Error checking method and system for intelligent water meter

Abstract

The invention relates to the technical field of error verification, and particularly discloses an error verification method and system for an intelligent water meter, which adopts a neural network model based on deep learning to carry out fine-grained time sequence feature extraction on time sequence data of temperature and pressure of a water body flowing through a multifunctional intelligent water meter in a target time period; according to the method, the time sequence change mode of the water body temperature and the pressure in each local time domain is captured, time sequence integral coding is carried out on the time sequence change characteristics of the water body temperature and the pressure in each local time domain to understand the time sequence accumulation effect of the water body temperature and the pressure on the water consumption in the global time domain, and intelligent prediction of the water consumption is achieved. And error verification is carried out on the intelligent water meter according to the difference between the prediction result and the measured value of the water meter. In this way, the measuring error of the intelligent water meter can be reflected more accurately, and then the error checking precision and the intelligent level of water affair management are improved.

Description

Technical Field

[0001] The present application relates to the technical field of error correction, and more specifically, to an error correction method and system for a smart water meter. Background Art

[0002] With the continuous advancement of science and technology, the application of smart water meters in modern water management is becoming more and more extensive. Smart water meters comprehensively use modern microelectronics technology, sensor technology, smart IC card and other technologies, which can not only accurately measure water consumption, but also have the functions of water data transmission, settlement transactions, record storage and automatic execution of tiered water price calculation, which greatly improves the intelligent level of water management and effectively avoids many disadvantages of traditional manual meter reading.

[0003] In the process of using smart water meters, it is very important to ensure their measurement accuracy. Error checking, as a key means to ensure the measurement accuracy of smart water meters, has received widespread attention. In the prior art, for example, the invention patent application with publication number CN118776645A proposes an error checking method for a multifunctional smart water meter, which uses a pre-established multivariate linear regression model to predict the second water consumption in the target time period based on the date information of the target time period, the temperature and pressure of the water body flowing through the multifunctional smart water meter, and combines the first water consumption in the target time period collected by the multifunctional smart water meter to realize error checking of the multifunctional smart water meter, thereby ensuring the accurate measurement of water consumption by the multifunctional smart water meter.

[0004] However, the multiple linear regression model has some obvious flaws in predicting water consumption. First, the model assumes that there is a linear relationship between variables, but in reality, the relationship between water temperature, pressure and water consumption is often more complicated and may exist in a nonlinear relationship. For example, under extreme weather conditions, such as extreme heat or cold, changes in water temperature and pressure may have a significant nonlinear effect on water consumption, and this association pattern is difficult to accurately describe with a simple linear relationship. In addition, the multiple linear regression model is highly dependent on data. If the historical data is noisy or incomplete, it will significantly affect the prediction accuracy of the model.

[0005] Therefore, an optimized error checking method and system for smart water meters are desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides an error checking method and system for a smart water meter, which uses a neural network model based on deep learning to perform fine-grained time series feature extraction on the time series data of the temperature and pressure of the water body flowing through the multifunctional smart water meter during the target time period, so as to capture the time series change pattern of the water body temperature and pressure in each local time domain, and to understand the time series cumulative effect of the water body temperature and pressure on the water consumption in the global time domain by performing time series integral encoding on the time series change characteristics of the water body temperature and pressure in each local time domain, so as to realize the intelligent prediction of the water consumption, and then perform error checking on the smart water meter according to the difference between the prediction result and the water meter measurement value. In this way, the measurement error of the smart water meter can be reflected more accurately, thereby improving the accuracy of error checking and the intelligent level of water management.

[0007] According to one aspect of the present application, there is provided an error checking method for a smart water meter, comprising: Acquire monitoring parameters of the multifunctional smart water meter within a target time period, wherein the monitoring parameters include: date information of the target time period, actual water consumption measurement value, and a time series of temperature and pressure of a water body flowing through the multifunctional smart water meter; Based on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter, determining the initial water consumption, wherein determining the initial water consumption comprises: performing temporal fine-grained integral encoding and decoding prediction on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption; Based on the historical monthly water consumption data of the month information corresponding to the target time period, the initial water consumption is corrected to obtain a second water consumption; The actual water consumption measurement value of the target time period is used as the first water consumption, and based on the difference between the first water consumption and the second water consumption, the multifunctional smart water meter is error-checked.

[0008] According to another aspect of the present application, there is provided an error checking system for a smart water meter, comprising: A monitoring parameter acquisition module is used to acquire monitoring parameters of the multifunctional smart water meter within a target time period, wherein the monitoring parameters include: date information of the target time period, actual water consumption measurement value, temperature of the water body flowing through the multifunctional smart water meter, and time series of pressure; An initial water consumption determination module is used to determine the initial water consumption based on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter, wherein determining the initial water consumption includes: performing time series fine-grained integral encoding and decoding prediction on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption; An initial water consumption correction module, configured to correct the initial water consumption based on the historical monthly water consumption data of the month information corresponding to the target time period to obtain a second water consumption; The error checking module is used to use the actual water consumption measurement value in the target time period as the first water consumption, and perform error checking on the multifunctional intelligent water meter based on the difference between the first water consumption and the second water consumption.

[0009] Compared with the prior art, the error checking method and system of the smart water meter provided by the present application uses a neural network model based on deep learning to extract fine-grained time series features from the time series data of the temperature and pressure of the water body flowing through the multifunctional smart water meter during the target time period, so as to capture the time series variation pattern of the water body temperature and pressure in each local time domain, and to understand the time series cumulative effect of the water body temperature and pressure on the water consumption in the global time domain by respectively performing time series integral encoding on the time series variation characteristics of the water body temperature and pressure in each local time domain, so as to realize the intelligent prediction of the water consumption, and thus perform error checking on the smart water meter according to the difference between the prediction result and the water meter measurement value. In this way, the measurement error of the smart water meter can be reflected more accurately, thereby improving the accuracy of error checking and the intelligent level of water management. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 The figure is a flow chart of an error checking method for a smart water meter according to an embodiment of the present application.

[0012] Figure 2 This is a flowchart of sub-step S2 of the error checking method for a smart water meter according to an embodiment of the present application.

[0013] Figure 3 Schematic diagram of data flow of sub-step S2 of the error checking method for a smart water meter according to an embodiment of the present application.

[0014] Figure 4 This is a flowchart of sub-step S23 of the error checking method for a smart water meter according to an embodiment of the present application.

[0015] Figure 5 This is a flowchart of sub-step S232 of the error checking method for a smart water meter according to an embodiment of the present application.

[0016] Figure 6 This is a flowchart of sub-step S3 of the error checking method for a smart water meter according to an embodiment of the present application.

[0017] Figure 7 4 is a block diagram of an error checking system for a smart water meter according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0019] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0020] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0021] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0022] It is worth noting that in this application, all actions to obtain data are carried out in compliance with the relevant data protection laws and policies of the country where the data is located, and with the authorization given by the owner of the corresponding device.

[0023] As mentioned in the background technology above, invention patent application CN118776645A proposes an error verification method for a multifunctional intelligent water meter, which uses a pre-established multivariate linear regression model to predict the second water consumption within the target time period based on the date information of the target time period, the temperature and pressure of the water flowing through the multifunctional intelligent water meter, and combines the first water consumption within the target time period collected by the multifunctional intelligent water meter to realize error verification of the multifunctional intelligent water meter, thereby ensuring the accurate measurement of water consumption by the multifunctional intelligent water meter.

[0024] In the prior art, the multiple linear regression model has some obvious defects in predicting water consumption. First, the model assumes that there is a linear relationship between variables, but in reality, the relationship between water temperature, pressure and water consumption is often more complicated, and there may be a nonlinear relationship. For example, under extreme weather conditions, such as extreme heat or cold, changes in water temperature and pressure may have a significant nonlinear effect on water consumption, and this association pattern is difficult to accurately describe with a simple linear relationship. In addition, the multiple linear regression model is highly dependent on data. If the historical data is noisy or incomplete, it will significantly affect the prediction accuracy of the model. In response to the above technical problems, this application proposes an optimized error calibration method for smart water meters, which uses a neural network model based on deep learning to extract fine-grained time series features from the time series data of the temperature and pressure of the water body flowing through the multifunctional smart water meter during the target time period, so as to capture the time series variation pattern of the water body temperature and pressure in each local time domain, and to understand the time series cumulative effect of the water body temperature and pressure on the water consumption in the global time domain by respectively performing time series integral encoding on the time series variation characteristics of the water body temperature and pressure in each local time domain, so as to realize the intelligent prediction of the water consumption, and then perform error calibration on the smart water meter according to the difference between the prediction result and the water meter measurement value. In this way, the measurement error of the smart water meter can be reflected more accurately, thereby improving the accuracy of error calibration and the intelligent level of water management.

[0025] Figure 1 FIG. 1 is a flow chart of an error checking method for a smart water meter according to an embodiment of the present application. Figure 1 As shown, the error checking method of the smart water meter includes the following steps: S1, obtaining monitoring parameters of the multifunctional smart water meter within a target time period, wherein the monitoring parameters include: date information of the target time period, actual water consumption measurement value, and time series of temperature and pressure of the water body flowing through the multifunctional smart water meter; S2, determining the initial water consumption based on the time series of temperature and pressure of the water body flowing through the multifunctional smart water meter, wherein determining the initial water consumption includes: performing time series fine-grained integral encoding and decoding prediction on the time series of temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption; S3, based on the historical monthly water consumption data of the month information corresponding to the target time period, correcting the initial water consumption to obtain the second water consumption; S4, taking the actual water consumption measurement value of the target time period as the first water consumption, and performing error checking on the multifunctional smart water meter based on the difference between the first water consumption and the second water consumption.

[0026] In the error checking method of the above-mentioned smart water meter, the step S1 obtains the monitoring parameters of the multifunctional smart water meter within the target time period, and the monitoring parameters include: the date information of the target time period, the actual water consumption measurement value, the temperature of the water body flowing through the multifunctional smart water meter, and the time series of pressure. It should be understood that the date information can reflect the differences in users' water use habits in different time periods, such as different water use patterns of users in different seasons. The actual water consumption measurement value is the direct result of the current water meter measurement and is the core basis for judging the error. The changes in water temperature and pressure can reflect the potential impact of the external environment on water consumption. For example, when the water temperature is low, such as in winter, the user's bathing frequency decreases, and the water consumption may decrease; when the temperature is high in summer, the user's water demand for bathing and washing clothes increases, resulting in an increase in water consumption. In terms of pressure, too low water pressure will cause the water flow to become smaller, thereby affecting the user's water consumption; when the water pressure is normal or high, the water will flow more smoothly and the water consumption may increase. Based on this, this application comprehensively considers multiple factors such as date information, water temperature and pressure of the target time period to more comprehensively understand and predict the user's actual water consumption, and then performs error correction on the actual water consumption measurement values ​​collected by the multi-functional smart water meter.

[0027] Specifically, regarding the acquisition of date information, it is necessary to accurately record the specific date of the target time period. This step seems simple, but it is crucial because date information directly affects subsequent data analysis and model prediction. Specifically, a high-precision clock module is integrated inside the smart water meter, which can record the current time in real time and associate these timestamps with the corresponding water consumption data. In this way, water consumption can be accurately tracked every day or even every hour, providing a solid foundation for subsequent analysis. At the same time, considering the impact that different seasons and months may have on water consumption, such as special situations such as increased water demand due to high temperatures in summer or freezing of water pipes due to low temperatures in winter, accurate date information helps to better understand the changing laws of water use patterns.

[0028] In order to obtain the actual water consumption measurement value, in addition to relying on the built-in high-sensitivity sensor, attention should also be paid to the long-term stability and calibration mechanism of the sensor. Over time, the performance of the sensor may drift, which will affect the accuracy of the measurement results. To this end, a self-calibration function can be introduced in the design stage to regularly self-check and calibrate the sensor. At the same time, in order to cope with emergencies such as abnormal readings caused by pipe blockage or damage, the smart water meter should be equipped with a fault detection and alarm system. Once an abnormal flow pattern is detected, the system can immediately issue an alarm and try to maintain basic functional operations through backup channels or algorithm compensation. In addition, combined with big data analysis technology, normal water use patterns can be learned from historical data to help identify potential problem points and warn of possible failures in advance, thereby improving system reliability and user satisfaction.

[0029] As for obtaining the time series of water temperature and pressure flowing through the multifunctional smart water meter, this process requires high accuracy and real-time response. To achieve this goal, the selected sensor must not only have high resolution and fast response speed, but also be able to withstand harsh working environment conditions, including extreme temperature, humidity and corrosive media. In order to solve these problems, corrosion-resistant materials are usually used to manufacture the sensor housing, and a waterproof sealing design is adopted to protect the internal electronic components. In addition, during the signal transmission process, in order to avoid the influence of external electromagnetic interference on the measurement results, shielded cables or anti-interference coding schemes in wireless transmission technology can be used. At the same time, considering the energy consumption problem that may be caused by long-term continuous work, it is particularly important to optimize the power management strategy. For example, the operating frequency of the sensor can be dynamically adjusted or energy harvesting technology (such as solar energy) can be used to extend battery life and reduce maintenance costs.

[0030] Finally, regarding data security and privacy protection, in addition to the encrypted storage and secure transmission measures mentioned above, a comprehensive data access control mechanism must be established. This means that only authorized personnel can access data at a certain level, and all access behaviors should be recorded for auditing.

[0031] In the error checking method of the above-mentioned smart water meter, the step S2 determines the initial water consumption based on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter, wherein determining the initial water consumption includes: performing time-series fine-grained integral encoding and decoding prediction on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption. It should be understood that the user's water use pattern may change with the change of water body temperature and pressure, thereby affecting the total water consumption generated within the target time period. Therefore, the present application further captures the time-series change pattern of water body temperature and pressure by performing time-series fine-grained integral encoding on the time series of water body temperature and pressure, thereby achieving decoding prediction of water consumption on this basis. Among them, Figure 2 This is a flowchart of sub-step S2 of the error checking method for a smart water meter according to an embodiment of the present application. Figure 3 FIG. 2 is a data flow diagram of sub-step S2 of the error checking method for a smart water meter according to an embodiment of the present application. Figure 2 and Figure 3As shown, the step S2 includes the steps of: S21, sorting the time series of the temperature and pressure of the water body flowing through the multifunctional intelligent water meter to obtain the time series of the water body temperature and the time series of the water body pressure; S22, extracting local time series features of the time series of the water body temperature and the time series of the water body pressure to obtain the time series of the local time series implicit correlation feature vector of the water body temperature and the time series of the local time series implicit correlation feature vector of the water body pressure; S23, inputting the time series of the local time series implicit correlation feature vector of the water body temperature and the time series of the local time series implicit correlation feature vector of the water body pressure into the time series integrator respectively to obtain the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector; S24, predicting the water consumption based on the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector to obtain the initial water consumption.

[0032] Specifically, the step S21 organizes the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the time series of the water body temperature and the time series of the water body pressure. Specifically, the present application takes into account that the water body temperature and the water body pressure have different physical properties and ranges of variation, and the influence patterns of the two on water consumption are also different. Therefore, in order to improve the pertinence of data analysis, the present application further groups and arranges the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter according to the type and time sequence of the data to form the time series of the water body temperature and the time series of the water body pressure, so as to facilitate independent time series encoding of the two, thereby improving the accuracy of data analysis.

[0033] Specifically, in step S22, local time series feature extraction is performed on the time series of the water body temperature and the time series of the water body pressure to obtain a time series of implicitly associated feature vectors of the local time series of the water body temperature and a time series of implicitly associated feature vectors of the local time series of the water body pressure. That is, in order to more carefully capture the short-term time series fluctuations of the water body temperature and pressure, and thus more accurately understand the immediate impact of these time series fluctuations on water consumption, the present application further uses a deep learning algorithm to extract local time series features from the time series of the water body temperature and the time series of the water body pressure. Specifically, the present application uses a preset window to perform sliding segmentation processing on the time series of the water body temperature and the time series of the water body pressure, divides the original global time domain data into multiple local time domain data segments, and then uses a one-dimensional convolutional neural network model to extract time series features of the data in each local time domain, and uses the powerful feature learning ability of the one-dimensional convolutional neural network to capture the time series fluctuation patterns of water body temperature and water body pressure in each local time window, thereby obtaining the time series of the local time series implicit correlation feature vector of the water body temperature and the time series of the local time series implicit correlation feature vector of the water body pressure.

[0034] Specifically, in step S23, the time series of the local time series implicitly associated characteristic vector of the water body temperature and the time series of the local time series implicitly associated characteristic vector of the water body pressure are respectively input into the time series integrator to obtain the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector. It should be understood that since the influence of water body temperature and water body pressure on water consumption is a cumulative process, in order to capture the cumulative effect of water body temperature and water body pressure on water consumption within the target time period, the present application further uses a time series integrator to perform feature aggregation processing on the time series of the local time series implicitly associated characteristic vector of the water body temperature and the time series implicitly associated characteristic vector of the water body pressure, respectively, and accumulates the water body temperature time series features and water body pressure time series features in each local time domain in the global time domain to obtain the water body temperature time series integral implicit coding vector and the water body pressure time series implicitly integrated coding vector, thereby revealing the cumulative influence of water body temperature and water body pressure on water consumption on a long time scale, and providing a more comprehensive input for subsequent intelligent prediction. Among them, Figure 4 FIG. 2 is a flowchart of sub-step S23 of the error checking method of the smart water meter according to an embodiment of the present application. Figure 4As shown, the step S23 includes the steps of: S231, performing information kernel coarse-grained aggregation on the time series of the local time series implicitly associated feature vectors of the water body temperature to obtain a coarse-grained aggregation coding vector of the local time series features of the water body temperature; and S232, performing dynamic compensation encoding on the coarse-grained aggregation coding vector of the local time series features of the water body temperature based on the feature differences of each local time series implicitly associated feature vector of the water body temperature in the time series of the local time series implicitly associated feature vector of the water body temperature relative to the coarse-grained aggregation coding vector of the local time series features of the water body temperature to obtain the water body temperature time series integral implicit coding vector.

[0035] More specifically, the step S231 is expressed by the formula:

[0036] in, represents the time series of the implicitly associated eigenvectors of the local time series of the water body temperature, , , and Respectively represent the first, second, and and vectors, is the number of vectors in the time series of the implicitly associated characteristic vectors of the local time series of the water body temperature, and Respectively represent the maximum and minimum values, express The median of the characteristic distribution boundary, represents the normalized exponential function, express The attention weight, Represents the coarse-grained aggregation encoding vector of the local time series characteristics of water body temperature.

[0037] That is, this application introduces the concept of information kernel, and compressively models the global characteristics of the time series of implicitly associated feature vectors of the local time series of water body temperature by means of coarse-grained aggregation of information kernels, and generates a coarse-grained aggregation encoding vector of the local time series characteristics of water body temperature. It should be understood that the introduction of information kernels provides an effective nonlinear mapping mechanism for the feature space, so that the node relationship in the sequence can be modeled in an implicit high-dimensional space, thereby accurately capturing the complex similarity structure between nodes. The information kernel coarse-grained aggregation network summarizes the overall characteristics of the nodes in the sequence by extracting the main patterns between nodes.

[0038] Figure 5 FIG. 2 is a flowchart of sub-step S232 of the error checking method of the smart water meter according to an embodiment of the present application. Figure 5As shown, the step S232 includes the steps of: S2321, calculating the kernel convergence compensation factor of each local time series implicitly associated feature vector of the water body temperature in the time series of the local time series implicitly associated feature vector of the water body temperature relative to the coarse-grained convergence coding vector of the local time series feature of the water body temperature to obtain the time series of the kernel convergence compensation factor of the local time series feature of the water body temperature; S2322, performing compensation explicit modeling based on the gating function on the time series of the kernel convergence compensation factor of the local time series feature of the water body temperature to obtain the time series of the kernel convergence compensation weight factor of the local time series feature of the water body temperature sequence; S2323, input the time series of the water body temperature local time series characteristic kernel convergence compensation weight factor, the water body temperature local time series characteristic coarse-grained convergence coding vector and the water body temperature local time series implicit correlation characteristic vector into the fine-grained dynamic compensation convergence network to obtain the water body temperature local time series compensation characteristic fine-grained convergence coding vector; S2324, input the water body temperature local time series compensation characteristic fine-grained convergence coding vector and the water body temperature local time series characteristic coarse-grained convergence coding vector into the residual unit to obtain the water body temperature time series integral implicit coding vector.

[0039] In a specific example of the present application, the step S2321 includes: performing point convolution encoding based on the Sigmoid activation function on the local temporal implicit association feature vector of the water body temperature and the coarse-grained aggregation coding vector of the local temporal series feature of the water body temperature to obtain a standardized local temporal implicit association feature vector of the water body temperature and a standardized local temporal series feature coarse-grained aggregation coding vector; calculating the position difference vector between the standardized local temporal implicit association feature vector of the water body temperature and the standardized local temporal series feature coarse-grained aggregation coding vector of the water body temperature, and taking the absolute value of the position difference vector to obtain a local temporal series feature kernel convergence difference compensation coding vector of the water body temperature; inputting the local temporal series feature kernel convergence difference compensation coding vector of the water body temperature into a compensation feature importance scoring module based on a neural network layer to obtain the local temporal series feature kernel convergence compensation factor of the water body temperature.

[0040]

[0041] in, represents the compensation factor calculation network, express Activation function, represents a 1×1 convolution operation, and They represent the weight parameter matrix of implicit association characteristics of local time series of water body temperature and the weight parameter matrix of local time series characteristics of water body temperature, respectively. represents the implicit correlation eigenvector of the local time series of standardized water body temperature, represents the coarse-grained aggregation encoding vector of the local time series characteristics of the standardized water body temperature, It means to subtract by position point. Represents the local time series feature kernel convergence difference compensation encoding vector of water body temperature, represents the weight parameter matrix of the local time series kernel convergence difference feature of water body temperature, represents the bias term, represents the water temperature difference compensation feature importance score conversion vector, The first in the set of kernel convergence compensation factors representing the local time series characteristics of water body temperature The kernel convergence compensation factor of the local time series characteristics of water body temperature.

[0042] That is, since the compressive aggregation process of global features may dilute or even completely lose the personalized information of certain important nodes, the present application further introduces the calculation of the kernel convergence compensation factor. By measuring the deviation between the implicit correlation feature vectors of the local time series of each water body temperature and the coarse-grained convergence encoding vector of the local time series features of the water body temperature, the kernel convergence compensation factor of the local time series features of the water body temperature is dynamically generated, so that the personalized information of the node can be retained, and strong supplementary support is provided for the subsequent fine-grained compensation modeling.

[0043] In particular, in a preferred example of the present application, the water body temperature local time series feature core convergence difference compensation coding vector is input into the compensation feature importance scoring module based on the neural network layer to obtain the water body temperature local time series feature core convergence compensation factor, including: calculating the ratio between the Euclidean norm of the water body temperature local time series implicit association feature vector and the Euclidean norm of the water body temperature local time series feature coarse-grained convergence coding vector; if the ratio is less than 1, then the ratio is added by one and the logarithmic value with base 2 is calculated as the bias item of the neural network layer of the compensation feature importance scoring module; if the ratio is greater than or equal to 1, then the ratio is used as the bias item of the neural network layer of the compensation feature importance scoring module, which is expressed by the formula:

[0044] in, represents the calculation of the Euclidean norm of a vector, Represents the base 2 logarithmic function.

[0045] Here, for the deviation compensation between the implicit correlation feature vector of the local time series of water body temperature and the coarse-grained convergence encoding vector of the local time series characteristics of water body temperature, the performance deviation of the core convergence strategy as a scenario strategy can be measured by quantifying the regret metric based on the information core compression hypothesis in the core convergence decision process, that is, the game counterfactual regret value. Specifically, the counterfactual regret value is provided with a normalized description of the decision point loss based on the policy action, that is, the vector norm representation of the implicit correlation feature vector of the local time series of water body temperature and the coarse-grained convergence encoding vector of the local time series characteristics of water body temperature through the norm representation of the vector. Then, for the possible differences in the game scenarios of vector distribution actions, the compensation rules of the personalized information of the local time series nodes of water body temperature are corrected by the information distribution degree of the regret value and the relative distribution amplitude of the regret value, so that the personalized information of the local time series of water body temperature is considered as the untaken action in the decision, and the biased compensation is performed based on the potential benefits of the information core convergence hypothesis.

[0046] In a specific example of the present application, the step S2322 is expressed by the formula:

[0047] in, represents the gating threshold, is a natural constant, represents the gating function, The first time series representing the kernel convergence compensation weight factor of the local time series characteristics of water body temperature The kernel convergence compensation weight factor of the local time series characteristics of water body temperature.

[0048] That is, after obtaining the local time series characteristic core convergence compensation factor of the water body temperature, the present application further regulates the effect of the local time series characteristic core convergence compensation factor of the water body temperature through explicit compensation modeling based on the gating function. It should be understood that the gating function can dynamically select information based on nonlinear constraints. By explicitly modeling the local time series characteristic core convergence compensation factor of the water body temperature, the system can screen its effect intensity and generate a local time series characteristic core convergence compensation weight factor of the water body temperature to ensure that only locally significant features are highlighted, while irrelevant or redundant information is appropriately suppressed. Through this explicit regulation, the model can more accurately capture the dynamic interaction between global and local information.

[0049] In a specific example of the present application, the step S2323 is expressed by the formula:

[0050] in, A fine-grained aggregation encoding vector representing the local time series compensation feature of the water body temperature.

[0051] That is, based on the obtained water body temperature local time series feature kernel convergence compensation weight factor, the difference information between the coarse-grained converged coding vector of the water body temperature local time series feature and the water body temperature local time series implicit correlation feature vector is dynamically adjusted, and the fine-grained converged coding vector of the water body temperature local time series compensation feature is generated through a dynamic compensation convergence network, so that the compensated water body temperature local time series compensation feature fine-grained converged coding vector can more accurately describe the local details of the local time series nodes, while being able to adapt to the dynamic changes of the global feature constraints, thereby achieving coordinated expression of features at the global and local levels.

[0052] In a specific example of the present application, the step S2324 is expressed by the formula:

[0053] in, and represents different weight parameters, Represents the implicit coding vector of the time series integral of the water body temperature.

[0054] That is, the residual unit is used to linearly weight the fine-grained aggregation coding vector of the local temporal compensation feature of the water body temperature and the coarse-grained aggregation coding vector of the local temporal feature of the water body temperature to generate the implicit coding vector of the temporal integral of the water body temperature. The residual unit can avoid the gradient vanishing problem while ensuring feature transfer, making the model easier to optimize and further improving the significance and robustness of the coding results.

[0055] Specifically, the step S24 includes: first, the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector are respectively input into the water consumption prediction module based on the decoder to obtain the first water consumption prediction value and the second water consumption prediction value. Specifically, the decoder model has been trained with a large amount of historical data and has effectively learned the complex mapping relationship between water body temperature and water body pressure and water consumption. Therefore, when the decoder receives the water body temperature time series integral implicit coding vector and the water body pressure time series implicit integration coding vector, it can accurately decode the user's water consumption prediction result within the target time period according to the time series change information of the water body temperature and water body pressure contained in the vector. Here, the present application independently decodes and predicts the water body temperature time series integral implicit coding vector and the water body pressure time series implicit integration coding vector to obtain the water consumption prediction results based on water body temperature and water body pressure, i.e., the first water consumption prediction value and the second water consumption prediction value, respectively. Among them, the first water consumption prediction value reflects the water consumption of the user in the target time period under the influence of the current water temperature conditions, while the second water consumption prediction value reveals the water consumption of the user in the target time period under the influence of the water pressure.

[0056] Then, the average of the first water consumption prediction value and the second water consumption prediction value is calculated to obtain the initial water consumption. That is, in order to comprehensively consider the combined effect of water body temperature and water body pressure, the present application combines the combined effect of the water body temperature and water body pressure by calculating the average of the water consumption prediction results based on water body temperature and water body pressure, thereby obtaining an initial water consumption that is closer to the actual water consumption of the user.

[0057] Specifically, in each sampling cycle, the system collects the water temperature and pressure values ​​at the current moment, combines them with the data of the previous cycle, and calculates the cumulative effect in this time period through numerical integration methods (such as the trapezoidal rule). The implicit coding vector generated by this step can capture subtle changes on a short time scale, providing a rich information basis for subsequent analysis. These coding vectors not only contain the changing trends of the physical quantities themselves, but also reflect the potential impact of environmental factors on water use behavior. In actual operation, the decoder receives the implicit coding vector obtained by fine-grained integral coding as input, and tries to extract patterns related to water consumption from it. In order to improve the prediction accuracy, a multi-layer structure can be used to increase the expressive power of the model, and regularization methods such as dropout can be used to prevent overfitting.

[0058] Then, before calculating the mean, the quality and integrity of the input data must be verified. If any of the predicted values ​​deviate significantly from the expected range or show abnormal fluctuations, the cause should be further investigated to avoid using unreliable data to affect the final results. To this end, a series of data cleaning techniques can be used, including identifying and removing outliers, filling in missing values, etc., to ensure that every piece of data used for calculation has a high degree of credibility.

[0059] In the calculation process, it is necessary to adopt appropriate numerical representations considering possible decimal precision issues. Generally speaking, floating-point numbers are sufficient to meet the needs of most application scenarios, but in some cases with high precision requirements, it may be necessary to use higher-precision data types, such as the Decimal class provided by the decimal module in Python. This approach allows users to specify the required level of precision, thereby reducing deviations caused by rounding errors. In addition, when calculating the average value, attention should also be paid to the issue of weight distribution. Although in the simplest case, the first water consumption forecast value and the second water consumption forecast value are given the same weight (i.e., 0.5), in some special situations, adjusting the weight distribution according to the specific situation may bring better results. For example, if the historical performance of one variable is better than that of another variable, the former can be given a greater weight when calculating the average value.

[0060] In the error checking method of the smart water meter, the step S3 corrects the initial water consumption based on the historical monthly water consumption data of the month information corresponding to the target time period to obtain the second water consumption. Figure 6 FIG. 1 is a flowchart of sub-step S3 of the error checking method of the smart water meter according to an embodiment of the present application. Figure 6 As shown, the step S3 includes the steps of: S31, extracting the historical monthly water consumption of the month information corresponding to the target time period to obtain a reference set of historical monthly water consumption; S32, determining a water consumption correction coefficient based on the reference set of historical monthly water consumption; S33, multiplying the water consumption correction coefficient by the initial water consumption to obtain the second water consumption.

[0061] Specifically, the step S31 extracts the historical monthly water consumption of the month information corresponding to the target time period to obtain a reference set of historical monthly water consumption. It should be understood that, considering that the user's life pattern and water demand have certain repeatability and similarity, that is, due to the certain stability of factors such as climate conditions and social activities in the same month of each year, the user's water consumption behavior in the same month of each year also has a certain regularity. Therefore, in order to improve the accuracy and reliability of water consumption prediction, the present application further extracts the water consumption data of the month in the past few years from the historical database according to the month information corresponding to the target time period, and forms a reference set of historical monthly water consumption, so as to analyze and mine the user's water consumption behavior characteristics in the month based on the historical data, and correct the above-mentioned water consumption prediction results to improve the accuracy and applicability of the water consumption prediction results.

[0062] Specifically, considering that the amount of data may be very large, it is crucial to choose the right database management system. Relational databases such as MySQL or PostgreSQL are common choices due to their stability and powerful query capabilities; at the same time, for large-scale data sets, non-relational databases such as MongoDB also show significant advantages, especially when processing semi-structured data. Regardless of the type of database used, it is necessary to ensure that it has good scalability so that the storage strategy can be flexibly adjusted as the amount of data grows.

[0063] After determining the appropriate data storage solution, the next step is to design the corresponding query logic to accurately extract the required historical monthly water consumption from the massive data. In this process, the month information corresponding to the target time period must be clearly identified, which usually involves operations such as date parsing and time interval matching. For example, if the target time period is July 2024, it is necessary to locate the water consumption data belonging to that month in all records. To this end, you can write a special SQL statement or other form of query script to filter qualified records based on key fields such as user ID and collection date. It is worth noting that before executing the query, the data should be preprocessed, including steps such as cleaning invalid or erroneous data and filling missing values ​​to ensure that subsequent analysis is based on high-quality information.

[0064] For the extraction of historical monthly water consumption, it is also necessary to consider how to effectively organize and present this data for subsequent analysis. One feasible approach is to create a structured reference set that contains the water consumption records of all relevant users in each target month. This set should not only cover the original values, but also come with some additional metadata, such as geographic location information, user type (domestic or commercial users), etc., to provide a more comprehensive perspective. In addition, in order to facilitate comparison and trend analysis, the data for each month can be further subdivided by week or day to form a more detailed time series. Doing so helps capture short-term fluctuations in water consumption and thus improve the accuracy of the forecasting model.

[0065] In the process of constructing a reference set of historical monthly water consumption, another important step is to standardize and normalize the data. Since water use habits may vary greatly between different regions and different types of users, directly comparing the original water consumption values ​​may lead to misleading conclusions. Therefore, the introduction of standardization methods, such as Z-score standardization or Min-Max normalization, can eliminate the bias caused by scale effects, so that data from different backgrounds can be compared on a unified scale. In addition, statistical indicators such as average water consumption and standard deviation in each month can be calculated as an important basis for evaluating the characteristics of water use behavior. These statistics can not only reveal long-term trends, but also help identify abnormal situations, such as sudden high water consumption events or sustained low consumption periods, providing clues for further in-depth analysis.

[0066] At the same time, in order to enhance the practical value of the reference set, it is also necessary to explore how to combine the impact of external factors on historical monthly water consumption. For example, factors such as temperature changes, holiday arrangements, and even the level of socioeconomic activities may indirectly affect users' water demand. Therefore, when constructing a reference set, it is advisable to try to integrate the relevant data of these external variables to form a multi-dimensional comprehensive data set. Specifically, historical weather records can be obtained from the meteorological department, public holiday information can be collected from government websites, and even social media analysis tools can be used to track local community dynamics. In this way, a more sophisticated water use behavior model can be established to better understand the interaction mechanism between various internal and external factors.

[0067] Specifically, in a specific example of the present application, the step S32 includes: inputting the reference set of the historical monthly water consumption into a feature encoding-decoding module based on the LSTM model to obtain the water consumption correction coefficient. Those of ordinary skill in the art should know that the LSTM (Long Short-Term Memory Network) model is widely used in time series prediction tasks because it can capture long-term dependencies in sequence data. In the present application, the LSTM model learns the long-term dependencies and periodic laws in the historical monthly water consumption data by encoding the time series features of the reference set of the historical monthly water consumption. Then, the time series features of the historical monthly water consumption data are decoded by a decoder to obtain the water consumption correction coefficient of the historical monthly water consumption data, which is used to characterize the fluctuations and trend characteristics of the historical monthly water consumption data, so as to further correct the current water consumption prediction results.

[0068] Specifically, in step S33, the water consumption correction coefficient is multiplied by the initial water consumption to obtain the second water consumption. Specifically, if the correction coefficient is greater than 1, it means that the water consumption in the target time period may be higher than the initial estimated value, which may be due to the fact that the water consumption in this month is usually large; if it is less than 1, it means that the water demand in this month is relatively low, and the water consumption may be lower than the initial estimated value. In this way, the data information of the historical monthly water consumption is effectively utilized, and the initial predicted value is reasonably adjusted, thereby providing a more accurate water consumption prediction value for subsequent error verification.

[0069] In the error checking method of the above-mentioned smart water meter, in step S4, the actual water consumption measurement value of the target time period is used as the first water consumption, and based on the difference between the first water consumption and the second water consumption, the multifunctional smart water meter is error checked. Accordingly, if the smart water meter measures accurately, the first water consumption and the second water consumption should be relatively close. If the difference between the first water consumption and the second water consumption is large, it indicates that there may be a measurement error in the smart water meter. Here, it can be judged whether the error exceeds the acceptable range based on the set difference threshold. If the error exceeds the acceptable range, the alarm mechanism is triggered, prompting the smart water meter to be inspected and maintained.

[0070] In summary, the error checking method of the smart water meter based on the embodiment of the present application is explained, which uses a neural network model based on deep learning to extract fine-grained time series features from the time series data of the temperature and pressure of the water body flowing through the multifunctional smart water meter during the target time period, so as to capture the time series change pattern of the water body temperature and pressure in each local time domain, and to understand the time series cumulative effect of the water body temperature and pressure on the water consumption in the global time domain by respectively performing time series integral encoding on the time series change characteristics of the water body temperature and pressure in each local time domain, so as to realize the intelligent prediction of the water consumption, and then perform error checking on the smart water meter according to the difference between the prediction result and the water meter measurement value. In this way, the measurement error of the smart water meter can be reflected more accurately, thereby improving the accuracy of error checking and the intelligent level of water management.

[0071] Furthermore, an error checking system for a smart water meter is also provided.

[0072] Figure 7 FIG. 1 is a block diagram of an error checking system for a smart water meter according to an embodiment of the present application. Figure 7As shown, according to the error checking system 100 of the smart water meter of the embodiment of the present application, it includes: a monitoring parameter acquisition module 110, which is used to obtain the monitoring parameters of the multifunctional smart water meter within the target time period, and the monitoring parameters include: the date information of the target time period, the actual water consumption measurement value, and the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter; an initial water consumption determination module 120, which is used to determine the initial water consumption based on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter, wherein determining the initial water consumption includes: performing time series fine-grained integral encoding and decoding prediction on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption; an initial water consumption correction module 130, which is used to correct the initial water consumption based on the historical monthly water consumption data of the month information corresponding to the target time period to obtain a second water consumption; an error checking module 140, which is used to use the actual water consumption measurement value of the target time period as the first water consumption, and based on the difference between the first water consumption and the second water consumption, perform error checking on the multifunctional smart water meter.

[0073] Here, those skilled in the art can understand that the specific operations of each module in the error checking system of the smart water meter have been described in the above reference. Figures 1 to 6 The error checking method of the smart water meter has been described in detail, and therefore, its repeated description will be omitted.

[0074] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0075] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0076] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0077] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0078] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An error checking method for a smart water meter, characterized in that: include: Acquire monitoring parameters of the multifunctional smart water meter within a target time period, wherein the monitoring parameters include: date information of the target time period, actual water consumption measurement value, and a time series of temperature and pressure of a water body flowing through the multifunctional smart water meter; Based on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter, determining the initial water consumption, wherein determining the initial water consumption comprises: performing temporal fine-grained integral encoding and decoding prediction on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption; Based on the historical monthly water consumption data of the month information corresponding to the target time period, the initial water consumption is corrected to obtain a second water consumption; The actual water consumption measurement value of the target time period is used as the first water consumption, and based on the difference between the first water consumption and the second water consumption, the multifunctional smart water meter is error-checked.

2. The error checking method for a smart water meter according to claim 1, characterized in that: Based on the time series of the temperature and pressure of the water flowing through the multifunctional intelligent water meter, determining the initial water consumption includes: Arrange the time series of the temperature and pressure of the water flowing through the multifunctional intelligent water meter to obtain a time series of the water temperature and a time series of the water pressure; Performing local time series feature extraction on the time series of the water body temperature and the time series of the water body pressure to obtain a time series of implicitly associated feature vectors of the local time series of the water body temperature and a time series of implicitly associated feature vectors of the local time series of the water body pressure; Inputting the time series of the local time series implicit correlation feature vector of the water body temperature and the time series of the local time series implicit correlation feature vector of the water body pressure into a time series integrator respectively to obtain a water body temperature time series integral implicit coding vector and a water body pressure time series integral implicit coding vector; The water consumption is predicted based on the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector to obtain the initial water consumption.

3. The error checking method for a smart water meter according to claim 2, characterized in that: Inputting the time series of the local time series implicitly associated characteristic vector of the water body temperature into a time series integrator to obtain a water body temperature time series integral implicit coding vector, including: Performing information kernel coarse-grained aggregation on the time series of the implicitly associated feature vector of the local time series of the water body temperature to obtain a coarse-grained aggregation encoding vector of the local time series feature of the water body temperature; Based on the feature differences of each local time series implicit correlation feature vector of water body temperature in the time series of the local time series implicit correlation feature vector of water body temperature relative to the coarse-grained aggregation coding vector of the local time series feature of water body temperature, the coarse-grained aggregation coding vector of the local time series feature of water body temperature is dynamically compensated and encoded to obtain the water body temperature time series integral implicit coding vector.

4. The error checking method for a smart water meter according to claim 3, characterized in that: Based on the feature difference of each water body temperature local time series implicit correlation feature vector in the time series of the water body temperature local time series implicit correlation feature vector relative to the water body temperature local time series feature coarse-grained aggregation coding vector, the water body temperature local time series feature coarse-grained aggregation coding vector is dynamically compensated and encoded to obtain the water body temperature time series integral implicit coding vector, including: Calculating the kernel convergence compensation factor of each water body temperature local time series implicit correlation feature vector in the time series of the water body temperature local time series implicit correlation feature vector relative to the water body temperature local time series feature coarse-grained convergence encoding vector to obtain a time series of the water body temperature local time series feature kernel convergence compensation factors; Performing compensation explicit modeling based on a gating function on the time series of the local time series characteristic kernel convergence compensation factor of the water body temperature to obtain the time series of the local time series characteristic kernel convergence compensation weight factor of the water body temperature; Input the time series of the water body temperature local time series feature kernel convergence compensation weight factor, the water body temperature local time series feature coarse-grained convergence encoding vector and the water body temperature local time series implicit correlation feature vector into a fine-grained dynamic compensation convergence network to obtain a water body temperature local time series compensation feature fine-grained convergence encoding vector; The fine-grained aggregation coding vector of the local time series compensation feature of the water body temperature and the coarse-grained aggregation coding vector of the local time series feature of the water body temperature are input into the residual unit to obtain the implicit coding vector of the water body temperature time series integration.

5. The error checking method for a smart water meter according to claim 4, characterized in that: Calculating the kernel convergence compensation factor of each water body temperature local time series implicit correlation feature vector in the time series of the water body temperature local time series implicit correlation feature vector relative to the water body temperature local time series feature coarse-grained convergence encoding vector to obtain the time series of the water body temperature local time series feature kernel convergence compensation factor, including: Performing point convolution encoding based on Sigmoid activation function on the water body temperature local time series implicit correlation feature vector and the water body temperature local time series feature coarse-grained aggregation coding vector respectively to obtain a standardized water body temperature local time series implicit correlation feature vector and a standardized water body temperature local time series feature coarse-grained aggregation coding vector; Calculate the position difference vector between the standardized water body temperature local time series implicit correlation feature vector and the standardized water body temperature local time series feature coarse-grained convergence coding vector, and take the absolute value of the position difference vector to obtain the water body temperature local time series feature kernel convergence difference compensation coding vector; The water body temperature local time series feature kernel convergence difference compensation encoding vector is input into the compensation feature importance scoring module based on the neural network layer to obtain the water body temperature local time series feature kernel convergence compensation factor.

6. The error checking method for a smart water meter according to claim 5, characterized in that: Inputting the water body temperature local time series feature kernel convergence difference compensation encoding vector into the compensation feature importance scoring module based on the neural network layer to obtain the water body temperature local time series feature kernel convergence compensation factor, including: Calculate the ratio between the Euclidean norm of the local time series implicit correlation feature vector of the water body temperature and the Euclidean norm of the coarse-grained aggregation encoding vector of the local time series feature of the water body temperature. If the ratio is less than 1, add one to the ratio and calculate the logarithmic value with base 2 as the bias item of the neural network layer of the compensation feature importance scoring module; If the ratio is greater than or equal to 1, the ratio is used as a bias item of the neural network layer of the compensatory feature importance scoring module.

7. The error checking method for a smart water meter according to claim 6, characterized in that: The method of predicting water consumption based on the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector to obtain the initial water consumption includes: Inputting the water body temperature time series integral implicit coding vector and the water body pressure time series integral implicit coding vector into a water consumption prediction module based on a decoder to obtain a first water consumption prediction value and a second water consumption prediction value respectively; The average of the first water consumption prediction value and the second water consumption prediction value is calculated to obtain the initial water consumption.

8. The error checking method for a smart water meter according to claim 7, characterized in that: Based on the historical monthly water consumption data of the month information corresponding to the target time period, the initial water consumption is corrected to obtain a second water consumption, including: Extract the historical monthly water consumption of the month information corresponding to the target time period to obtain a reference set of historical monthly water consumption; Determining a water consumption correction factor based on the reference set of historical monthly water consumption; The water consumption correction coefficient is multiplied by the initial water consumption to obtain the second water consumption.

9. The error checking method for a smart water meter according to claim 8, characterized in that: Based on the reference set of historical monthly water consumption, a water consumption correction factor is determined, including: The reference set of the historical monthly water consumption is input into a feature encoding-decoding module based on the LSTM model to obtain the water consumption correction coefficient.

10. An error checking system for a smart water meter, characterized in that: include: A monitoring parameter acquisition module is used to acquire monitoring parameters of the multifunctional smart water meter within a target time period, wherein the monitoring parameters include: date information of the target time period, actual water consumption measurement value, temperature of the water body flowing through the multifunctional smart water meter, and time series of pressure; An initial water consumption determination module is used to determine the initial water consumption based on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter, wherein determining the initial water consumption includes: performing time series fine-grained integral encoding and decoding prediction on the time series of the temperature and pressure of the water body flowing through the multifunctional smart water meter to obtain the initial water consumption; An initial water consumption correction module, configured to correct the initial water consumption based on the historical monthly water consumption data of the month information corresponding to the target time period to obtain a second water consumption; The error checking module is used to use the actual water consumption measurement value in the target time period as the first water consumption, and perform error checking on the multifunctional intelligent water meter based on the difference between the first water consumption and the second water consumption.

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