heating network considering extreme weather, characterized in that: Specifically include: Determine the snowfall range corresponding to the extreme weather according to the extreme weather, and determine the probability of occurrence of different snowfall ranges in a specified area based on historical weather data; Determine the environmental forecast temperature of different snowfall intervals based on different snowfall intervals, determine the heating demand of different thermal power stations under different snowfall intervals according to the environmental forecast temperature, and determine the demand reliability coefficient under different snowfall intervals in combination with the maximum heating supply of the heat source, and determine the comprehensive demand reliability coefficient based on the demand reliability coefficient under different snowfall intervals and the occurrence probability under the snowfall interval; The damage risk of different heating network areas under different snowfall intervals is determined by using the predicted ambient temperature, and the heating reliability coefficient under different snowfall intervals is determined in combination with the users affected by the failures in different heating network areas and the amount of heat consumed by the users affected by the failures, and the comprehensive heating reliability coefficient is determined based on the heating reliability coefficient under different snowfall intervals and the probability of occurrence under the snowfall intervals; A comprehensive reliability coefficient is determined based on the comprehensive heating reliability coefficient and the comprehensive demand reliability coefficient, and maintenance recommendations for the heating network are output based on the comprehensive reliability coefficient.

2. The reliability assessment method of a heating network considering extreme weather conditions according to claim 1, characterized in that: The snowfall range corresponding to the extreme weather is determined based on the historical weather data of the designated area.

3. The reliability assessment method of a heating network considering extreme weather conditions as claimed in claim 1, characterized in that: Determine the probability of snowfall in a given area in different ranges based on historical weather data, including: Determine the historical snowfall times in the designated area under different snowfall ranges through the historical weather data, and use the historical snowfall times as the matching snowfall times; Determine the comprehensive snowfall credibility of different matching snowfall times according to the snowfall data of the matching snowfall times and the time interval between the snowfall time and the current time; The occurrence probability of different snowfall ranges is determined based on the comprehensive snowfall credibility of different matching snowfall times, the matching snowfall times and the interval time between different adjacent historical snowfall times.

4. The reliability assessment method of a heating network considering extreme weather conditions as claimed in claim 1, characterized in that: The snowfall data includes snowfall duration and snowfall amount in different time periods within the snowfall duration.

5. The reliability assessment method of a heating network considering extreme weather conditions according to claim 1, characterized in that: The method for determining the demand reliability coefficient under the snowfall range is: Determine the fluctuation range of the heating demand of different heating stations within the snowfall range based on the predicted ambient temperature, determine the historical probability of different heating demands within the fluctuation range according to the historical duration ratio of the heating demand within the fluctuation range, and determine the correction value of the heating demand of the heating station in combination with the fluctuation range of the heating demand; The demand reliability coefficient under different snowfall ranges is determined by the correction value of the heat demand and the maximum heat supply of the heat source.

6. The reliability assessment method of a heating network considering extreme weather conditions according to claim 1, characterized in that: The value range of the demand reliability coefficient in the different snowfall intervals is between 0 and 1, wherein the larger the demand reliability coefficient is, the higher the heating reliability in the snowfall interval is.

7. The reliability assessment method of a heating network considering extreme weather conditions as claimed in claim 1, characterized in that: The method for determining the comprehensive demand reliability coefficient is: According to the occurrence probability of different snowfall ranges and the demand reliability coefficients of different snowfall ranges, the modified reliability coefficients of different snowfall ranges are determined; The comprehensive demand reliability coefficient is determined based on the modified reliability coefficients under different snowfall ranges and the number of snowfall ranges in which the modified reliability coefficients do not meet the requirements.

8. The reliability assessment method of a heating network considering extreme weather conditions as claimed in claim 1, characterized in that: The method for determining the heating reliability coefficient under the snowfall range is: Determine the heating reliability coefficient of different heating pipe network areas in the snowfall range according to the damage risk of different heating pipe network areas in the snowfall range, the number of users affected by the failure of different heating pipe network areas, and the amount of heat consumed by users affected by the failure; The number of heating pipe network areas with damage risks in the snowfall range is obtained, and the heating reliability coefficient in the snowfall range is determined in combination with the heating reliability coefficients of different heating pipe network areas in the snowfall range.

9. The reliability assessment method of a heating network considering extreme weather conditions as claimed in claim 1, characterized in that: The method for determining the comprehensive heating reliability coefficient is: According to the occurrence probability of different snowfall ranges and the heat supply reliability coefficients in different snowfall ranges, the heat supply reliability correction coefficients in different snowfall ranges are determined; The comprehensive heating reliability coefficient is determined based on the heating reliability correction coefficients under different snowfall intervals and the number of snowfall intervals in which the heating reliability correction coefficients do not meet the requirements.

10. A heat supply network reliability assessment system considering extreme weather, using a heat supply network reliability assessment method considering extreme weather as described in any one of claims 1 to 9, characterized in that: Specifically include: Probabilistic assessment module, demand reliability assessment module, heating reliability assessment module, optimization module; The probability assessment module is responsible for determining the snowfall range corresponding to the extreme weather according to the extreme weather, and determining the probability of occurrence of the specified area in different snowfall ranges based on historical weather data; The demand reliability assessment module is responsible for determining the environmental forecast temperature of different snowfall intervals based on different snowfall intervals, determining the heating demand of different thermal power stations under different snowfall intervals through the environmental forecast temperature, and determining the demand reliability coefficient under different snowfall intervals in combination with the maximum heating supply of the heat source, and determining the comprehensive demand reliability coefficient based on the demand reliability coefficient under different snowfall intervals and the occurrence probability under the snowfall interval; The heating reliability assessment module is responsible for determining the damage risk of different heating network areas under different snowfall intervals by using the predicted ambient temperature, and determining the heating reliability coefficient under different snowfall intervals in combination with the users affected by the failures in different heating network areas and the amount of heat consumed by the users affected by the failures, and determining the comprehensive heating reliability coefficient based on the heating reliability coefficient under different snowfall intervals and the probability of occurrence under the snowfall intervals; The optimization module is responsible for determining a comprehensive reliability coefficient based on the comprehensive heating reliability coefficient and the comprehensive demand reliability coefficient, and outputting maintenance recommendations for the heating network based on the comprehensive reliability coefficient.

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