Heating detection method and device, storage medium and program product

Abstract

The invention discloses a heating detection method and device, a storage medium and a program product, and relates to the technical field of the Internet of Things, and the heating detection method comprises the steps: determining detection data associated with a current heating mode based on obtained local monitoring data, and judging whether the detection data is in a preset reference data range of the current heating mode or not; when the detection data is not in the reference data range, recording early warning information; and under the condition that the early warning information meets the preset warning condition, a heating pipeline valve is closed, and warning information is output. According to the method, the abnormal heating condition is automatically judged and processed, the manual intervention process is reduced, and timely warning and timely response to heating abnormity are achieved, so that the heating detection efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things, and in particular to a heating detection method, device, storage medium and program product. Background Art

[0002] With the accelerated urbanization process, the central heating system has become a common heating method in modern cities as an efficient and environmentally friendly heating method. Floor heating, as a terminal heat dissipation method of central heating, is widely used because of its uniform heat and high comfort.

[0003] At present, the floor heating pressure test before heating starts requires a dedicated person to stay at the test site to promptly detect and deal with possible leakage problems; at the same time, someone needs to be sent to confirm each household to ensure the accuracy and completeness of the test results. After the heating starts, if the user finds problems such as water leakage, he needs to contact the heating unit by phone, and the heating unit will arrange on-site inspection and repair. This process not only has a long response time, but also has low efficiency and is difficult to meet the user's demand for immediate service.

[0004] Although the current Internet of Things platform can support the access, management and scheduling of a large number of devices and has been widely used in the industrial field, such as smart electricity meters and smart water meters, it has not been effectively applied in floor heating pressure testing and heating system monitoring, resulting in management and control still relying on manual methods and low efficiency of heating detection.

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide a heating detection method, device, storage medium and program product, aiming to improve the efficiency of heating detection.

[0007] To achieve the above objectives, the present application proposes a heating detection method, the method comprising: Based on the acquired local monitoring data, determining the detection data associated with the current heating mode, and judging whether the detection data is within the reference data range preset for the current heating mode; When the detection data is not within the reference data range, recording warning information; When the early warning information reaches the preset alarm condition, the heating pipe valve is closed and the alarm information is output.

[0008] In one embodiment, the step of determining the detection data associated with the current heating mode based on the acquired local monitoring data includes: In the case where the current heating mode is the daily mode, determining the water inlet flow value and the water return flow value of the heating pipe valve from the local monitoring data, and determining the difference between the water inlet flow value and the water return flow value as the detection data associated with the current heating mode; In the case where the current heating mode is the pressure mode, the pressure value and flow change of the heating pipe valve are determined from the local monitoring data, and the pressure value and the flow change are determined as the detection data associated with the current heating mode.

[0009] In one embodiment, before the step of determining whether the detection data is within the reference data range preset in the current heating mode, the method further includes: Preprocess the acquired historical data to obtain a learning data set; Determine a prediction result of the reference data range based on the learning data set using a machine learning model; Based on the prediction result, the reference data range is adjusted.

[0010] In one embodiment, before the step of determining the detection data associated with the current heating mode based on the acquired local monitoring data, the method further includes: A physical model of each local device is established, and local monitoring data uploaded by each local device is obtained through each physical model.

[0011] In one embodiment, the method is applied to a heating detection system, the heating detection system comprising a heating detection device and a controller, and after the step of establishing a physical model of each local device, further comprising: In the case where the controller is connected to the heating detection device for the first time, the detection rules are sent to the controller through the heating detection device, wherein the controller is a physical model of the heating pipe valve.

[0012] In one embodiment, after the step of outputting the alarm information, the method further includes: According to the alarm information, a maintenance work order is generated by the heating detection device; When the heating detection device and the controller communicate normally and a completed maintenance work order is received, a valve opening instruction is sent to the controller through the heating detection device.

[0013] In one embodiment, after the step of outputting the alarm information, the method further includes: In the case where the communication between the heating detection device and the controller is abnormal, detecting whether there is a flow value reporting situation through the controller; In the case where the flow value is reported, the controller determines whether to open the heating pipe valve according to the change of the acquired pressure value.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a heating detection device, which comprises: A judgment module, configured to determine the detection data associated with the current heating mode based on the acquired local monitoring data, and to judge whether the detection data is within a preset reference data range of the current heating mode; A recording module, used for recording warning information when the detection data is not within the reference data range; The alarm module is used to close the heating pipe valve and output the alarm information when the early warning information reaches the preset alarm condition.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a heating detection device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the heating detection method described above.

[0016] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the heating detection method described above are implemented.

[0017] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the heating detection method described above are implemented.

[0018] One or more technical solutions proposed in this application have at least the following technical effects: based on the acquired local monitoring data, the detection data associated with the current heating mode is determined, so as to realize the real-time perception and data extraction of the operating status of the heating equipment and ensure the accuracy of monitoring for different operating modes; then, it is judged whether the detection data is within the preset reference data range of the current heating mode, and by instantly evaluating the detection data, it is helpful to find potential problems or signs of deviation from normal operation as early as possible; when the detection data is not within the reference data range, the early warning information is recorded, so as to realize the recording and tracking of abnormal situations, and help to find and solve problems in time; further, when the early warning information reaches the preset alarm condition, the heating pipe valve is closed and the alarm information is output, so as to reduce losses and prevent accidents from further expanding by quickly responding to and effectively controlling potential risks, and at the same time, the output of the alarm information also ensures that relevant personnel can understand the abnormal situation in time and take countermeasures. The automated response mechanism in this application reduces the time of manual judgment and operation, realizes effective monitoring and timely response to the operating status of the heating pipe system, ensures heating safety and rapid processing of abnormal situations, and thus improves the efficiency of heating detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A schematic diagram of a flow chart provided for the first embodiment of the heating detection method of the present application; Figure 2 An overall working overview of the heating detection provided in Example 1 of the present application; Figure 3 A schematic diagram of the physical model interaction of the heating detection method provided in the second embodiment of the present application; Figure 4 This is a schematic diagram of the module structure of the heating detection device according to an embodiment of the present application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the heating detection method in the embodiment of the present application. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] In this embodiment, for ease of description, the following description is made taking the heating detection terminal as the execution entity.

[0025] In conventional technology, floor heating pressure testing requires the on-site participation of residents and heating units, which is an inefficient process. In addition, due to the uncertainty of residents' schedules, it is often necessary to arrange some re-pressure tests, which increases the workload and cost. In addition, if residents fail to keep someone to observe, once problems such as water leakage occur, it may cause greater losses. In daily use, if residents find abnormal conditions such as water leakage, they still need to call the heating unit to report the problem. This process is not only cumbersome, but also may have a significant impact on residents' daily lives if it is not handled in a timely manner.

[0026] The present application provides a solution, firstly, a physical model of each local device is established to interact with the local device, thereby improving the efficiency of data collection and processing and reducing manual intervention; then, based on the acquired local monitoring data, the detection data for the current heating mode is determined, thereby realizing real-time capture of the operating status of the heating equipment and ensuring the accuracy of monitoring; then, it is determined whether the detection data is within the reference data range preset for the current heating mode, and by instantly evaluating the detection data, it is helpful to discover potential problems or signs of deviation from normal operation as early as possible, and provide data support for subsequent early warning or response; regarding the setting of the reference data range, a machine learning model can be used to learn historical data, thereby predicting the reference data range, improving the accuracy and rationality of the range setting, and reducing false alarms of abnormal situations; in the case where the detection data is not within the reference data range, the warning information is recorded, a preliminary response to the abnormal situation is realized, and a tracking clue is provided for subsequent troubleshooting; further, in the case where the warning information reaches the preset alarm condition, the heating pipe valve is closed and the alarm information is output, and the alarm condition is set to reduce false alarms, and the efficiency of heating detection can be improved through rapid response and effective control.

[0027] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes the heating detection terminal as an example of the execution subject to illustrate this embodiment and the following embodiments.

[0028] Based on this, the present application embodiment provides a heating detection method, referring to Figure 1 , Figure 1This is a flow chart of the first embodiment of the heating detection method of the present application.

[0029] In this embodiment, the heating detection method includes steps S10 to S30: Step S10, based on the acquired local monitoring data, determining the detection data associated with the current heating mode, and judging whether the detection data is within the reference data range preset for the current heating mode; It should be noted that local monitoring data refers to the raw data collected by local equipment during operation, which can reflect local environmental conditions; detection data refers to the data set extracted or calculated from local monitoring data for specific detection purposes, including evaluating whether the equipment or system is operating normally.

[0030] In addition, it should be noted that the heating mode refers to the operating state or configuration of the heating equipment or heating system under specific conditions. Each mode has its own specific parameters and performance indicators; the reference data range refers to a set of data ranges or thresholds preset for each heating mode, which are used to determine whether the detection data is in a normal or acceptable state.

[0031] For example, if the local device has the ability to communicate over the network, the local detection data collected by the local device can be directly obtained from the local device, but this direct acquisition method will make data parsing difficult. It is also possible to establish a physical model to clarify the format, type, and range of the data, so that after obtaining the local detection data uploaded by the local device, the data uploaded by the device can be correctly parsed and identified. For example, the pressure sensor (local device) uploads a data packet, which contains the current pressure value, timestamp and other information. The physical model defines the attribute name of the current pressure value as "pressure", the data type is a floating point number, the unit is Pascal (Pa), and the valid range of the data is specified. After receiving the data packet, the IoT platform will parse the current pressure value according to the definition in the physical model, and store it or directly use it for subsequent comparative analysis.

[0032] For example, in daily mode, the normal range of floor heating flow is approximately between 2 and 3 cubic meters per hour; in pressure mode, a pressure drop of no more than 0.03Mpa can be considered as a normal range. These data can be used to determine whether the heating system is operating normally and whether there are possible leaks or other problems.

[0033] Step S20, when the detection data is not within the reference data range, record warning information; It should be noted that the warning information refers to the possible abnormal situation information that is automatically recorded when the detection data does not meet the reference data range.

[0034] For example, during routine inspections of heating pipes, it was found that the flow difference between the water inlet and the return outlet of a certain section was 2L (detection data), but under normal circumstances the flow difference should be below 1L (reference data range). Therefore, it was judged that there might be a water leak in this section of the pipe, and the information was recorded.

[0035] It is understandable that real-time monitoring and recording of early warning information can help to promptly detect potential problems and subsequently analyze and judge related abnormal situations.

[0036] Step S30, when the early warning information reaches the preset alarm condition, the heating pipe valve is closed and the alarm information is output.

[0037] It should be noted that preset alarm conditions refer to pre-set conditions for triggering specific actions or responses, such as the number, duration or severity of warning information. Heating pipe valves refer to devices used to control the flow of fluids in heating pipes, including valves at the water inlet and return outlet of heating pipes, which can control the flow of pipes by opening, closing or adjusting the valve opening.

[0038] In addition, it should be noted that the alarm information is sent to users (such as managers and residents) to indicate water leakage problems or other emergency situations, so that users can take remedial measures in a timely manner.

[0039] For example, during daily use, if a section of the heating pipe is found to have three abnormal reports within 10 seconds (preset alarm conditions), the corresponding heating pipe valve will be automatically closed, and an abnormal notification will be sent to residents and the heating department, prompting the heating department to inspect and repair the abnormal pipe, reminding local residents that there is a problem with the current heating and that repairs will be arranged immediately.

[0040] For example, if a physical model is used to obtain local monitoring data and control pipeline valves, when the communication between the heating equipment and the physical model is abnormal, the physical model cannot receive the operation instructions sent by the intelligent detection terminal. The controller (the physical model of the pipeline valve) will judge the local monitoring data according to its built-in detection rules, and record the corresponding logs. After the network is restored, it will report the relevant logs during the network outage. However, after the early warning information reaches the preset alarm condition, the controller can automatically close the heating pipeline valve, but it cannot send an alarm message to the residents and the heating party. Instead, it will emit an alarm sound to remind the residents.

[0041] For example, please refer to Figure 2 , Figure 2An overall working overview of heating detection is provided. First, B20 mode selection is performed. The current heating mode (such as daily mode and pressure mode) is selected according to the application scenario. Then, B21 is executed to obtain local monitoring data uploaded by the local sensor, and detection data for subsequent abnormal judgment of the current heating mode is screened out, and then B22 is performed to determine whether the detection data is within the reference data range; if the detection data is within the reference data range, return to B21 to continue monitoring; if the detection data is not within the reference data range, B23 is executed to record the warning information of the time, and then B24 is executed to determine whether the warning information meets the preset alarm condition; when the record of the same warning information meets the preset alarm condition, B25 is executed to automatically close the corresponding pipeline valve, notify the user (heating party and residents) of the heating abnormality to prevent further losses, and B26 is executed to generate a maintenance work order to monitor and repair the abnormal part in time; if the record of the warning information does not meet the preset alarm condition, it means that it may be a false alarm caused by network jitter or equipment collection deviation, and it is unnecessary to issue an alarm, and return to B23 to continue recording the warning information, which will not affect the current heating.

[0042] It is understandable that further measures are taken only when the warning information meets the preset alarm conditions, as long as it is to avoid false data reports caused by local device collection deviation or network jitter.

[0043] The present embodiment provides a heating detection method, which can quickly capture abnormal data and issue an early warning by automatically detecting local data, thereby timely discovering leakage in the heating pipe; at the same time, when the early warning data exceeds the preset range, the alarm mechanism is automatically triggered to close the corresponding pipe valve to cut off the source of the leakage; this series of processes all do not require human intervention, realizes the automation of heating abnormality detection, and improves the efficiency of heating detection.

[0044] In a feasible implementation manner, the step of determining the detection data associated with the current heating mode based on the acquired local monitoring data in step S10 includes: Step S11, when the current heating mode is the daily mode, the water inlet flow value and the water return flow value of the pipeline valve are screened from the local monitoring data, and the difference between the water inlet flow value and the water return flow value is determined as the detection data associated with the current heating mode; It should be noted that the inlet flow value and the return flow value refer to the fluid flow values ​​flowing through the inlet and return port of the pipeline valve respectively, and are key parameters for evaluating the working status of the pipeline; the comparison of these two values ​​can be used to determine whether there are problems such as leakage, blockage or valve failure.

[0045] Exemplarily, in daily mode, the temperature, pressure and flow data of the water inlet and return outlet are collected by sensors, and from these data, the water inlet flow value and return flow value of each section of the heating pipe are screened out, and the flow difference between the water inlet and return outlet of each section of the heating pipe is defined as the detection data, which can be used for subsequent abnormal situation judgment.

[0046] It is understandable that by comparing the flow difference between the water inlet and the water return, the working condition of the current heating pipe can be evaluated more accurately, which helps to detect pipe faults in time and reduce the steps of manual troubleshooting.

[0047] Step S12, when the current heating mode is the pressure mode, filter the pressure value and flow rate change from the local monitoring data, and determine the pressure value and flow rate change as the detection data associated with the current heating mode.

[0048] It should be noted that the pressure testing mode refers to the process of pressure testing the heating pipes before the heating system starts running. The purpose is to ensure that the pipes, valves and other components of the heating system can withstand a certain pressure during formal heating, thereby ensuring the safety and normal operation of the heating system.

[0049] In addition, it should be noted that the pressure value refers to the value collected by the pressure sensor, which indicates the pressure size in the equipment or environment; the flow change refers to the value collected by the flow meter, which indicates the change in volume or mass of the fluid passing through the pipeline or equipment per unit time.

[0050] For example, during the pressure test, the pressure is increased to a certain value (such as the standard pressure is usually 6 pressures, i.e. 0.6MPa, and for high-rise buildings it may reach 8 pressures), and water is continuously injected into the pipe. The sensor collects the pressure value of 10MPa in real time, and the water flow rate gradually increases from 0 to 5 liters per minute. These data are recorded as test data for subsequent analysis.

[0051] In this embodiment, by setting different detection data for evaluating rationality for different application scenarios (operating modes), the accuracy of heating water leakage detection is improved, which helps to reduce false alarms and improve the heating experience.

[0052] In a feasible implementation manner, before the step of determining whether the detection data is within a reference data range preset for the current heating mode in step S10, the method further includes: Step S14, preprocessing the acquired historical data to obtain a learning data set; It should be noted that historical data refers to data collected and stored in some way (such as sensors, user input, log files, etc.) during a certain period of time in the past, including data from the water trial phase, data from previous heating seasons, etc.

[0053] In addition, it should be noted that preprocessing refers to a series of operations on raw data to make it more suitable for subsequent analysis, modeling or learning, including data cleaning (noise removal, filling missing values, etc.), data conversion (such as normalization, standardization, etc.), data screening (filtering data according to specific conditions), etc.; learning data set refers to the data set that is used for machine learning model training or testing after preprocessing.

[0054] For example, after obtaining data from previous heating seasons and the water trial phase in the same heating system, these historical data will be standardized and normalized, and the noise and outliers will be filtered out. Data with relatively large deviations will be discarded. After processing, the pressure and flow data of the heating pipes will be selected as a learning data set according to business needs to facilitate learning of a reasonable pressure and flow range.

[0055] It can be understood that through preprocessing, the quality of the learning data set is ensured, supporting more accurate analysis and prediction, thereby ensuring that the pressure and flow ranges learned and predicted based on the data set are reasonable; through the automated preprocessing process, the data processing efficiency is improved and manual intervention is reduced.

[0056] Step S15, using the machine learning model to determine the prediction result of the reference data range according to the learning data set; It should be noted that the machine learning model includes a series of algorithms or systems that learn from data and make predictions or decisions; the prediction result is the output provided by machine learning based on the input data, which represents the predicted value or prediction range of a variable (such as temperature, pressure, flow, etc.) at a certain moment or period of time in the future.

[0057] For example, to determine the pressure range in the daily mode, the LSTM (Long Short-Term Memory) model can be used to learn the pressure data in the daily mode of the same heating system in previous years, so as to predict the reference data range of this year's pressure value. The LSTM model analyzes factors such as seasonal changes, weather conditions and special events, and predicts the reference data range of future pressure changes.

[0058] Step S16, adjusting the reference data range according to the prediction result.

[0059] For example, the prediction results show that in the daily mode, the pressure and flow rate in the heating pipe will increase during the Spring Festival. According to the prediction results, the upper limit of the reference data range in the daily mode can be appropriately increased during the Spring Festival to avoid false alarms.

[0060] In this implementation, historical data is learned through machine learning to predict and adjust the reference data range, which reduces the steps of manual setting and improves the efficiency of heating detection; at the same time, compared with manual settings, data predicted based on machine learning has higher accuracy and rationality, which is conducive to reducing missed alarms and false alarms, avoiding impacts on normal heating pipes, and improving residents' heating experience.

[0061] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. On this basis, before step S10, the heating detection method further includes: Step S01: Establish a physical model of each local device, and obtain local monitoring data uploaded by each local device through each physical model.

[0062] It should be noted that the object model refers to an abstract model used in the Internet of Things to describe the properties, functions and behaviors of physical devices, which usually includes elements such as device attributes, actions and events. The object model defines how the device interacts with the external system, including data reporting, command reception and status feedback.

[0063] In addition, it should be noted that local devices refer to IoT devices that are directly connected to the local network (such as home Wi-Fi, LAN, etc.), which can collect environmental data, execute control instructions, and communicate with other devices or systems through the network, such as gate valve controllers, pressure sensors and flow meters.

[0064] Illustratively, through the physical model of the pressure sensor product, the pressure values ​​and pressurization events and depressurization events reported by the pressure sensor are obtained regularly or on demand; through the physical model of the flow meter product, the flow changes detected by the inlet and return flow meters are obtained regularly or on demand, such as the flow value in the current collection period.

[0065] For example, please refer to Figure 3 , Figure 3An interactive schematic diagram for realizing water leakage detection based on a physical model is provided. Local equipment (such as a pressure sensor and a flow meter) executes step S101 to upload local monitoring data to the physical model. After the physical model performs a simple format conversion and data simplification on the data, it executes step S102 to upload the processed local monitoring data to the Internet of Things platform. The Internet of Things platform compares the received local monitoring data with a preset reference data range. When the local monitoring data is not within the reference data range, it executes step S103 to record the current warning information. After the recorded warning information reaches the preset alarm condition, it executes step S104 to send a valve closing instruction to the physical model so that the physical model closes the corresponding pipeline valve (S105). At the same time, the Internet of Things platform also sends an alarm message to the residents (S106) and generates a maintenance work order (S107). According to the alarm message, the staff of the corresponding area is promptly assigned to conduct on-site inspection and maintenance.

[0066] In this implementation, a connection is established between the physical model and the local device to directly obtain local monitoring data, thereby reducing manual intervention, improving the efficiency of data collection and processing, and facilitating the automation of water leakage detection and improving the efficiency of heating detection.

[0067] In a feasible implementation manner, the heating detection method is applied to a heating detection system, the heating detection system includes a heating detection device and a controller, and after the step of establishing a physical model of each local device in step S01, the method further includes: Step E10, when the controller is connected to the heating detection device for the first time, the detection rules are sent to the controller through the heating detection device, so that when the communication between the controller and the heating detection device is abnormal, the controller controls the opening and closing of the heating pipe valve according to the detection rules, wherein the controller is a physical model of the heating pipe valve.

[0068] It should be noted that a controller refers to a device or system that can receive instructions, process data and make corresponding controls. It can receive external inputs (such as detection rules, operating instructions, etc.) and regulate pipeline valves accordingly.

[0069] Exemplarily, when initializing the connection to the controller, the detection rules under different modes are sent to the controller in the form of a Docker image package. Initializing the connection requires ensuring that the controller receives the corresponding image package. After receiving the image package, the controller automatically creates and runs an application container for related rule detection. In addition, script files, executable files, etc. can also be used to enable the controller to judge the rationality of the acquired local monitoring data according to the corresponding detection rules.

[0070] For example, in an intelligent heating system, the physical model controller of the pipe valve is responsible for regulating the hot water flow and pressure in the heating pipe. When the pipe valve controller is first connected, detection rules including flow threshold and pressure limit are sent to the controller, so that in the event of abnormal communication between the controller and the heating detection device, the controller automatically adjusts the valve opening and closing according to these rules to ensure that the pressure and flow in the heating pipe remain within a safe and effective parameter range.

[0071] In this embodiment, the need for manual configuration is reduced through the automated sending of detection rules, which reduces the possibility of human configuration errors. At the same time, the controller can automatically control the opening and closing of the heating pipe valves in the event of abnormal communication with the heating detection equipment, thereby improving the efficiency of heating detection.

[0072] In a feasible implementation manner, after step S30, the method further includes: Step A10, generating a maintenance work order through the heating detection equipment according to the alarm information; It should be noted that a maintenance work order refers to a task order created to repair a fault in a device or system, which contains all the key information required for maintenance, such as fault description, maintenance location, maintenance time, maintenance personnel, etc.

[0073] It is understandable that automatically generating maintenance work orders can reduce manual processing time and improve the response speed to alarm information.

[0074] Step A20, when the heating detection device and the controller communicate normally and the completed maintenance work order is received, a valve opening instruction is sent to the controller through the heating detection device; For example, based on the heating pipe section with problems in the alarm information, a maintenance work order is generated to specify the workers in the corresponding area to come to the site for inspection; after the heating party completes the inspection and repair, they will receive the completed maintenance work order, and then, under normal network conditions, they can automatically send a device instruction to the controller to open the pipe valve; after receiving the corresponding instruction, the controller automatically opens the corresponding pipe valve.

[0075] In this embodiment, the opening of the heating pipe valve is controlled through networking and automation, which reduces the time and cost of manual intervention and improves the efficiency of heating detection.

[0076] In a feasible implementation manner, after step S30, the method further includes: Step A30, in the case where the communication between the heating detection device and the controller is abnormal, the controller is used to detect whether there is a flow value reporting situation; It should be noted that flow value reporting refers to the process in which the flow meter detects the flow value in the pipeline and transmits the value to the controller through the communication network.

[0077] In the case of abnormal communication between the heating detection equipment and the controller, although the controller cannot receive the relevant operation instructions sent by the platform or terminal, it can understand the current pipeline status and equipment status by receiving local monitoring data uploaded by the local device, and thus determine whether the maintenance is completed without cloud intervention.

[0078] It is understandable that by detecting whether there is a flow value report, it can be determined whether there is a device failure problem.

[0079] Step A40, when there is a flow value report, the controller determines whether to open the heating pipe valve according to the change of the obtained pressure value.

[0080] It should be noted that the change in pressure value refers to the increase or decrease in the pressure value in the heating pipe detected by the pressure sensor within a certain period of time.

[0081] For example, if there is a water leakage problem in the heating pipe, the pressure value in the pipe will drop due to the leakage. After the inspection is completed, the pressure value monitored by the pressure sensor will increase and remain stable (the change in the pressure value), which means that the inspection is successful and the pipe valve can be opened.

[0082] For example, after the inspection is completed, the heating pipe is pressure tested to detect the effect of the pipe repair. During this process, the pressure value in the pipe monitored by the pressure sensor will increase significantly, and the temperature will be maintained after the pressure test. In this case, it can also be said that there is no water leakage in the current pipe and the valve can be opened.

[0083] In this implementation, the health of the local monitoring equipment is determined based on the reported flow value, and the valve is opened after maintenance is completed based on the reported pressure value. In the event of abnormal communication between the controller and the heating detection equipment, the current maintenance status can be determined based on the reported flow value and pressure value, thereby automatically controlling the opening and closing of the valve, ensuring normal heating use, and improving the efficiency of heating detection.

[0084] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the heating detection method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0085] The present application also provides a heating detection device, please refer to Figure 4 , the heating detection device comprises: A judgment module 10 is used to determine the detection data associated with the current heating mode based on the acquired local monitoring data, and to judge whether the detection data is within the reference data range preset for the current heating mode; The recording module 20 is used to record warning information when the detection data is not within the reference data range; The alarm module 30 is used to close the heating pipe valve and output the alarm information when the early warning information reaches the preset alarm condition.

[0086] The heating detection device provided in the embodiment of the present application adopts the heating detection method in the above embodiment, which can enhance the user's heating experience. Compared with the prior art, the beneficial effects of the heating detection device provided in the present application are the same as the beneficial effects of the heating detection method provided in the above embodiment, and the other technical features in the heating detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0087] An embodiment of the present application provides a heating detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the heating detection method in the above-mentioned embodiment one.

[0088] Reference below Figure 5 , which shows a schematic diagram of the structure of a heating detection device suitable for implementing the embodiment of the present application. The heating detection device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The heating detection device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0089] like Figure 5As shown, the heating detection device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the heating detection device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the heating detection device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a heating detection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0090] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0091] The heating detection device provided in the embodiment of the present application adopts the heating detection method in the above embodiment, which can enhance the user's heating experience. Compared with the prior art, the beneficial effects of the heating detection device provided in the present application are the same as the beneficial effects of the heating detection method provided in the above embodiment, and the other technical features in the heating detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0092] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0093] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0094] An embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the heating detection method in the above-mentioned embodiment.

[0095] The computer-readable storage medium provided in the embodiment of the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0096] The above-mentioned computer-readable storage medium may be included in the heating detection device; or it may exist independently without being assembled into the heating detection device.

[0097] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the heating detection device, the heating detection device: determines the detection data associated with the current heating mode based on the acquired local monitoring data, and judges whether the detection data is within the reference data range preset for the current heating mode; when the detection data is not within the reference data range, records early warning information; when the early warning information reaches the preset alarm condition, closes the heating pipe valve and outputs an alarm message.

[0098] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0099] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0100] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0101] The readable storage medium provided in the embodiment of the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned heating detection method, and can enhance the user's heating experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the heating detection method provided in the above-mentioned embodiment, and will not be elaborated here.

[0102] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned heating detection method when executed by a processor.

[0103] The computer program product provided in the embodiment of the present application can enhance the user's heating experience. Compared with the prior art, the beneficial effects of the computer program product provided in the present application are the same as the beneficial effects of the heating detection method provided in the above embodiment, which will not be described in detail here.

[0104] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A heating detection method, characterized in that: The heating detection method comprises: Based on the acquired local monitoring data, determining the detection data associated with the current heating mode, and judging whether the detection data is within the reference data range preset for the current heating mode; When the detection data is not within the reference data range, recording warning information; When the early warning information reaches the preset alarm condition, the heating pipe valve is closed and the alarm information is output.

2. The heating detection method according to claim 1, characterized in that: The step of determining the detection data associated with the current heating mode based on the acquired local monitoring data includes: In the case where the current heating mode is the daily mode, determining the water inlet flow value and the water return flow value of the heating pipe valve from the local monitoring data, and determining the difference between the water inlet flow value and the water return flow value as the detection data associated with the current heating mode; In the case where the current heating mode is the pressure mode, the pressure value and flow change of the heating pipe valve are determined from the local monitoring data, and the pressure value and the flow change are determined as the detection data associated with the current heating mode.

3. The heating detection method according to claim 1, characterized in that: Before the step of judging whether the detection data is within the reference data range preset in the current heating mode, the method further includes: Preprocess the acquired historical data to obtain a learning data set; Determine a prediction result of the reference data range based on the learning data set using a machine learning model; Based on the prediction result, the reference data range is adjusted.

4. The heating detection method according to claim 1, characterized in that: Before the step of determining the detection data associated with the current heating mode based on the acquired local monitoring data, the method further includes: A physical model of each local device is established, and local monitoring data uploaded by each local device is obtained through each physical model.

5. The heating detection method according to claim 4, characterized in that: The heating detection method is applied to a heating detection system, which includes a heating detection device and a controller. After the step of establishing a physical model of each local device, the method further includes: When the controller is connected to the heating detection device for the first time, the detection rules are sent to the controller through the heating detection device, so that when the communication between the controller and the heating detection device is abnormal, the controller controls the opening and closing of the heating pipe valve according to the detection rules, wherein the controller is a physical model of the heating pipe valve.

6. The heating detection method according to claim 5, characterized in that: After the step of outputting the alarm information, the method further includes: According to the alarm information, a maintenance work order is generated by the heating detection device; When the heating detection device and the controller communicate normally and a completed maintenance work order is received, a valve opening instruction is sent to the controller through the heating detection device.

7. The heating detection method according to claim 5, characterized in that: After the step of outputting the alarm information, the method further includes: In the case where the communication between the heating detection device and the controller is abnormal, detecting whether there is a flow value reporting situation through the controller; In the case where the flow value is reported, the controller determines whether to open the heating pipe valve according to the change of the acquired pressure value.

8. A heating detection device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the heating detection method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the heating detection method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the heating detection method according to any one of claims 1 to 7 are implemented.

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