Intelligent drinking water equipment remote management method, system and equipment based on Internet of Things

Abstract

The invention relates to the technical field of data processing, and discloses an intelligent drinking water equipment remote management method, system and equipment based on the Internet of Things. The method comprises the steps that various sensors are installed on each drinking water device, related data are collected through the sensors, and the sensors send the collected related data to a remote server through a wireless network; after the remote server receives the related data, the related data are stored in a database, the equipment state of the drinking equipment is obtained and analyzed based on the related data, a control instruction is generated based on the related data and preset control logic, the control instruction is sent to the drinking equipment through a wireless network, and the drinking equipment adjusts the drinking equipment based on the control instruction; dynamically adjusting the set temperature and the use mode of the drinking equipment based on the water taking frequency and the historical data of the drinking equipment; filter element replacement reminding information is generated in time, and the reminding information is sent to the management terminal. The running reliability of the drinking water equipment can be improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a remote management method, system, and device for intelligent drinking water equipment based on the Internet of Things. Background Art

[0002] With the rapid development of Internet of Things technology, intelligent drinking water equipment has been more and more widely used. Existing intelligent drinking water equipment management systems have achieved remote monitoring and management of drinking water equipment to a certain extent.

[0003] A similar prior art Chinese patent application with publication number CN111585849A provides a management method, cloud server, system, and readable storage medium for drinking water equipment, including: obtaining the status information of the drinking water equipment collected by sensors; obtaining the current time information; determining the drinking status of the drinking water equipment according to the status information and the current time information; when it is determined that the drinking water equipment is in an abnormal state, obtaining corresponding control information; and responding to the control information. However, this document does not consider the problem that control information may have problems during transmission, resulting in control anomalies.

[0004] Another similar prior art is the Chinese patent application with publication number CN113037799A, which provides a control method for drinking water equipment based on wearable devices, including: receiving behavior parameters uploaded by the wearable device; obtaining the location information uploaded by the wearable device, and determining whether the wearable device is within a preset range according to the location information; if it is determined that the wearable device is within the preset range, determining corresponding drinking water parameters according to the behavior parameters; generating corresponding control instructions according to the drinking water parameters; and sending the control instructions to the drinking water equipment to control the drinking water equipment to respond to the control instructions. However, this document also does not consider the problem that control instructions may go wrong during transmission.

[0005] Therefore, the present invention provides a remote management method, system, and device for intelligent drinking water equipment based on the Internet of Things. Summary of the Invention

[0006] To solve the above technical problems, this application provides a remote management method, system, and device for intelligent drinking water equipment based on the Internet of Things, which is used to ensure the accuracy of control instructions during transmission.

[0007] In the first aspect, this application provides a remote management method for intelligent drinking water equipment based on the Internet of Things. The method includes:

[0008] Step S1: Install multiple sensors on each drinking water device, collect relevant data through the sensors, where the relevant data includes water temperature, water level, water quality indicators, water intake time, water intake frequency, and device data. The device data includes the power switch status, the operating status of each functional component, the filter element status, and the device operating time. Each sensor sends the collected relevant data to the remote server through a wireless network;

[0009] Step S2: After receiving the relevant data, the remote server stores the relevant data in the database, obtains and analyzes the device status of the drinking water device based on the relevant data, generates a control instruction based on the relevant data and the preset control logic, and sends the control instruction to the drinking water device through the wireless network. The drinking water device adjusts itself based on the control instruction;

[0010] Step S3: Analyze the historical relevant data stored in the database, use machine learning algorithms to identify the normal mode, abnormal mode, and characteristic mode of the drinking water device. When it is detected that a certain drinking water device exhibits a characteristic mode at a specific time, adjust the corresponding monitoring threshold and update the corresponding abnormal mode. Also, dynamically adjust the set temperature and usage mode of the drinking water device based on the water intake frequency and historical data of the drinking water device;

[0011] Step S4: Train a prediction model based on the historical relevant data collected from each drinking water device, use the prediction model to predict the future change trend of the water quality indicators of the drinking water device over time, and generate a filter element replacement reminder message in a timely manner according to the predicted change trend, and send the reminder message to the management terminal.

[0012] Combined with the first aspect, in the first implementation manner of the first aspect of the present application, sending the collected relevant data to the remote server through the network includes:

[0013] Statistically quantify the relevant data of each drinking water device, obtain the data range of each data parameter in several pieces of relevant data, divide the data range into several small ranges, assign a data feature value to each small range, map each data parameter in the relevant data to the corresponding data feature value, summarize the data feature values corresponding to each data parameter, calculate the occurrence times of each data feature value, calculate the proportion of the occurrence times to the total times as the summary proportion, calculate the summary proportion of all data parameters, preset several different data representative values, assign a data representative value to each data feature value in combination with the summary proportion, associate the data feature value and the data representative value of each data parameter, and allocate a sending channel for the corresponding relevant data based on the associated data feature value and data representative value, and send the relevant data to the remote server through the allocated sending channel.

[0014] Combined with the first aspect, in the second implementation manner of the first aspect of the present application, a data representative value is assigned to each data feature value of each data parameter, including:

[0015] Presetting a first quantity of different data representative values, where the first quantity is greater than the number of data parameters included in the relevant data. Obtain the data feature values corresponding to the first data parameter, assign different data representative values to each data feature value, obtain the summary ratio of each data feature value of the first data parameter, record the data representative value corresponding to the data feature value corresponding to the largest summary ratio and mark it as the first data representative value. After assigning the corresponding data representative values to the data feature values of each data parameter, record the corresponding first data representative value. When assigning the corresponding data representative values to subsequent data parameters, avoid multiple identical data feature values using the same data representative value, and also assign data representative values different from the first data representative value to the data feature values with the largest corresponding summary ratio.

[0016] Combined with the first aspect, in the third implementation manner of the first aspect of the present application, storing the relevant data in a database includes:

[0017] After the remote server receives the relevant data, classify the relevant data, create multiple different data storage tables based on different types of relevant data, and store the relevant data of the corresponding type in the corresponding data storage table;

[0018] When storing different types of relevant data in the corresponding data storage table, also extract the key fields of different types of relevant data, create an index based on the key fields, and store the index and the corresponding relevant data in an associated manner;

[0019] Obtain the collection time of the relevant data, obtain the collection time period to which the collection time belongs, store the relevant data collected in the corresponding collection time period in a partitioned manner according to the preset collection time period, archive and store the historical relevant data at preset time intervals, and also back up the historical relevant data.

[0020] Combined with the first aspect, in the fourth implementation manner of the first aspect of the present application, generating a control instruction based on the relevant data and a preset control logic includes:

[0021] A preset control rule, the control rule including the normal range, fault threshold, and control logic of the drinking water device, matching the real-time collected relevant data with the preset control rule, generating a corresponding control instruction after matching the corresponding control rule, generating a control instruction data packet based on the control instruction, and then performing multiple verifications on the control instruction data packet. After the verification is successful, the control instruction data packet is sent to the corresponding drinking water device. In the case of verification failure, error information is recorded and the system administrator is notified.

[0022] Combined with the first aspect, in the fifth implementation manner of the first aspect of this application, performing multiple verifications on the control instruction data packet includes:

[0023] Judging whether the control instruction data packet contains all necessary fields, also checking whether the format of each field in the control instruction data packet conforms to the preset specification, also judging whether the length of the control instruction data packet is within the preset length range, verifying whether the control instruction included in the control instruction data packet conforms to the actual operation logic of the corresponding drinking water device, and also verifying whether the control instruction parameters are within the operating range.

[0024] Combined with the first aspect, in the sixth implementation manner of the first aspect of this application, sending the control instruction data packet to the corresponding drinking water device includes:

[0025] Performing binary encoding on the control instruction data packet to obtain corresponding first data, dividing the first data into multiple groups of second data according to a preset number of bits, obtaining the first quantity of the first value in multiple groups of the second data. If the first quantity is odd, adding one bit of the first value after the corresponding second data. If the first quantity is even, adding one bit of the second value after the corresponding second data. Combining the added multiple groups of second data to generate a sending data packet, sending the sending data packet to the corresponding drinking water device. After the drinking water device receives the sending data packet, verifying the correctness of the control instruction data packet. In the case of a correct judgment, generating a corresponding control instruction based on the sending data packet. In the case of a verification error, requesting to resend the sending data packet.

[0026] Combined with the first aspect, in the seventh implementation manner of the first aspect of this application, verifying the correctness of the control instruction includes:

[0027] Adding one to the preset number of bits to obtain a third value, dividing the sending data packet into multiple groups of third data according to the third value, judging whether the first quantity of the first value in all the third data is even. If so, judging that the sending data packet is correct. If not, judging that the sending data packet is incorrect.

[0028] Second aspect, the present application provides an Internet of Things-based intelligent drinking water equipment remote management system, the system comprising:

[0029] A data collection module, configured to install a variety of sensors on each drinking water equipment, collect relevant data through the sensors, the relevant data including water temperature, water level, water quality indicators, water intake time, water intake frequency, and equipment data, the equipment data including power switch status, operating status of each functional device, filter element status, and equipment operating time, and each sensor sends the collected relevant data to a remote server through a wireless network;

[0030] A first regulation module, configured to, after the remote server receives the relevant data, store the relevant data in a database, obtain and analyze the equipment status of the drinking water equipment based on the relevant data, generate a control instruction based on the relevant data and a preset control logic, and send the control instruction to the drinking water equipment through a wireless network, and the drinking water equipment adjusts the drinking water equipment based on the control instruction;

[0031] A second regulation module, configured to analyze the historical relevant data stored in the database, use a machine learning algorithm to identify the normal mode, abnormal mode, and characteristic mode of the drinking water equipment, adjust the corresponding monitoring threshold when a characteristic mode of a certain drinking water equipment is detected at a specific time, and update the corresponding abnormal mode, and also dynamically adjust the set temperature and usage mode of the drinking water equipment based on the water intake frequency and historical data of the drinking water equipment;

[0032] A water quality warning module, configured to train a prediction model based on the historical relevant data collected by each drinking water equipment, use the prediction model to predict the future change trend of the water quality indicators of the drinking water equipment over time, and generate a filter element replacement reminder message in a timely manner according to the predicted change trend, and send the reminder message to a management terminal.

[0033] The third aspect of the present application provides an Internet of Things-based intelligent drinking water equipment remote management device, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory to enable the Internet of Things-based intelligent drinking water equipment remote management device to execute the above-mentioned Internet of Things-based intelligent drinking water equipment remote management method.

[0034] Compared with the prior art, the beneficial effects of the present invention are at least as follows:

[0035] In the technical solution provided by this application, by assigning data characteristic values and transmission channels to different data parameters, conflicts during data transmission are reduced, and data transmission efficiency and reliability are improved; multiple checksums and encoding processes are performed on control instruction data packets to ensure the accuracy of data transmission and the security of device operation. At the same time, through the data backup mechanism, the security and integrity of data are guaranteed; machine learning algorithms are used to analyze historical data to identify normal modes, abnormal modes, and characteristic modes of drinking water devices, and the monitoring thresholds and device operation modes can be dynamically adjusted, improving the intelligent management level of the devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] Figure 1 FIG. is a schematic diagram of an embodiment of the remote management method for intelligent drinking water devices based on the Internet of Things in the embodiments of this application;

[0038] Figure 2 FIG. is a schematic diagram of an embodiment of the process of statistically quantifying the relevant data of each drinking water device and assigning corresponding data characteristic values in the embodiments of this application;

[0039] Figure 3 FIG. is a schematic diagram of an embodiment of the process of assigning data representative values in the embodiments of this application;

[0040] Figure 4 FIG. is a schematic diagram of an embodiment of the remote management system for intelligent drinking water devices based on the Internet of Things in the embodiments of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The embodiments of this application provide a remote management method, system, and device for intelligent drinking water devices based on the Internet of Things. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and drawings of this application are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0042] For ease of understanding, the following describes the specific process of the embodiments of the present application. Please refer to Figure 1 , an embodiment of the method for remotely managing an intelligent drinking water device based on the Internet of Things in the embodiments of the present application includes:

[0043] Step S1: Install a variety of sensors on each drinking water device, collect relevant data through the sensors. The relevant data includes water temperature, water level, water quality indicators, water intake time, water intake frequency, and device data. The device data includes the power switch state, the operating states of each functional component, the filter element state, and the device operating time. Each sensor sends the collected relevant data to the remote server through a wireless network.

[0044] Specifically, in order to manage the drinking water device reliably and efficiently, a variety of sensors are installed on each drinking water device, and relevant data such as water temperature, water level, and device data are collected through the sensors. Since the amount of collected data is large, and there may be a process where multiple sensors send relevant data to the remote server simultaneously, conflicts may occur during the sending process. In order to reduce conflicts during the data occurrence process and improve the transmission efficiency of relevant data, different sending channels are set for different data parameters to reduce data conflicts. The specific sending process will be explained in detail later.

[0045] Step S2: After the remote server receives the relevant data, store the relevant data in the database, obtain and analyze the device state of the drinking water device based on the relevant data, generate a control instruction based on the relevant data and the preset control logic, and send the control instruction to the drinking water device through a wireless network. The drinking water device adjusts the drinking water device based on the control instruction.

[0046] Specifically, after the remote server receives the relevant data, store the relevant data in the database. In order to improve the efficiency of subsequent data query and also for the accuracy of the data, when storing the relevant data, first classify the relevant data, store different types of data in different data storage tables, extract key fields while storing, create an index based on the key fields, and store the data collected at different times in partitions. The specific storage process will be explained in detail later. Analyze the device state of the drinking water device based on the stored relevant data. For example, when it is found that the water intake volume increases during a certain period, increase the corresponding set temperature so that the outlet water temperature of the drinking water device always remains at an appropriate temperature. Generate a corresponding control instruction based on the relevant data and the preset control logic. In order to ensure the accuracy of the control instruction, generate corresponding verification data while sending the control instruction to the drinking water device so that the drinking water device can determine whether the control instruction is correct. In the case of correct judgment, the drinking water device adjusts the drinking water device based on the control instruction to enhance the experience of drinking water users.

[0047] Step S3: Analyze the historical relevant data stored in the database, use machine learning algorithms to identify the normal mode, abnormal mode, and characteristic mode of the drinking water device. When a characteristic mode of a certain drinking water device is detected at a specific time, adjust the corresponding monitoring threshold and update the corresponding abnormal mode. Also, dynamically adjust the set temperature and usage mode of the drinking water device based on the water intake frequency and historical data of the drinking water device.

[0048] Specifically, analyze the historical relevant data stored in the database, extract key features from the data, such as the temperature change rate, user water intake frequency, component operation time, etc. Use machine learning algorithms to identify the normal mode, abnormal mode, and characteristic mode of the drinking water device. When a characteristic mode of a certain drinking water device is detected at a specific time, for example, if the device frequently shows abnormal temperatures in a high-temperature environment but there are no signs of malfunction in the drinking water device, then appropriately relax the temperature threshold, update the abnormal mode library according to the detected characteristic mode, optimize the fault judgment logic. Also, dynamically adjust the set temperature and usage mode of the drinking water device based on the water intake frequency and historical data of the drinking water device. For example, if the user water intake frequency is high, appropriately increase the hot water temperature or decrease the cold water temperature. During the peak user usage period, start the heater or cooler in advance. During the non-peak period, switch to the energy-saving mode and turn off unnecessary components. Improve the drinking efficiency and reliability of the drinking water device through the above methods.

[0049] Step S4: Train a prediction model based on the historically collected relevant data of each drinking water device, use the prediction model to predict the future change trend of the water quality index of the drinking water device over time, and generate a filter replacement reminder message in a timely manner according to the predicted change trend, and send the reminder message to the management terminal.

[0050] Specifically, to ensure the water quality of the drinking water device, train a prediction model based on the historically collected relevant data of each drinking water device, use the prediction model to predict the future change trend of the water quality index of the drinking water device over time, and according to the predicted change trend, when it is predicted that the water quality index does not meet the preset threshold, it means that the water quality index of the drinking water device may soon not meet the reference standard. At this time, it may be necessary to replace equipment such as filters, so generate a filter replacement reminder message in a timely manner and send the reminder message to the management terminal. After the relevant management personnel see this reminder, replace the corresponding filter in a timely manner to ensure that the water quality index of the drinking water device is always within the suitable range for drinking.

[0051] In a specific embodiment, the collected relevant data is sent to a remote server through a wireless network, which specifically includes the following steps:

[0052] Statistically quantify the relevant data of each drinking water device, obtain the data range of each data parameter in a number of relevant data, divide the data range into several small ranges, assign a data feature value to each small range, map each data parameter in the relevant data to the corresponding data feature value, summarize the data feature values corresponding to each data parameter, calculate the occurrence times of each data feature value, and also calculate the proportion of the occurrence times in the total times as the summary proportion, calculate the summary proportion of all data parameters, preset several different data representative values, assign a data representative value to each data feature value in combination with the summary proportion, associate the data feature value and the data representative value of each data parameter, and based on the associated data feature value and data representative value, allocate a sending channel for the corresponding relevant data, and send the relevant data to the remote server through the allocated sending channel.

[0053] Specifically, assume that the management scope is the drinking water devices in a factory or an office building, and the number of drinking water devices to be managed is dozens or even hundreds. A large number of sensors are installed on each drinking water device to collect relevant data, and all relevant data needs to be sent to the remote server. When the sensors of a large number of drinking water devices send relevant data to the remote server simultaneously, due to the increase in the amount of transmitted data, the probability of data packet collision will increase significantly. To reduce the amount of data transmission, data feature values are assigned to the data parameters of the relevant data. If the same data feature values are transmitted simultaneously, the corresponding data packets may be assigned to the same sending channel, then the probability of collision on the sending channel will increase, which may cause the remote server to fail to correctly obtain the corresponding data. To reduce the collision of data packets and improve the reliability and efficiency of data transmission, by statistically summarizing the relevant data, quantifying the relevant data collected by each sensor, and then assigning corresponding data representative values to the quantified data feature values, making the combination of the data representative value and the corresponding data feature value as unique as possible. When subsequently allocating the corresponding sending channel based on the combination of the data representative value and the data feature value, try to avoid using the same sending channel for data with a large amount, so as to achieve the purpose of avoiding data collision and further improving the efficiency of data transmission.

[0054] To clearly illustrate the above technical solution, taking three drinking water devices, each with five sensors as an example, as Figure 2 shown, it is the process of statistically quantifying the relevant data of each drinking water device and assigning corresponding data feature values. Figure 2As shown, there are a temperature sensor, a pH sensor, a flow sensor, a residual chlorine concentration sensor, and a turbidity sensor. These five sensors are used to collect five data parameters: temperature value, pH value, flow value, residual chlorine concentration, and turbidity. The obtained temperature values are divided into three small ranges: a low temperature range of 45 - 50 °C, a medium temperature range of 50 - 55 °C, and a high temperature range of 55 - 60 °C. The pH value, flow value, residual chlorine concentration, and turbidity are also divided into three small ranges: low, medium, and high. Corresponding data characteristic values are set for each small range. For example, low, medium, and high correspond to 1, 2, and 3 respectively. For each of the 100 relevant data collected historically by each sensor in each drinking water device, count the number of occurrences of each data characteristic value. For example, for the data parameter of temperature value, the data characteristic value 1 appears 30 times, the data characteristic value 2 appears 40 times, and the data characteristic value 3 appears 10 times. Calculate the corresponding summary ratios as 30%, 60%, and 10% respectively. Calculate the summary ratios of the data characteristic values corresponding to all data parameters. Subsequently, based on the summary ratios, assign corresponding data representative values to each data characteristic value of each data parameter, associate the data characteristic values with the data representative values, and based on the associated data characteristic values and data representative values, allocate transmission channels for the corresponding relevant data.

[0055] In a specific embodiment, in combination with the summary ratio, a data representative value is assigned to each data characteristic value, which specifically includes the following steps:

[0056] Preset a first number of different data representative values, where the first number is greater than the number of data parameters included in the relevant data. Obtain the data characteristic values corresponding to the first data parameter, assign different data representative values to each data characteristic value, obtain the summary ratio of each data characteristic value of the first data parameter, record the data representative value corresponding to the data characteristic value with the largest recorded summary ratio as the first data representative value. After assigning the corresponding data representative values to the data characteristic values of each data parameter, record the corresponding first data representative value. When assigning the corresponding data representative values to subsequent data parameters, avoid multiple identical data characteristic values using the same data representative value, and also assign data representative values different from the first data representative value to the data characteristic values with the largest corresponding summary ratio.

[0057] Specifically, in order to reduce packet collisions, by the method shown in Figure 3 assign data representative values to each data characteristic value. For example, for the data characteristic values corresponding to the three data parameters of sensor 1, since the number of preset data representative values is greater than 3, first assign data representative values to the three data characteristic values in order, as shown in Figure 3As shown, it is the process of allocating data representative values. Among them, data representative value a is allocated to data characteristic value 1, data representative value b is allocated to data characteristic value 2, and data representative value c is allocated to data characteristic value 3. Based on the previous statistical summary, the summary ratio of data characteristic value 2 is the highest. Therefore, the data representative value c of data characteristic value 2 is recorded. When allocating data representative values to subsequent data characteristic values, when allocating data representative values to the data characteristic value with the highest summary ratio, data characteristic values other than data characteristic value c are used. The data representative values allocated to the three data characteristic values of sensor 2 are d, e, and a respectively in sequence. Among them, the summary ratio of data characteristic value 2 is the highest, that is, when sending this data parameter, the probability of data characteristic value 2 appearing is the greatest. Therefore, when allocating the corresponding data representative value to data characteristic value 2, a data representative value e different from data representative value b is used. The above allocation method is used to allocate corresponding data representative values to each subsequent data characteristic value, so that data that is likely to appear will not be allocated to the same sending channel during the transmission process, such as Figure 2 The data with a pH value of 7.0 - 7.5 shown will be generated frequently. The data characteristic value and data representative value allocated to the corresponding data parameter are 2b respectively. The data with a temperature value of 50 - 55 degrees Celsius will also be generated frequently. The data characteristic value and data representative value allocated to the corresponding data parameter are 2e respectively. Since 2b and 2e are different, the corresponding allocated sending channels are probably different, which reduces the probability of conflicts occurring during the sending process of relevant data, thereby improving the data transmission efficiency.

[0058] The above method allocates unique data representative values to each data characteristic value of each data parameter, and tries to avoid multiple data parameters with a high probability of occurrence using the same data representative value, so that frequently occurring data parameters use different sending channels during data transmission, thereby reducing the probability of conflicts occurring during data transmission.

[0059] In a specific embodiment, relevant data is stored in a database, which specifically includes the following steps:

[0060] When the remote server receives relevant data for the first time, it classifies the relevant data, creates multiple different data storage tables based on different types of relevant data, and stores the corresponding type of relevant data in the corresponding data storage table;

[0061] When storing different types of relevant data in the corresponding data storage table, the keyword fields of different types of relevant data are also extracted, indexes are created based on the keyword fields, and the indexes and the corresponding relevant data are stored in an associated manner;

[0062] Obtain the collection time of relevant data, obtain the collection time period to which the collection time belongs, partition and store the relevant data collected during the corresponding collection time period according to the preset collection time period, archive and store the historical relevant data at preset time intervals, and also back up the historical relevant data.

[0063] Specifically, to lay a solid data foundation for efficient and reliable device management, when storing relevant information in a database, design a structured database to store relevant data for convenient subsequent data reading and writing. When the remote server receives relevant data for the first time, classify the relevant data. For example, classify the relevant data into device data, sensor data, device usage data, device operation data, etc., and set corresponding data storage tables for different types of relevant data for fast subsequent data reading. When storing different types of data, to improve the efficiency of subsequent data query, extract the corresponding key fields from different types of relevant data. The key fields refer to fields that can uniquely represent data, such as device ID, collection timestamp, etc. Create an index based on the key fields, and associate and store the index with the corresponding relevant data. Subsequently, query the corresponding data based on the index to improve the query efficiency. Also obtain the collection time of relevant data, and partition and store the relevant data belonging to the same collection time period. For example, store the data collected on the same day in one partition for convenient daily query and analysis. To save storage space and improve the performance of the database, also regularly, for example, every 10 days, archive and store the historically collected relevant data, such as storing it in cloud storage space or a hard disk, and at the same time back up the historical relevant data locally or in the cloud to prevent data loss.

[0064] In a specific embodiment, generating a control instruction based on relevant data and a preset control logic specifically includes the following steps:

[0065] Preset control rules. The control rules include the normal range, fault threshold, and control logic of the drinking water device. Match the real-time collected relevant data with the preset control rules. After matching the corresponding control rules, generate the corresponding control instruction, generate a control instruction data packet based on the control instruction, and then perform multiple verifications on the control instruction data packet. After the verification is successful, send the control instruction data packet to the corresponding drinking water device. In the case of verification failure, record the error information and notify the system administrator.

[0066] Specifically, in order to significantly improve the intelligent management level of drinking water equipment and also provide support for realizing efficient and reliable equipment monitoring and equipment control, preset control rules. The control rules can be dynamically updated. The control rules include the normal range of equipment operation, fault thresholds, and control logics. Real-time matching is performed between relevant data and control rules, and corresponding control instructions are generated after matching the corresponding control rules. For example, if the water temperature exceeds the preset temperature setting value, the rule to turn off the heater is triggered. If the water output of a certain drinking water equipment exceeds the preset water output threshold within a predetermined time period, the rule to increase the hot water temperature is triggered. For example, under normal conditions, the heating temperature of the heater of the drinking water equipment is set at 45 degrees, which is more suitable for drinking. If the water output of this drinking water equipment exceeds the preset water output within half an hour, the heating temperature is set at a higher temperature so that the drinking water equipment can continuously produce water at 45 degrees suitable for drinking. Based on the control instructions, a control instruction data packet is generated. To ensure the accuracy and reliability of the generated control instructions and avoid equipment failures or abnormal operations of the drinking water equipment caused by incorrect instructions, multiple verifications are performed on the generated control instruction data packet. The specific verification process will be explained in detail later. After successful verification, the control instruction data packet is sent to the corresponding drinking water equipment. In the case of verification failure, corresponding handling measures are taken based on the corresponding failure reasons. For example, if it is a format error or parameter error, an error message is returned and the control instructions are required to be regenerated. If it is a logic conflict or security issue, it is recorded.

[0067] In a specific embodiment, multiple verifications are performed on the control instruction data packet, including the following steps:

[0068] Judge whether the control instruction data packet contains all necessary fields, also check whether the format of each field in the control instruction data packet conforms to the preset specifications, also judge whether the length of the control instruction data packet is within the preset length range, verify whether the control instructions included in the control instruction data packet conform to the actual operation logic of the corresponding drinking water equipment, and also verify whether the control instruction parameters are within the operating range.

[0069] Specifically, to avoid malfunction of the drinking water equipment caused by incorrect instructions, multiple verifications are performed on the control instruction data packet. First, it is checked whether the control instruction data packet contains all necessary fields. The necessary fields refer to the fields necessary to trigger the control instruction. If these fields are missing, the corresponding control instruction cannot be triggered successfully. For example, the device ID, instruction type, and target parameters. It is also checked whether the format of each field in the control instruction data packet conforms to the preset specifications. For example, the field format of the device ID should be of string type, and the field format of the target parameter should be of Number type. Then, it is also determined whether the length of the generated control instruction data packet is within the preset length range. If the control instruction data packet is too long, there may be redundant information. If the control instruction data packet is too short, it may indicate missing information. In this case, further checks are needed to delete the redundant information or complete the missing information. Logical verification is also performed on the control instruction data packet. For example, when the drinking water equipment is in the off state, a control instruction to turn on the equipment is logical. If the control instruction is other instructions, such as turning off the equipment, it is illogical. At this time, sending these illogical control instructions is not only meaningless but also a waste of resources. Therefore, logical verification is performed on the control instruction. Safety checks are also performed on the target parameters of the control instruction. For example, the target temperature parameter in the temperature adjustment instruction should be within 0-100°C. If the target temperature parameter exceeds this temperature, it indicates that the instruction is incorrect.

[0070] Through the above multiple verifications of the control instruction, such as format verification and logical verification, the reliability and intelligent level of the control instruction are improved, ensuring that the drinking water equipment can operate efficiently and safely.

[0071] In a specific embodiment, sending the control instruction data packet to the corresponding drinking water equipment specifically includes the following steps:

[0072] The control instruction data packet is binary-encoded to obtain the corresponding first data. The first data is divided into multiple groups of second data according to the preset number of bits. The first quantity of the first value in the multiple groups of second data is obtained. If the first quantity is odd, a first value is added after the corresponding second data. If the first quantity is even, a second value is added after the corresponding second data. The multiple groups of second data after addition are combined to generate a transmission data packet. The transmission data packet is sent to the corresponding drinking water equipment. After the drinking water equipment receives the transmission data packet, it verifies the correctness of the transmission data packet. If the verification is correct, a corresponding control instruction data packet is generated based on the transmission data packet. If the verification is incorrect, a request is sent to resend the above transmission data packet.

[0073] Specifically, to ensure that the drinking water device can receive correct control instructions, before sending the control instruction data packet, the control instruction data packet is binary-encoded to obtain the corresponding first data. The first data is binary data. The first data is grouped according to a preset number of bits. Assuming the preset number of bits is four, the first data is divided into groups of four in sequence to obtain multiple groups of second data. The first quantity of the first value in the second data is obtained. The first value is 1. The first quantity of 1 in the second data is obtained. Assuming the second data is 1101, the number of 1s in the second data is 3, and 3 is an odd number. One first value is added after the second data, and the second data becomes 11011. Assuming the second data is 1001, the number of 1s in the second data is 2, and 2 is an even number. Then one second value is added after the second data. The second value refers to 0, and the second data becomes 10010. After performing the same operation on all the second data, the second data is combined to generate a sending data packet and sent to the drinking water device. After receiving the sending data packet, the drinking water device verifies the correctness of the sending data packet. The specific verification process will be explained in detail later. In the case of correct verification, the first value and the second value added to the sending data packet are deleted, and binary decoding is performed to restore the original control instruction data packet, and the corresponding control instruction is triggered based on the control instruction data packet. In the case of incorrect verification, a request is made to resend the above-mentioned sending data packet.

[0074] In a specific embodiment, verifying the correctness of the control instruction specifically includes the following steps:

[0075] Add one to the preset number of bits to obtain a third value. Divide the sending data packet into multiple groups of third data according to the third value. Determine whether the third quantity of the data first value in all the third data is an even number. If so, determine that the sending data packet is correct. If not, determine that the sending data packet is incorrect.

[0076] Specifically, when the preset number of bits is 4, add 1 to the preset number of bits to get 5. Divide the received sending data packet into groups of 5 bits each to obtain multiple groups of third data. Since before sending the data, the number of 1s in each 5-bit data of the sending data packet has been made an even number, it is determined whether the third quantity of 1s in all the third data is an even number. If they are all even numbers, it means the probability of the sending data packet being sent incorrectly is very, very small. Therefore, it is determined that the sending data packet has not erred during the sending process, and the sending data packet is determined to be correct. Otherwise, it means the sending data packet has erred during the sending process, and the sending data packet is determined to be incorrect.

[0077] The above describes the method for remotely managing an intelligent drinking water device based on the Internet of Things in the embodiments of the present application. Next, the system for remotely managing an intelligent drinking water device based on the Internet of Things in the embodiments of the present application will be described. Please refer to Figure 4, an embodiment of the remote management system for intelligent drinking water devices based on the Internet of Things in the embodiments of the present application includes:

[0078] A data collection module, configured to install multiple sensors on each drinking water device, collect relevant data through the sensors, where the relevant data includes water temperature, water level, water quality indicators, water intake time, water intake frequency, and device data, and the device data includes the power switch state, the operating states of each functional component, the filter element state, and the device operating time, and each sensor sends the collected relevant data to the remote server through a wireless network.

[0079] A first regulation module, configured to, after the remote server receives the relevant data, store the relevant data in a database, obtain and analyze the device state of the drinking water device based on the relevant data, generate a control instruction based on the relevant data and a preset control logic, and send the control instruction to the drinking water device through a wireless network, and the drinking water device adjusts itself based on the control instruction.

[0080] A second regulation module, configured to analyze the historical relevant data stored in the database, use machine learning algorithms to identify the normal mode, abnormal mode, and characteristic mode of the drinking water device, adjust the corresponding monitoring threshold and update the corresponding abnormal mode when detecting that a certain drinking water device appears in the characteristic mode at a specific time, and also dynamically adjust the set temperature and usage mode of the drinking water device based on the water intake frequency and historical data of the drinking water device.

[0081] A water quality warning module, configured to train a prediction model based on the historical relevant data collected from each drinking water device, use the prediction model to predict the future change trend of the water quality indicators of the drinking water device over time, and generate a filter element replacement reminder message in a timely manner according to the predicted change trend, and send the reminder message to the management terminal.

[0082] The present application also provides a remote management device for intelligent drinking water devices based on the Internet of Things. The remote management device for intelligent drinking water devices based on the Internet of Things includes a memory and a processor. When the computer-readable instructions stored in the memory are executed by the processor, the processor executes the steps of the remote management method for intelligent drinking water devices based on the Internet of Things in the above embodiments.

[0083] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system, and unit can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0084] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0085] As described above, the above embodiments are only used to illustrate the technical solution of this application and are not intended to limit it; although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of this application.

Claims

1. A remote management method for an intelligent drinking water device based on the Internet of Things, characterized in that, The method includes: Step S1: Install multiple sensors on each drinking water device, collect relevant data through the sensors. The relevant data includes water temperature, water level, water quality index, water intake time, water intake frequency, and device data. The device data includes power switch status, operating status of each functional device, filter element status, and device operating time. Each sensor sends the collected relevant data to the remote server through a wireless network; Step S2: After the remote server receives the relevant data, store the relevant data in the database, obtain and analyze the device status of the drinking water device based on the relevant data, generate a control instruction based on the relevant data and a preset control logic, and send the control instruction to the drinking water device through the wireless network. The drinking water device adjusts itself based on the control instruction; Step S3: Analyze the historical relevant data stored in the database, use machine learning algorithms to identify the normal mode, abnormal mode, and characteristic mode of the drinking water device. When it is detected that a certain drinking water device shows a characteristic mode at a specific time, adjust the corresponding monitoring threshold and update the corresponding abnormal mode. Also, dynamically adjust the set temperature and usage mode of the drinking water device based on the water intake frequency and historical data of the drinking water device; Step S4: Train a prediction model based on the historical relevant data collected from each drinking water device, use the prediction model to predict the future change trend of the water quality index of the drinking water device over time, generate a filter element replacement reminder message in a timely manner according to the predicted change trend, and send the reminder message to the management terminal.

2. The method according to claim 1, wherein Sending the collected relevant data to the remote server through the network includes: Statistically quantify the relevant data of each drinking water device, obtain the data range of each data parameter in several pieces of relevant data, divide the data range into several small ranges, assign a data feature value to each small range, map each data parameter in the relevant data to the corresponding data feature value, summarize the data feature values corresponding to each data parameter, calculate the occurrence times of each data feature value, calculate the proportion of the occurrence times in the total times as the summary proportion, calculate the summary proportion of all data parameters, preset several different data representative values, assign a data representative value to each data feature value in combination with the summary proportion, associate the data feature value and the data representative value of each data parameter, and based on the associated data feature value and data representative value, allocate a sending channel for the corresponding relevant data, and send the relevant data to the remote server through the allocated sending channel.

3. The method according to claim 2, wherein Assigning a data representative value to each data feature value of each data parameter includes: Preset a first quantity of different data representative values, where the first quantity is greater than the number of data parameters included in the relevant data. Obtain the data characteristic value corresponding to the first data parameter, assign different data representative values to each data characteristic value, obtain the summary ratio of each data characteristic value of the first data parameter, record the data representative value corresponding to the maximum summary ratio among them as the first data representative value. After assigning the corresponding data representative value to the data characteristic value of each data parameter, record the corresponding first data representative value. When assigning the corresponding data representative value to the subsequent data parameters, avoid multiple identical data characteristic values using the same data representative value, and also assign a data representative value different from the first data representative value to the data characteristic value with the maximum corresponding summary ratio.

4. The method according to claim 1, characterized in that Store the relevant data in a database, including: After the remote server receives the relevant data, classify the relevant data, create multiple different data storage tables based on different types of relevant data, and store the corresponding type of relevant data in the corresponding data storage table; When storing different types of relevant data in the corresponding data storage table, also extract the key fields of different types of relevant data, create an index based on the key fields, and associate and store the index with the corresponding relevant data; Obtain the collection time of the relevant data, obtain the collection time period to which the collection time belongs, partition and store the relevant data collected in the corresponding collection time period according to the preset collection time period. At regular preset time intervals, archive and store the historical relevant data, and at the same time back up the historical relevant data.

5. The method according to claim 1, characterized in that Generate control instructions based on the relevant data and a preset control logic, including: Preset control rules, where the control rules include the normal range, fault threshold, and control logic of the drinking water equipment. Match the real-time collected relevant data with the preset control rules. After matching the corresponding control rules, generate the corresponding control instructions, generate a control instruction data packet based on the control instructions, and then perform multiple validations on the control instruction data packet. After the validation is successful, send the control instruction data packet to the corresponding drinking water equipment. In the case of failed validation, record the error information and notify the system administrator.

6. The method according to claim 5, characterized in that, Perform multiple validations on the control instruction data packet, including: Judge whether the control instruction data packet contains all necessary fields, also check whether the format of each field in the control instruction data packet conforms to the preset specification, also judge whether the length of the control instruction data packet is within the preset length range, verify whether the control instructions included in the control instruction data packet conform to the actual operation logic of the corresponding drinking water equipment, and also verify whether the control instruction parameters are within the operating range.

7. The method according to claim 5, wherein Send the control instruction data packet to the corresponding drinking water equipment, including: Perform binary encoding on the control instruction data packet to obtain corresponding first data. Divide the first data into multiple groups of second data according to a preset number of bits. Obtain the first quantity of the first value in multiple groups of the second data. If the first quantity is odd, add one bit of the first value after the corresponding second data. If the first quantity is even, add one bit of the second value after the corresponding second data. Combine the multiple groups of the added second data to generate a transmission data packet. Send the transmission data packet to the corresponding drinking water device. After the drinking water device receives the transmission data packet, verify the correctness of the control instruction data packet. In the case of a correct judgment, generate a corresponding control instruction based on the transmission data packet. In the case of an incorrect verification, request to resend the transmission data packet.

8. The method according to claim 7, wherein Verifying the correctness of the control instruction includes: Increment the preset number of bits by one to obtain a third value. Divide the transmission data packet into multiple groups of third data according to the third value. Determine whether the first quantity of the first value in all the third data is even. If so, determine that the transmission data packet is correct. If not, determine that the transmission data packet is incorrect.

9. An Internet of Things-based intelligent drinking water equipment remote management system for implementing the Internet of Things-based intelligent drinking water equipment remote management method according to any one of claims 1-8, characterized in that, The system includes: A data collection module for installing various sensors on each drinking water device and collecting relevant data through the sensors. The relevant data includes water temperature, water level, water quality indicators, water intake time, water intake frequency, and device data. The device data includes the power switch state, the operating states of each functional component, the filter element state, and the device operating time. Each sensor sends the collected relevant data to a remote server through a wireless network; A first regulation module for, after the remote server receives the relevant data, storing the relevant data in a database, obtaining and analyzing the device state of the drinking water device based on the relevant data, generating a control instruction based on the relevant data and a preset control logic, and sending the control instruction to the drinking water device through a wireless network. The drinking water device adjusts itself based on the control instruction; A second regulation module for analyzing the historical relevant data stored in the database, using machine learning algorithms to identify the normal mode, abnormal mode, and characteristic mode of the drinking water device. When detecting that a certain drinking water device exhibits a characteristic mode at a specific time, adjusting the corresponding monitoring threshold and updating the corresponding abnormal mode. Also, dynamically adjusting the set temperature and usage mode of the drinking water device based on the water intake frequency and historical data of the drinking water device; A water quality warning module for training a prediction model based on the historical relevant data collected from each drinking water device, using the prediction model to predict the future change trend of the water quality indicators of the drinking water device over time, and generating a filter element replacement reminder message in a timely manner according to the predicted change trend, and sending the reminder message to the management terminal.

10. The remote management device for the intelligent drinking water equipment based on the Internet of Things is characterized in that The Internet of Things-based intelligent drinking water device remote management device includes: A memory and at least one processor, with instructions stored in the memory; The at least one processor invokes the instructions in the memory to cause the Internet of Things-based intelligent drinking water device remote management device to execute the Internet of Things-based intelligent drinking water device remote management method according to any one of claims 1-8.

Images