Heat exchange station equipment operation information acquisition and generation method based on big data analysis

Abstract

The invention relates to the field of data processing, in particular to a heat exchange station equipment operation information acquisition and generation method based on big data analysis, which comprises the following steps: periodically detecting a temperature reference value of each target heating user to obtain an average temperature reference value and a temperature difference value; a temperature analysis mode is determined according to the average temperature reference value and the temperature difference value, and the temperature analysis mode comprises the steps that detection is conducted on temperature fluctuation values in a preset fluctuation detection range, and whether compensation analysis is conducted on the temperature reference values of all the target heating users or not is determined according to the distribution state of all the types of users; determining a setting mode of an early warning time period according to the heating effect trend and the temperature difference value; and if the equipment state is under the heat exchange early warning condition, equipment state early warning is carried out, and the equipment operation information processing efficiency in the equipment operation maintenance process is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method for collecting and generating operation information of heat exchange station equipment based on big data analysis. Background Art

[0002] The operating status of the heat exchange station equipment has a great impact on the actual effect of the heating system, the energy conversion efficiency and the operation and maintenance cost. During the actual operation of the heat exchange station, the amount of data of the operating information of each device in the heat exchange station is extremely large. If all the data are analyzed, it is easy to lead to low analysis efficiency. The existing methods for collecting and generating the operating information of the heat exchange station equipment often fail to consider screening the equipment operating information to reduce the pressure of data analysis. Therefore, how to screen out the equipment operating information that really needs to be analyzed according to the actual working process is a problem that needs to be solved urgently by technical personnel in this field.

[0003] Chinese patent publication number CN118361780A discloses a method, system and medium for intelligent inspection and adjustment of equipment based on big data, which belongs to the technical field of big data and water heating facilities. It includes the following steps: through the temperature monitoring of the heat exchange station composed of a temperature collector and a monitoring platform, collect and record the temperature parameters of the heat exchange station and the user's room; collect the temperature of multiple water inflows and multiple water outflows of the heat exchange station; store the data in the database as a historical record for future reference, and understand and grasp the operating status of the equipment through the temperature monitoring of the heat exchange station; according to the temperature setting values ​​before and after the design of the user's indoor temperature, batch simulate the data to obtain heating energy consumption data; based on the data, batch simulate the heating energy consumption data, conduct indoor space distribution research for different users, analyze the reasons and explore the rules, and determine the air temperature threshold. The above scheme only considers how to improve the accuracy of the temperature threshold setting, and fails to screen the operation information of the heat exchange station equipment according to the actual working effect and environmental influencing factors, which leads to low efficiency in analyzing the equipment status. Summary of the invention

[0004] To this end, the present invention provides a method for collecting and generating operation information of heat exchange station equipment based on big data analysis, so as to overcome the problem in the prior art that the operation information of heat exchange station equipment is not screened according to actual working results and environmental influencing factors, thereby resulting in low efficiency in analyzing the equipment status.

[0005] To achieve the above-mentioned purpose, the present invention provides a method for collecting and generating operation information of heat exchange station equipment based on big data analysis, comprising:

[0006] Periodically test the temperature reference value of each target heating user to obtain the average temperature reference value and the temperature difference value;

[0007] Determine a temperature analysis method according to the average temperature reference value and the temperature difference value, the temperature analysis method including detecting the temperature fluctuation value within a preset fluctuation detection range, and determining whether to perform compensation analysis on the temperature reference value of each target heating user according to the distribution state of each type of user;

[0008] Determine the setting method of the warning period according to the heating effect trend and temperature difference value;

[0009] If the equipment is in the heat exchange warning condition, a device status warning will be issued.

[0010] Furthermore, the average temperature reference value is the average value of the temperature reference values ​​of each target heating user within the temperature detection period;

[0011] The temperature difference value is the ratio of the sum of the absolute values ​​of the differences between the temperature reference values ​​of each target heating user and the average temperature reference value within the temperature detection period to the number of the target heating users.

[0012] Further, if the average temperature reference value is not within the preset temperature range and the temperature difference value is less than or equal to the preset temperature difference value, a temperature fluctuation value within the preset fluctuation detection range is detected;

[0013] If the temperature fluctuation value is greater than the preset temperature fluctuation value, the heating effect trend is determined according to the temperature fluctuation value to determine the setting method of the early warning period.

[0014] Further, the heating effect trend is determined according to the turning point state within the preset fluctuation detection range, and the turning point state is determined according to the change of the average temperature reference value of the warning information within the preset fluctuation detection range;

[0015] If the average temperature reference values ​​of two adjacent temperature detection cycles of a temperature detection cycle are both greater than the average temperature reference value of the temperature detection cycle, the end time of the temperature detection cycle is recorded as the growth turning point;

[0016] If the average temperature reference values ​​of two adjacent temperature detection cycles of a temperature detection cycle are both less than or equal to the average temperature reference value of the temperature detection cycle, the end time of the temperature detection cycle is recorded as a descending turning point;

[0017] The heating effect state includes a fluctuation rising state and a fluctuation falling state.

[0018] Further, if the temperature difference value is greater than a preset temperature difference value, it is determined whether to perform compensation analysis on the temperature reference value of each target heating user according to the distribution state of a type of user;

[0019] The first type of users are target heating users whose temperature reference values ​​in the current temperature detection cycle are not within a preset temperature range.

[0020] Furthermore, for a class of users in the same service scope,

[0021] For a single type of user, the distribution state includes a concentrated distribution state and a discrete distribution state;

[0022] If the proportion of users of one category in the preset reference set of users of one category is greater than the preset proportion of users of one category, it is determined that the users of one category are in a concentrated distribution state;

[0023] If the proportion of one type of users in the preset reference set of one type of users is less than or equal to the preset proportion of one type of users, it is determined that the one type of users is in a discrete distribution state.

[0024] Furthermore, if there is a type of user in a concentrated distribution state, a compensation analysis is performed on the temperature reference value of each target heating user, and the coincidence of the light cycles of each target heating user is detected, and it is determined whether to perform compensation adjustment on the temperature reference value of the corresponding target heating user according to the coincidence of the light cycles of each target heating user;

[0025] For a single target heating user, the light cycle overlap is the ratio of the light overlap duration to the temperature detection cycle, and the light overlap duration is the overlap duration between the current temperature detection cycle and the reference light cycle of the target heating user.

[0026] Furthermore, for a single target heating user, if the light cycle overlap is greater than a preset light cycle overlap, the temperature reference value for the target heating user is reduced and adjusted according to the radiation enhancement coefficient;

[0027] The decrease value of the temperature reference value is positively correlated with the radiation enhancement coefficient;

[0028] The radiation enhancement coefficient is determined according to the light cycle overlap and the reference radiation intensity.

[0029] Furthermore, if the heat exchange warning condition is met, the equipment status warning is carried out according to the operation information of each heat exchange station equipment within the warning period;

[0030] The heat exchange warning condition is that the average temperature reference value in the current temperature detection cycle is not within the preset temperature range, or the temperature difference value before and after the compensation analysis is performed is greater than the preset temperature difference value.

[0031] Furthermore, the setting method of the warning period includes:

[0032] For the current temperature detection period, if the preset fluctuation detection range of the current temperature detection period is not in an upward trend or a downward trend or the temperature difference values ​​are greater than the preset temperature difference values, the current temperature detection period is set as a warning period;

[0033] If the preset fluctuation detection range in which the current temperature detection cycle is located is in an upward trend or a downward trend, the number of temperature detection cycles included in the warning period is determined according to the fluctuation cycle ratio;

[0034] The number of temperature detection cycles included in the warning period is positively correlated with the proportion of fluctuation cycles;

[0035] The fluctuation period ratio is the ratio of the number of fluctuation periods within the preset fluctuation detection range to the number of temperature detection periods within the preset fluctuation detection range.

[0036] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme of the present invention, the operating status of each device in the heat exchange station is judged according to the actual heating effect of each target heating user, and the judgment result of the actual heating effect is compensated and adjusted according to environmental influencing factors. The equipment operation information that needs to be further analyzed is screened based on the target user data after the compensation adjustment. The present invention improves the processing efficiency of the equipment operation information during the equipment operation and maintenance process.

[0037] Furthermore, the present invention periodically detects the temperature reference value of each target heating user to obtain an average temperature reference value and a temperature difference value, and determines the temperature analysis method based on the average temperature reference value and the temperature difference value. While monitoring the actual heating effect in real time, it ensures that the subsequent analysis process is more in line with the actual situation, making the evaluation of the actual heating effect more accurate, thereby improving the accuracy of the early warning period and the effectiveness of the screened equipment operation information.

[0038] Furthermore, the present invention determines whether to perform compensation analysis on the temperature reference value of each target heating user according to the distribution status of a category of users; if there are target heating users in a concentrated distribution state, compensation analysis is performed on the temperature reference value of each target user to reduce the impact of environmental factors on the temperature reference value of some users, thereby improving the accuracy of the evaluation of the actual heating effect. The present invention improves the accuracy of the acquired warning period.

[0039] Furthermore, in the present invention, if a compensation analysis is performed on the temperature reference value of each target user, it is determined whether to perform a compensatory adjustment on the temperature reference value of the corresponding target heating user according to the overlap of the light cycles of each target heating user, and the temperature reference value is adjusted according to the radiation enhancement coefficient, so as to avoid the accuracy of the actual heating effect evaluation being affected by environmental factors and ensure the accuracy of the subsequent warning period selection results.

[0040] Furthermore, the present invention determines the setting method of the warning period according to the heating trend effect and the temperature difference value. If the preset fluctuation detection range of the current temperature detection cycle is in an upward trend or a downward trend, the number of temperature detection cycles included in the warning period is determined according to the proportion of the fluctuation cycle, thereby ensuring the accuracy of the warning period selection result, and further ensuring the validity of the screened equipment operation information. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of a method for collecting and generating operation information of heat exchange station equipment based on big data analysis of the present invention;

[0042] Figure 2 A flow chart of the present invention for determining a temperature analysis method according to an average temperature reference value and a temperature difference value;

[0043] Figure 3 It is a flow chart of the present invention for determining whether to perform compensation analysis on the temperature reference value of each target heating user according to the distribution state of a type of users;

[0044] Figure 4 The present invention is a flow chart for determining whether to perform compensation adjustment on the temperature reference value of the corresponding target heating user according to the light cycle overlap of each target heating user. DETAILED DESCRIPTION

[0045] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0047] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0048] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] See also Figures 1 to 4 As shown, the present invention provides a method for collecting and generating operation information of heat exchange station equipment based on big data analysis, comprising:

[0050] Periodically test the temperature reference value of each target heating user to obtain the average temperature reference value and the temperature difference value;

[0051] Determine a temperature analysis method according to the average temperature reference value and the temperature difference value, the temperature analysis method including detecting the temperature fluctuation value within a preset fluctuation detection range, and determining whether to perform compensation analysis on the temperature reference value of each target heating user according to the distribution state of each type of user;

[0052] Determine the setting method of the warning period according to the heating effect trend and temperature difference value;

[0053] If the equipment is in the heat exchange warning condition, a device status warning will be issued.

[0054] Among them, the target heating users are users who receive heating services from the heat exchanger station to which the equipment that needs to monitor its operating status belongs. The target heating users are divided into service areas according to the house information registered by each target heating user. For target heating users within the same service area, the community information and building number information of their registered house information are the same.

[0055] Specifically, the average temperature reference value is the average value of the temperature reference values ​​of each target heating user within the temperature detection period;

[0056] The temperature difference value is the ratio of the sum of the absolute values ​​of the differences between the temperature reference values ​​of each target heating user and the average temperature reference value within the temperature detection period to the number of target heating users.

[0057] Among them, the present invention applies a cyclic temperature detection cycle, and the duration of the temperature detection cycle can be determined by the user. The higher the user's requirements for the stability of the operation of the heat exchange station equipment, the shorter the duration of the temperature detection cycle. A temperature detection cycle duration is provided, and the temperature detection cycle is 30 minutes. At the end of each temperature detection cycle, the indoor temperature of each target heating user at the current moment is detected, and the indoor temperature corresponding to each target heating user is recorded as the temperature reference value of the corresponding target heating user.

[0058] Specifically, if the average temperature reference value is not within the preset temperature range and the temperature difference value is less than or equal to the preset temperature difference value, a temperature fluctuation value within the preset fluctuation detection range is detected;

[0059] If the temperature fluctuation value is greater than the preset temperature fluctuation value, the heating effect trend within the preset fluctuation detection range is detected to determine the setting method of the early warning period.

[0060] Among them, the user can set the preset temperature range according to the actual heating requirements and historical records, record the minimum value of the preset temperature range as the first preset temperature, and record the maximum value of the preset temperature range as the second preset temperature. A method for obtaining the first preset temperature and the second preset temperature is provided, record the minimum value of the user's actual heating requirement as the first preset temperature, and record the average value of the average temperature reference values ​​obtained in each detection in the historical records in which the temperature reference values ​​of each target heating user are greater than the first preset temperature as the second preset temperature. If the average temperature reference value of a temperature detection cycle is less than or equal to the first preset temperature, or greater than the second preset temperature, it is determined that the average temperature reference value of the temperature detection cycle is not within the preset temperature range. If the average temperature reference value of a temperature detection cycle is greater than the first preset temperature and less than or equal to the second preset temperature, it is determined that the average temperature reference value of the temperature detection cycle is within the preset temperature range.

[0061] The value of the preset temperature difference value can be set by the user according to actual needs and historical records. The higher the accuracy requirement of the user for the obtained warning period, the smaller the value of the preset temperature difference value is. A method for setting the value of the preset temperature difference value is provided. After completing the compensation analysis, the average value of the temperature difference values ​​of each temperature detection cycle in the historical records that meet the user's accuracy requirement for the obtained warning period is recorded as the preset temperature difference value;

[0062] The duration of the preset fluctuation detection range can be set by the user according to actual needs and historical records. The higher the user's requirements for the stability of the operating state of the heat exchange station equipment, the longer the duration of the preset fluctuation detection range. A method for determining the duration of the preset fluctuation detection range is provided. The maximum duration of the rising trend or the falling trend in the historical records is recorded as the duration of the preset fluctuation detection range. A value of the duration of the preset fluctuation detection range is provided. The value of the duration of the preset fluctuation detection range is 8 times the duration of the temperature detection cycle of the current application.

[0063] The temperature fluctuation value is the ratio of the sum of the absolute values ​​of the differences between the average temperature reference values ​​of each temperature detection period within the preset fluctuation detection period and the reference average value to the number of temperature detection periods within the preset detection period. The reference average value is the average value of the average temperature reference values ​​of each temperature detection period within the preset fluctuation detection period. The user can set the preset temperature fluctuation value according to actual needs and historical records. The higher the user's requirements for the stability of the operating state of the heat exchange station equipment, the smaller the value of the preset temperature fluctuation value. A method for determining the preset temperature fluctuation value is provided, and the average value of the temperature fluctuation values ​​within the time range corresponding to each rising trend and falling trend in the historical record that meets the user's requirements for the stability of the operating state of the heat exchange station equipment is recorded as the preset temperature fluctuation value.

[0064] Specifically, the heating effect trend is determined according to the turning point state within the preset fluctuation detection range, and the turning point state is determined according to the change of the average temperature reference value of the warning information within the preset fluctuation detection range;

[0065] If the average temperature reference values ​​of two adjacent temperature detection cycles of a temperature detection cycle are both greater than the average temperature reference value of the temperature detection cycle, the end time of the temperature detection cycle is recorded as the growth turning point;

[0066] If the average temperature reference values ​​of two adjacent temperature detection cycles of a temperature detection cycle are both less than or equal to the average temperature reference value of the temperature detection cycle, the end time of the temperature detection cycle is recorded as a descending turning point;

[0067] The heating effect state includes a fluctuation rising state and a fluctuation falling state.

[0068] Among them, each turning point is the end time of the temperature detection cycle. For the current temperature detection cycle, if the average temperature reference value of the temperature detection cycle corresponding to each growth turning point within the preset fluctuation detection range is greater than the previous growth turning point, the preset fluctuation detection range is in an upward trend; if the average temperature reference value of the temperature detection cycle corresponding to each decline turning point within the preset fluctuation detection range is less than the previous decline turning point, the preset fluctuation detection range is in a downward trend.

[0069] Specifically, if the temperature difference value is greater than the preset temperature difference value, it is determined whether to perform compensation analysis for the temperature reference value of each target heating user according to the distribution state of a type of user;

[0070] The first type of users are target heating users whose temperature reference values ​​in the current temperature detection cycle are not within a preset temperature range.

[0071] Specifically, for a category of users in the same service scope,

[0072] If the proportion of users of a category in the preset reference set of users of a category is greater than the preset proportion of users of the category, it is determined that the users of the category are in a concentrated distribution state;

[0073] If the proportion of a class of users in a preset reference set of a class of users is less than or equal to the preset proportion of a class of users, it is determined that the class of users is in a discrete distribution state.

[0074] Among them, the preset reference set is a set of target heating users who have the same orientation as this type of users and whose height difference value is less than the preset height difference value. If the houses of two target heating users can receive direct sunlight in the same direction, it is determined that the two target heating users have the same orientation. The height difference value is the absolute value of the difference in the lowest heights of the houses of the two target heating users. The user can set the value of the preset height difference value according to actual needs. The higher the user's requirement for the accuracy of the obtained warning time period, the smaller the preset height difference value. A preset height difference value of 6m is provided.

[0075] The proportion of one type of users is the ratio of the number of one type of users in the preset reference set to the number of target heating users in the preset reference set. The value of the proportion of one type of users can be set by the user according to actual needs and historical records. The higher the user's requirement for the accuracy of the obtained warning period, the smaller the value of the proportion of one type of users is. A value of the proportion of one type of users is provided, and the value of the proportion of one type of users is 0.5.

[0076] Specifically, if there is a type of user in a concentrated distribution state, a compensation analysis is performed on the temperature reference value of each target heating user, and the coincidence of the light cycles of each target heating user is detected, and it is determined whether to perform compensation adjustment on the temperature reference value of the corresponding target heating user according to the coincidence of the light cycles of each target heating user;

[0077] For a single target heating user, the light cycle overlap is the ratio of the light overlap duration to the temperature detection cycle, and the light overlap duration is the overlap duration between the current temperature detection cycle and the reference light cycle of the target heating user.

[0078] Among them, for the reference light cycle of a single target heating user, the starting time of the reference light cycle is the time when the house first begins to receive direct sunlight in a day, and the end time of the reference light cycle is the time when it cannot receive direct sunlight. After the end time of the reference light cycle, the house of the target heating user cannot receive direct sunlight on the current date. The user can determine it by performing light simulation for target heating users at different positions and heights. The present invention does not make specific restrictions on the building sunlight calculation software used. How to perform light simulation is easy to understand for technicians in this field and will not be repeated here. However, it is worth noting that during the heating service period of the heat exchange station, each target heating user has a different reference light cycle in different light monitoring intervals. Whenever the light monitoring cycle ends, light simulation is performed again to determine the reference light cycle in the next light monitoring cycle. The user can set the duration of the light monitoring cycle according to actual needs. The higher the user's accuracy requirements for the obtained warning period, the shorter the duration of the light monitoring cycle. A light monitoring cycle duration is provided, and the duration of the light monitoring cycle is 10 days.

[0079] Specifically, for a single target heating user, if the light cycle overlap is greater than a preset light cycle overlap, the temperature reference value for the target heating user is reduced and adjusted according to the radiation enhancement coefficient;

[0080] The decrease value of the temperature reference value is positively correlated with the radiation enhancement coefficient;

[0081] The radiation enhancement coefficient is determined according to the light cycle overlap and the reference radiation intensity.

[0082] Among them, since light can easily affect the temperature reference value of some target heating users, thereby affecting the accuracy of the judgment result of the actual heating effect, the temperature reference value of the target heating user is reduced and adjusted according to the radiation enhancement coefficient to improve the accuracy of the judgment result of the actual heating effect and ensure the reliability of the selection result of the early warning period.

[0083] The value of the preset light cycle overlap can be set by the user according to actual needs and historical records. The higher the user's requirements for the accuracy of the obtained warning period, the smaller the value of the preset light cycle overlap. A method for setting the value of the preset light cycle overlap is provided, and historical records in which the temperature difference value is greater than the preset temperature difference value and the temperature reference value is not adjusted are recorded as reference records, and the maximum value of the light cycle overlap of each target heating user in the reference record that meets the user's requirements for the accuracy of the obtained warning period is recorded as the preset light cycle overlap.

[0084] The radiation enhancement coefficient is the natural logarithm of the product of the overlap of the lighting cycle and the reference radiation intensity. At the end of each temperature detection cycle, the solar radiation intensity at the current moment is detected, and the solar radiation intensity at this moment is recorded as the reference radiation intensity of the temperature detection cycle. How to detect the solar radiation intensity is easy for technicians in this field to understand and will not be elaborated here.

[0085] Specifically, if the heat exchange warning condition is met, the equipment status warning is carried out based on the operation information of each heat exchange station equipment within the warning period;

[0086] The heat exchange warning condition is that the average temperature reference value in the current temperature detection cycle is not within the preset temperature range, or the temperature difference value before and after the compensation analysis is performed is greater than the preset temperature difference value.

[0087] Specifically, the setting method of the warning period includes:

[0088] For the current temperature detection period, if the current temperature detection period is not in an upward trend or a downward trend or the temperature difference values ​​are greater than the preset temperature difference values, the current temperature detection period is set as a warning period;

[0089] If the preset fluctuation detection range in which the current temperature detection cycle is located is in an upward trend or a downward trend, the number of temperature detection cycles included in the warning period is determined according to the fluctuation cycle ratio;

[0090] The number of temperature detection cycles included in the warning period is positively correlated with the proportion of fluctuation cycles;

[0091] The fluctuation period ratio is the ratio of the number of fluctuation periods within the preset fluctuation detection range to the number of temperature detection periods within the preset fluctuation detection range.

[0092] Wherein, the fluctuation period is a temperature detection period in which the absolute value of the difference between the average temperature reference value and the average temperature reference value of the adjacent temperature detection period is greater than the preset temperature fluctuation value;

[0093] The warning period is determined according to the proportion of fluctuation cycles. While reducing the pressure of operating information analysis, the validity of the equipment operating information that needs further analysis is guaranteed, effectively avoiding the situation where the actual heating effect does not meet the actual requirements or energy waste occurs.

[0094] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for collecting and generating equipment operation information of a heat exchange station based on big data analysis, characterized in that: include: Periodically test the temperature reference value of each target heating user to obtain the average temperature reference value and the temperature difference value; Determine a temperature analysis method according to the average temperature reference value and the temperature difference value, the temperature analysis method including detecting the temperature fluctuation value within a preset fluctuation detection range, and determining whether to perform compensation analysis on the temperature reference value of each target heating user according to the distribution state of each type of user; Determine the setting method of the warning period according to the heating effect trend and temperature difference value; If the equipment is in the heat exchange warning condition, a device status warning will be issued.

2. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 1 is characterized in that: The average temperature reference value is the average value of the temperature reference values ​​of each target heating user within the temperature detection period; The temperature difference value is the ratio of the sum of the absolute values ​​of the differences between the temperature reference values ​​of each target heating user and the average temperature reference value within the temperature detection period to the number of the target heating users.

3. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 2 is characterized in that: If the average temperature reference value is not within the preset temperature range and the temperature difference value is less than or equal to the preset temperature difference value, a temperature fluctuation value within the preset fluctuation detection range is detected; If the temperature fluctuation value is greater than the preset temperature fluctuation value, the heating effect trend is determined according to the temperature fluctuation value to determine the setting method of the early warning period.

4. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 3 is characterized in that: The heating effect trend is determined according to the turning point state within the preset fluctuation detection range, and the turning point state is determined according to the change of the average temperature reference value of the warning information within the preset fluctuation detection range; If the average temperature reference values ​​of two adjacent temperature detection cycles of a temperature detection cycle are both greater than the average temperature reference value of the temperature detection cycle, the end time of the temperature detection cycle is recorded as the growth turning point; If the average temperature reference values ​​of two adjacent temperature detection cycles of a temperature detection cycle are both less than or equal to the average temperature reference value of the temperature detection cycle, the end time of the temperature detection cycle is recorded as a descending turning point; The heating effect state includes a fluctuation rising state and a fluctuation falling state.

5. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 4 is characterized in that: If the temperature difference value is greater than the preset temperature difference value, determine whether to perform compensation analysis for the temperature reference value of each target heating user according to the distribution state of a type of user; The first type of users are target heating users whose temperature reference values ​​in the current temperature detection cycle are not within a preset temperature range.

6. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 5 is characterized in that: For a class of users in the same service scope, If the proportion of users of a category in the preset reference set of users of a category is greater than the preset proportion of users of the category, it is determined that the users of the category are in a concentrated distribution state; If the proportion of a class of users in a preset reference set of a class of users is less than or equal to the preset proportion of a class of users, it is determined that the class of users is in a discrete distribution state.

7. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 6 is characterized in that: If there is a type of user in a concentrated distribution state, a compensation analysis is performed on the temperature reference value of each target heating user, and the coincidence of the light cycles of each target heating user is detected. According to the coincidence of the light cycles of each target heating user, it is determined whether to perform compensation adjustment on the temperature reference value of the corresponding target heating user; For a single target heating user, the light cycle overlap is the ratio of the light overlap duration to the temperature detection cycle, and the light overlap duration is the overlap duration between the current temperature detection cycle and the reference light cycle of the target heating user.

8. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 7 is characterized in that: For a single target heating user, if the light cycle overlap is greater than the preset light cycle overlap, the temperature reference value for the target heating user is reduced and adjusted according to the radiation enhancement coefficient; The decrease value of the temperature reference value is positively correlated with the radiation enhancement coefficient; The radiation enhancement coefficient is determined according to the light cycle overlap and the reference radiation intensity.

9. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 8 is characterized in that: If the heat exchange warning condition is met, the equipment status warning will be issued based on the operation information of each heat exchange station equipment within the warning period; The heat exchange warning condition is that the average temperature reference value in the current temperature detection cycle is not within the preset temperature range, or the temperature difference value before and after the compensation analysis is performed is greater than the preset temperature difference value.

10. The method for collecting and generating heat exchange station equipment operation information based on big data analysis according to claim 9 is characterized in that: The setting method of the warning period includes: For the current temperature detection period, if the preset fluctuation detection range of the current temperature detection period is not in an upward trend or a downward trend or the temperature difference values ​​are greater than the preset temperature difference values, the current temperature detection period is set as a warning period; If the preset fluctuation detection range in which the current temperature detection cycle is located is in an upward trend or a downward trend, the number of temperature detection cycles included in the warning period is determined according to the fluctuation cycle ratio; The number of temperature detection cycles included in the warning period is positively correlated with the proportion of fluctuation cycles; The fluctuation period ratio is the ratio of the number of fluctuation periods within the preset fluctuation detection range to the number of temperature detection periods within the preset fluctuation detection range.

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