CN112183830A - Method and device for predicting temperature of chilled water - Google Patents
Method and device for predicting temperature of chilled water Download PDFInfo
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- CN112183830A CN112183830A CN202010975879.7A CN202010975879A CN112183830A CN 112183830 A CN112183830 A CN 112183830A CN 202010975879 A CN202010975879 A CN 202010975879A CN 112183830 A CN112183830 A CN 112183830A
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- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
Abstract
The invention discloses a method and a device for predicting the temperature of chilled water, a readable medium and electronic equipment, wherein the method comprises the following steps: acquiring at least two historical chilled water temperature data acquired by a chilled water temperature sensor on refrigeration equipment and historical influence factor data respectively corresponding to the historical chilled water temperature data; and training the model to be trained according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively to determine a chilled water temperature prediction model, wherein the chilled water temperature prediction model is used for predicting the chilled water temperature data of the refrigeration equipment. By the technical scheme provided by the invention, the inlet water temperature and the outlet water temperature of the chilled water can be predicted by the chilled water temperature prediction model under the condition that the temperature sensor fails.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a method and a device for predicting the temperature of chilled water.
Background
Load control of a refrigerating unit is adjusted according to the difference value of inlet water temperature and outlet water temperature of chilled water, the inlet water temperature and the outlet water temperature of the chilled water are collected by a temperature sensor, if the temperature sensor fails, data of the inlet water temperature and the outlet water temperature of the collected chilled water cannot reflect the real temperature of the chilled water, error of load control can be caused, energy consumption is increased, and therefore the inlet water temperature and the outlet water temperature of the chilled water are collected under the condition that the temperature sensor fails, and the load control is very important.
However, the prior art lacks a method for solving the above technical problems, and therefore how to acquire the inlet water temperature and the outlet water temperature of the chilled water when the temperature sensor fails is an urgent technical problem to be solved.
Disclosure of Invention
The invention provides a method and a device for predicting chilled water temperature, a computer readable storage medium and electronic equipment aiming at the technical problems in the prior art, and the method and the device can predict the inlet water temperature and the outlet water temperature of the chilled water through a chilled water temperature prediction model under the condition that a temperature sensor fails.
In a first aspect, the present invention provides a method for predicting chilled water temperature, comprising:
acquiring at least two historical chilled water temperature data acquired by a chilled water temperature sensor on refrigeration equipment and historical influence factor data corresponding to the historical chilled water temperature data respectively;
training a model to be trained according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively to determine a chilled water temperature prediction model, wherein the chilled water temperature prediction model is used for predicting chilled water temperature data of the refrigeration equipment.
In a second aspect, the present invention provides a method for determining failure of a chilled water temperature sensor, including:
acquiring current influence factor data corresponding to the chilled water temperature prediction model, the current chilled water temperature data acquired by the chilled water temperature sensor and at least two influence factors corresponding to the historical influence factor data;
substituting the current influence factor data corresponding to each influence factor into the chilled water temperature prediction model to determine predicted chilled water temperature data;
and judging whether the chilled water temperature sensor fails or not according to the current chilled water temperature data and the predicted chilled water temperature data.
In a third aspect, the present invention provides a device for predicting a temperature of chilled water, comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring at least two historical chilled water temperature data acquired by a chilled water temperature sensor on refrigeration equipment and historical influence factor data corresponding to the historical chilled water temperature data respectively;
and the training module is used for training a model to be trained according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively so as to determine a chilled water temperature prediction model, and the chilled water temperature prediction model is used for predicting the chilled water temperature data of the refrigeration equipment.
In a fourth aspect, the present invention provides a chilled water temperature sensor failure determination device, including:
the second acquisition module is used for acquiring the chilled water temperature prediction model, the current chilled water temperature data acquired by the chilled water temperature sensor and current influence factor data corresponding to at least two influence factors corresponding to the historical influence factor data;
the prediction module is used for substituting the current influence factor data corresponding to each influence factor into the chilled water temperature prediction model so as to determine the predicted chilled water temperature data;
and the failure judgment module is used for judging whether the chilled water temperature sensor fails or not according to the current chilled water temperature data and the predicted chilled water temperature data.
In a fifth aspect, the invention provides a computer-readable storage medium comprising executable instructions which, when executed by a processor of an electronic device, perform the method according to any one of the first aspect or the method according to any one of the second aspect.
In a sixth aspect, the present invention provides an electronic device comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect or the method according to any one of the second aspect.
The invention provides a method for predicting chilled water temperature, a method for judging failure of a chilled water temperature sensor, a device, a computer readable storage medium and electronic equipment.
Further effects of the above-mentioned unconventional preferred modes will be described below in conjunction with specific embodiments.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flow chart of a method for predicting chilled water temperature according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a method for determining failure of a chilled water temperature sensor according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for predicting a temperature of chilled water according to an embodiment of the present invention;
fig. 4 is a schematic structural view of a failure determination device for a chilled water temperature sensor according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for predicting a chilled water temperature, which includes the following steps:
and 102, training a model to be trained according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively to determine a chilled water temperature prediction model, wherein the chilled water temperature prediction model is used for predicting the chilled water temperature data of the refrigeration equipment.
The embodiment of the invention provides a method for predicting chilled water temperature, which comprises the steps of acquiring a plurality of historical chilled water temperature data acquired by a chilled water temperature sensor on refrigeration equipment and historical influence factor data corresponding to the historical chilled water temperature data respectively, and then training a model to be trained according to the historical influence factor data corresponding to the historical chilled water temperature data and the historical chilled water temperature data respectively to determine a chilled water temperature prediction model, wherein the chilled water temperature prediction model is used for predicting the chilled water temperature data of the refrigeration equipment, so that the inlet water temperature and the outlet water temperature of chilled water can be predicted through the chilled water temperature prediction model under the condition that the temperature sensor fails.
The method provided by the embodiment of the invention is applied to electronic equipment, wherein the electronic equipment can be a computer or a general server, and is not specifically limited herein.
Specifically, a refrigeration device may be understood as a device for refrigeration, such as an evaporator, where the evaporator is a heat exchanger that absorbs heat of a cooling medium depending on evaporation (actual boiling) of a refrigerant liquid, and where a chilled water temperature prediction model provided by an embodiment of the present invention is mainly used for predicting an inlet water temperature of chilled water entering the evaporator and an outlet water temperature of the chilled water flowing out of the evaporator, that is, the outlet water temperature of the chilled water is acquired based on a temperature sensor at an evaporator water inlet and the outlet water temperature of the chilled water is acquired based on a temperature sensor at an evaporator water outlet, and accordingly, the number of the chilled water temperature sensors is two, one is installed at the evaporator water inlet and the other is installed at the evaporator water outlet.
Specifically, each historical influence factor data corresponds to a time point, the time points corresponding to different historical influence factor data are different, the historical influence factor data comprises a plurality of influence factors influencing chilled water temperature data, the plurality of influence factors respectively correspond to the historical data at a certain time point, and here, the number of the plurality of influence factors and the number of the influence factors need to be determined by combining with an actual scene.
Optionally, the chilled water temperature data includes an inlet water temperature of the chilled water and an outlet water temperature of the chilled water, and the historical influencing factor data includes an outdoor temperature and a refrigerant saturation temperature. As a possible implementation, the refrigerant saturation temperature is determined based on a refrigerant saturation evaporating pressure collected by a pressure sensor on the refrigeration equipment and a preset refrigerant saturation temperature pressure function. The preset refrigerant saturation temperature pressure function can be a cubic polynomial of saturation temperature-pressure, or a quintic polynomial of saturation temperature-pressure, preferably a quintic polynomial.
Considering that data of the refrigeration device at the time of startup and shutdown are unstable, if model training is performed by using the data, the model accuracy of the obtained chilled water temperature prediction model may be reduced, and therefore, in order to ensure the reference value of the data used for model training and the model accuracy, optionally, each historical chilled water temperature data does not include historical chilled water temperature data of the refrigeration device in a preset startup period and a preset shutdown period. The preset starting time interval and the preset shutdown time interval need to be set by combining the actual running condition of the refrigeration equipment.
In an embodiment of the present invention, the training a model to be trained according to each historical chilled water temperature data and historical influence factor data corresponding to each historical chilled water temperature data to determine a chilled water temperature prediction model includes:
determining a training set and a testing set according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively;
obtaining at least two models to be trained, carrying out model training on the models to be trained according to the training set aiming at each model to be trained so as to determine the trained models, and determining the trained models as candidate models;
determining model precision corresponding to each candidate model according to the test set;
and determining a chilled water temperature prediction model according to the candidate models and the model precision corresponding to each candidate model.
In the embodiment, a training set and a test set are determined according to historical chilled water temperature data and historical influence factor data corresponding to the historical chilled water temperature data respectively, then, a plurality of models to be trained are obtained, for each model to be trained, model training is carried out on the model to be trained according to a training set so as to determine the trained model, the trained model is determined as a candidate model, then, according to the test set, determining the model accuracy corresponding to each candidate model, finally, according to the model accuracy corresponding to each candidate model and each candidate model, determining a chilled water temperature prediction model, determining the obtained chilled water temperature prediction model based on the model accuracy of a plurality of trained models, therefore, the model precision of the chilled water temperature prediction model is ensured, and the accuracy of the chilled water temperature data predicted by the chilled water temperature prediction model is further ensured.
Specifically, historical chilled water temperature data and historical influence factor data corresponding to the historical chilled water temperature data are used as one piece of data, so that a plurality of pieces of data are obtained, and the plurality of pieces of data are divided, so that a training set and a test set are obtained. Optionally, the partitioning of the plurality of pieces of data is a random partitioning, preferably the ratio of the amount of data in the training set and the test set is 4: 1.
It should be noted that, the model training process and the determination process of the model precision are both in the prior art, and are not described herein in detail. Alternatively, the model accuracy may be expressed by the magnitude of the mean square error, with the smaller the error, the higher the model accuracy.
Optionally, the plurality of models to be trained include, but are not limited to, any two or more of a multiple nonlinear regression model, a support vector regression model, and a neural network model, preferably, the multiple nonlinear regression model and the support vector regression model, so as to reduce the amount of computation in model training.
In an embodiment of the present invention, the determining a chilled water temperature prediction model according to model accuracies respectively corresponding to each candidate model and each candidate model includes:
and determining the candidate model corresponding to the maximum value in the model accuracy as a chilled water temperature prediction model.
Specifically, the candidate model with the highest model precision is selected as the chilled water temperature prediction model, so that the model precision of the chilled water temperature prediction model is ensured, and the accuracy of the chilled water temperature data predicted by the chilled water temperature prediction model is further ensured.
It should be noted that, it is the best implementation manner to select the candidate model with the highest model accuracy as the chilled water temperature prediction model, or select several candidate models with model accuracy greater than the preset value as the chilled water temperature prediction models, or sort the candidate models in the descending order of model accuracy, and take the candidate models respectively corresponding to the model accuracy in the top as the chilled water temperature prediction models, and then, during prediction, weighted average may be performed on the chilled water temperature data respectively predicted by several candidate models to obtain the prediction result.
As shown in fig. 2, an embodiment of the present invention provides a method for determining failure of a chilled water temperature sensor, including the following steps:
202, substituting the current influence factor data corresponding to each influence factor into the chilled water temperature prediction model to determine predicted chilled water temperature data;
and 203, judging whether the chilled water temperature sensor fails or not according to the current chilled water temperature data and the predicted chilled water temperature data.
The embodiment of the invention provides a failure judgment method for a chilled water temperature sensor, which comprises the steps of obtaining a chilled water temperature prediction model, current chilled water temperature data acquired by the chilled water temperature sensor and current influence factor data corresponding to a plurality of influence factors corresponding to historical influence factor data respectively, substituting the current influence factor data corresponding to the influence factors into the chilled water temperature prediction model to determine the predicted chilled water temperature data, judging whether the chilled water temperature sensor fails or not according to the current chilled water temperature data and the predicted chilled water temperature data, and judging whether the temperature sensor fails or not without reading judgment on the temperature sensor manually, so that whether the temperature sensor fails or not can be judged more simply and rapidly.
When the chilled water temperature sensor is invalid, the accuracy of the chilled water temperature data detected by the chilled water temperature sensor can be greatly reduced, so that the load control of a refrigerating unit is possibly failed, and the user requirements cannot be met. Therefore, the failure detection of the chilled water temperature sensor is needed, whether the chilled water temperature sensor fails or not is judged by comparing the predicted value of the chilled water temperature prediction model with the current true value acquired by the chilled water temperature sensor, and then the load control strategy is changed in time under the condition that the chilled water fails, so that the normal operation of a refrigerating unit is ensured, and the user requirements are met.
In an embodiment of the present invention, the determining whether the chilled water temperature sensor fails according to the current chilled water temperature data and the predicted chilled water temperature data includes:
and when the absolute value of the difference value between the current chilled water temperature data and the predicted chilled water temperature data exceeds a threshold value, judging that the chilled water temperature sensor is invalid.
Specifically, the current chilled water temperature data includes a current inlet water temperature and a current outlet water temperature of the chilled water, the predicted chilled water temperature data includes a predicted inlet water temperature and a predicted outlet water temperature of the chilled water, correspondingly, the absolute difference value can be understood as a first absolute difference value between the current inlet water temperature and the predicted inlet water temperature of the chilled water, and can also be understood as a second absolute difference value between the current outlet water temperature and the predicted outlet water temperature of the chilled water, and when the first absolute difference value or the second absolute difference value is greater than a corresponding threshold value, it is described that an error between the predicted value and the true value is too large, and it can be determined that the chilled water temperature sensor is out of service. It should be noted that the threshold corresponding to the first difference absolute value and the threshold corresponding to the second difference absolute value may be the same or different, and the specific need needs to be determined by combining with the actual situation.
Based on the same concept as the method for predicting the temperature of the chilled water provided by the embodiment of the method of the present invention, referring to fig. 3, the present invention implements a device for predicting the temperature of the chilled water, including:
the first acquisition module 301 is configured to acquire at least two historical chilled water temperature data acquired by a chilled water temperature sensor on a refrigeration device and historical influence factor data corresponding to each of the historical chilled water temperature data;
a training module 302, configured to train a model to be trained according to each historical chilled water temperature data and historical influence factor data corresponding to each historical chilled water temperature data, so as to determine a chilled water temperature prediction model, where the chilled water temperature prediction model is used to predict chilled water temperature data of the refrigeration equipment.
In an embodiment of the present invention, the training module 302 includes: the system comprises a dividing unit, a model training unit, a model precision determining unit and a model determining unit; wherein the content of the first and second substances,
the dividing unit is used for determining a training set and a testing set according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively;
the model training unit is used for acquiring at least two models to be trained, carrying out model training on the models to be trained according to the training set aiming at each model to be trained so as to determine the trained models, and determining the trained models as candidate models;
the model precision determining unit is used for determining the model precision corresponding to each candidate model according to the test set;
and the model determining unit is used for determining the chilled water temperature prediction model according to the candidate models and the model precision corresponding to the candidate models respectively.
In one embodiment of the present invention, the model determination unit is configured to determine a candidate model corresponding to a maximum value among the model accuracies as the chilled water temperature prediction model.
In an embodiment of the present invention, the at least two models to be trained include: any two or more of a multivariate nonlinear regression model, a support vector regression model, a neural network model.
In one embodiment of the present invention, the historical chilled water temperature data includes: the inlet water temperature of the chilled water and the outlet water temperature of the chilled water;
the historical impact factor data includes: outdoor temperature and refrigerant saturation temperature.
In one embodiment of the invention, the refrigerant saturation temperature is determined based on a refrigerant saturation evaporating pressure collected by a pressure sensor on the refrigeration equipment and a preset refrigerant saturation temperature pressure function.
In an embodiment of the present invention, each of the historical chilled water temperature data does not include historical chilled water temperature data of the refrigeration equipment in a preset startup period and a preset shutdown period.
Referring to fig. 4, a chilled water temperature sensor failure determination method provided in an embodiment of the present invention is implemented as a chilled water temperature sensor failure determination device, including:
a second obtaining module 401, configured to obtain a chilled water temperature prediction model in the chilled water temperature prediction method provided in the embodiment, current chilled water temperature data acquired by the chilled water temperature sensor, and current influence factor data corresponding to at least two influence factors corresponding to the historical influence factor data, respectively;
a prediction module 402, configured to substitute current influence factor data corresponding to each influence factor into the chilled water temperature prediction model to determine predicted chilled water temperature data;
and a failure determining module 403, configured to determine whether the chilled water temperature sensor fails according to the current chilled water temperature data and the predicted chilled water temperature data.
In an embodiment of the present invention, the failure determination module 403 is configured to determine that the chilled water temperature sensor fails when an absolute value of a difference between the current chilled water temperature data and the predicted chilled water temperature data exceeds a threshold.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device includes a processor 501 and a memory 502 storing execution instructions, and optionally includes an internal bus 503 and a network interface 504. The Memory 502 may include a Memory 5021, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory 5022(non-volatile Memory), such as at least 1 disk Memory; the processor 501, the network interface 504, and the memory 502 may be connected to each other by an internal bus 503, and the internal bus 503 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like; the internal bus 503 may be divided into an address bus, a data bus, a control bus, etc., and is indicated by only one double-headed arrow in fig. 5 for convenience of illustration, but does not indicate only one bus or one type of bus. Of course, the electronic device may also include hardware required for other services. When the processor 501 executes execution instructions stored by the memory 502, the processor 501 performs a method in any of the embodiments of the present invention and at least is used to perform the method as shown in fig. 1 or fig. 2.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the corresponding execution instruction, and the corresponding execution instruction can also be obtained from other equipment, so as to form a chilled water temperature prediction device or a chilled water temperature sensor failure judgment device on a logic level. The processor executes the execution instructions stored in the memory to realize a method for predicting the temperature of the chilled water or a method for judging the failure of the chilled water temperature sensor provided by any embodiment of the invention through the executed execution instructions.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Embodiments of the present invention further provide a computer-readable storage medium, which includes an execution instruction, and when a processor of an electronic device executes the execution instruction, the processor executes a method provided in any one of the embodiments of the present invention. The electronic device may specifically be the electronic device shown in fig. 5; the execution instruction is a computer program corresponding to a method for predicting the temperature of the chilled water or a method for judging the failure of the chilled water temperature sensor.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The multiple embodiments of the present invention are described in a progressive manner, and the same and similar parts among the multiple embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (11)
1. A method for predicting a temperature of chilled water, comprising:
acquiring at least two historical chilled water temperature data acquired by a chilled water temperature sensor on refrigeration equipment and historical influence factor data corresponding to the historical chilled water temperature data respectively;
training a model to be trained according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively to determine a chilled water temperature prediction model, wherein the chilled water temperature prediction model is used for predicting chilled water temperature data of the refrigeration equipment.
2. The method according to claim 1, wherein the training a model to be trained according to each historical chilled water temperature data and the historical influence factor data corresponding to each historical chilled water temperature data to determine a chilled water temperature prediction model comprises:
determining a training set and a testing set according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively;
obtaining at least two models to be trained, carrying out model training on the models to be trained according to the training set aiming at each model to be trained so as to determine the trained models, and determining the trained models as candidate models;
determining model precision corresponding to each candidate model according to the test set;
and determining a chilled water temperature prediction model according to the candidate models and the model precision corresponding to each candidate model.
3. The method according to claim 2, wherein the determining a chilled water temperature prediction model according to the model accuracy corresponding to each candidate model and each candidate model comprises:
and determining the candidate model corresponding to the maximum value in the model accuracy as a chilled water temperature prediction model.
4. The method of claim 2, wherein the at least two models to be trained comprise: any two or more of a multivariate nonlinear regression model, a support vector regression model, a neural network model.
5. The method of claim 1, wherein the historical chilled water temperature data comprises: the inlet water temperature of the chilled water and the outlet water temperature of the chilled water;
the historical impact factor data includes: outdoor temperature and refrigerant saturation temperature.
6. The method of claim 5, wherein the refrigerant saturation temperature is determined based on a refrigerant saturation evaporating pressure collected by a pressure sensor on the refrigeration equipment and a preset refrigerant saturation temperature pressure function.
7. The method of any of claims 1 to 6, wherein each of the historical chilled water temperature data does not include historical chilled water temperature data for the refrigeration appliance during preset on and off periods.
8. A failure judgment method for a chilled water temperature sensor is characterized by comprising the following steps:
acquiring current influence factor data corresponding to at least two influence factors corresponding to the chilled water temperature prediction model according to any one of claims 1 to 7, the current chilled water temperature data acquired by the chilled water temperature sensor and the historical influence factor data respectively;
substituting the current influence factor data corresponding to each influence factor into the chilled water temperature prediction model to determine predicted chilled water temperature data;
and judging whether the chilled water temperature sensor fails or not according to the current chilled water temperature data and the predicted chilled water temperature data.
9. The method of claim 8, wherein said determining if the chilled water temperature sensor has failed based on the current chilled water temperature data and the predicted chilled water temperature data comprises:
and when the absolute value of the difference between the current chilled water temperature data and the predicted chilled water temperature data exceeds a threshold value, judging that the chilled water temperature sensor is invalid.
10. A prediction device of a temperature of chilled water, comprising:
the system comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for acquiring at least two historical chilled water temperature data acquired by a chilled water temperature sensor on refrigeration equipment and historical influence factor data corresponding to the historical chilled water temperature data respectively;
and the training module is used for training a model to be trained according to the historical chilled water temperature data and the historical influence factor data corresponding to the historical chilled water temperature data respectively so as to determine a chilled water temperature prediction model, and the chilled water temperature prediction model is used for predicting the chilled water temperature data of the refrigeration equipment.
11. A chilled water temperature sensor failure judgment device, comprising:
the second acquisition module is used for acquiring the chilled water temperature prediction model, the current chilled water temperature data acquired by the chilled water temperature sensor and current influence factor data corresponding to at least two influence factors corresponding to the historical influence factor data;
the prediction module is used for substituting the current influence factor data corresponding to each influence factor into the chilled water temperature prediction model so as to determine the predicted chilled water temperature data;
and the failure judgment module is used for judging whether the chilled water temperature sensor fails or not according to the current chilled water temperature data and the predicted chilled water temperature data.
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