Operation control method and device of temperature adjusting equipment, equipment and storage medium

Abstract

The embodiment of the invention provides an operation control method and device for temperature adjusting equipment, the equipment and a storage medium. The method comprises the steps that a first operation parameter of communication equipment in a communication machine room at first time is acquired; according to the first operation parameter and the determined first constraint condition and second constraint condition, solving the constructed first objective function to obtain a power consumption predicted value of the first temperature adjusting device in the communication machine room; the first target function takes the minimum power consumption predicted value of the first temperature adjusting equipment as a target; the first constraint condition is used for indicating that the power consumption value of the first temperature adjusting equipment is within a first range, and the second constraint condition is used for indicating that at least one parameter in the second operation parameters of the second temperature adjusting equipment is within a corresponding second range; and adjusting the second operation parameter according to the solved power consumption predicted value.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to an operation control method, device, equipment and storage medium for a temperature regulating device. Background Art

[0002] There are often multiple communication devices in the communication room, and these communication devices have certain requirements for the working environment temperature. The requirements of communication equipment for indoor temperature reflect the importance of maintaining stable equipment performance, which is crucial to ensure smooth communication.

[0003] At present, the temperature in the communication room can be adjusted by temperature control equipment (such as air conditioners) in the communication room to keep it within the temperature range required for the normal operation of the communication equipment. However, some temperature control equipment, such as air conditioners, consume electricity, and such temperature control equipment is one of the key energy-consuming equipment in the communication industry room. According to statistics, air conditioners account for 30%-50% of the total power consumption of the communication industry room.

[0004] At present, in order to save the power consumption of temperature control equipment such as air conditioners, air cooling, water cooling and other new energy-saving methods can be used. However, the investment is large and it is not suitable for all environmental conditions. Therefore, facing small and medium-sized computer rooms with energy-saving needs, new energy-saving methods are not suitable. Summary of the invention

[0005] The embodiments of the present application provide an operation control method, device, equipment and storage medium for a temperature regulating device to solve the problem that the prior art method of using air cooling and water cooling to reduce the power consumption of the temperature regulating device in the communication room is costly and thus not suitable for small and medium-sized rooms.

[0006] In a first aspect, an embodiment of the present application provides an operation control method of a temperature adjustment device, the method comprising:

[0007] Obtain the first operating parameters of the communication equipment in the communication room at the first time;

[0008] According to the first operating parameter and the determined first constraint and second constraint, the constructed first objective function is solved to obtain the power consumption prediction value of the first temperature adjustment device in the communication room; wherein the first objective function takes the minimum power consumption prediction value of the first temperature adjustment device as the target, and the first objective function is used to indicate: there is a functional relationship between the input parameter and the power consumption prediction value of the first temperature adjustment device through the power consumption prediction model, and the input parameter includes the first operating parameter and the second operating parameter of the second temperature adjustment device in the communication room; the first constraint is used to indicate that the power consumption value of the first temperature adjustment device is within a first range, and the second constraint is used to indicate that at least one of the second operating parameters of the second temperature adjustment device is within a corresponding second range;

[0009] According to the solved power consumption prediction value, the second operating parameter is adjusted so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold.

[0010] In a second aspect, an embodiment of the present application provides an operation control device for a temperature adjustment device, the device comprising:

[0011] A first acquisition module, used to acquire a first operating parameter of a communication device in a communication room at a first time;

[0012] A solution module, used for solving the constructed first objective function according to the first operating parameter and the determined first constraint condition and second constraint condition, to obtain the power consumption prediction value of the first temperature adjustment device in the communication room; wherein the first objective function takes the minimum power consumption prediction value of the first temperature adjustment device as the target, and the first objective function is used to indicate: there is a functional relationship between the input parameter and the power consumption prediction value of the first temperature adjustment device through the power consumption prediction model, and the input parameter includes the first operating parameter and the second operating parameter of the second temperature adjustment device in the communication room; the first constraint condition is used to indicate that the power consumption value of the first temperature adjustment device is within a first range, and the second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature adjustment device is within a corresponding second range;

[0013] The adjustment control module is used to adjust the second operating parameter according to the solved power consumption prediction value so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a transceiver, and a processor:

[0015] A memory for storing computer programs; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer program in the memory and executing the operation control method of the temperature regulating device described in the first aspect above.

[0016] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the operation control method of the temperature control device described in the first aspect is implemented.

[0017] In an embodiment of the present application, the first operating parameters of the communication equipment in the communication room at the first time can be obtained, so that the constructed first objective function can be solved according to the first operating parameters and the determined first and second constraints to obtain the power consumption prediction value of the first temperature adjustment device in the communication room, and then according to the solved power consumption prediction value, the second operating parameters are adjusted so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to the first threshold value.

[0018] Among them, the first objective function aims to minimize the predicted power consumption value of the first temperature control device, and the first objective function is used to indicate: there is a functional relationship between the input parameters and the predicted power consumption value of the first temperature control device through the power consumption prediction model, and the input parameters include the first operating parameters and the second operating parameters of the second temperature control device in the communication room; the first constraint condition is used to indicate that the power consumption value of the first temperature control device is within the first range, and the second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature control device is within the corresponding second range.

[0019] It can be seen that in the embodiment of the present application, the power consumption prediction value of the first temperature regulating device can be solved to be the minimum, and the power consumption value of the first temperature regulating device is within the first range, and at least one of the second operating parameters of the second temperature regulating device is within the corresponding second range; wherein, the power consumption value of the first temperature regulating device is related to the temperature in the communication room, and if the power consumption value of the first temperature regulating device is within the first range, then the temperature in the communication room is within the temperature range required for the normal operation of the communication equipment. In this way, according to the solved power consumption prediction value, the second operating parameter of the second temperature regulating device is automatically adjusted, so that the power consumption value of the first temperature regulating device can be minimized on the basis of ensuring that the temperature in the communication room is within the temperature range required for the normal operation of the communication equipment and ensuring that the second operating parameter is within a reasonable range. The whole process does not involve additional equipment such as air cooling and water cooling. Therefore, the embodiment of the present application can solve the problem that the prior art uses air cooling and water cooling to reduce the power consumption of the temperature regulating equipment in the communication room, which is costly and thus not suitable for small and medium-sized rooms. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 A flow chart of an operation control method of a temperature regulating device provided in an embodiment of the present application;

[0022] Figure 2 This is a schematic diagram of the structure of the CNN temporal feature input layer in an embodiment of the present application;

[0023] Figure 3 This is a schematic diagram of the CNN convolutional pooling layer structure in the embodiment of the present application;

[0024] Figure 4 This is a schematic diagram of the CNN fully connected layer structure in the embodiment of the present application;

[0025] Figure 5 A schematic diagram of the process of a multi-strategy constrained differential evolution algorithm based on population partitioning in an embodiment of the present application;

[0026] Figure 6 A schematic diagram of a specific implementation of the operation control method of the temperature adjustment device in the embodiment of the present application;

[0027] Figure 7 A structural block diagram of an operation control device for a temperature regulating device provided in an embodiment of the present application;

[0028] Figure 8 A structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the embodiments of the present application, the term "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0030] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The embodiments of the present application provide a method, device, equipment and storage medium for controlling the operation of a temperature regulating device, so as to solve the problem that the prior art uses air cooling and water cooling to reduce the power consumption of the temperature regulating device in the communication room, which is costly and thus not suitable for small and medium-sized rooms.

[0033] Among them, the method and the device are based on the same application concept. Since the method and the device solve the problem in a similar principle, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0034] See also Figure 1 As shown, an embodiment of the present application provides an operation control method of a temperature adjustment device, and the method may include the following steps 101 to 103:

[0035] Step 101: Acquire the first operating parameters of the communication equipment in the communication room at the first time.

[0036] The first operating parameter may include at least one of the following: the current value of the communication device, the temperature of the environment where the communication device is located (ie, inside the communication room), and the temperature outside the communication room.

[0037] The communication equipment may include at least one of the following: A device (i.e., access layer device), B device (i.e., aggregation layer device), optical line terminal (OLT), broadband access server (BRAS), wavelength division equipment, indoor baseband processing unit (BBU), remote radio unit (RRU), and switch.

[0038] In addition, the first time may be the current time.

[0039] Step 102: Solve the constructed first objective function according to the first operating parameter and the determined first constraint condition and second constraint condition to obtain a predicted power consumption value of the first temperature adjustment device in the communication room.

[0040] Among them, the first objective function aims to minimize the predicted power consumption value of the first temperature control device, and the first objective function is used to indicate: there is a functional relationship between the input parameters and the predicted power consumption value of the first temperature control device through a power consumption prediction model, and the input parameters include the first operating parameters and the second operating parameters of the second temperature control device in the communication room; the first constraint condition is used to indicate that the power consumption value of the first temperature control device is within a first range, and the second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature control device is within a corresponding second range.

[0041] It can be seen that in the embodiment of the present application, through the first constraint condition, in the process of solving the minimum power consumption prediction value of the first temperature control device, the power consumption value of the first temperature control device can be made within a reasonable range; through the second constraint condition, in the process of solving the minimum power consumption prediction value of the first temperature control device, at least one parameter of the second operating parameters of the second temperature control device can be made within a reasonable range to ensure the normal operation of the second temperature control device.

[0042] In addition, the first temperature regulating device may be an air conditioner; the second temperature regulating device may include at least the first temperature regulating device, or the second temperature regulating device may include a temperature regulating device other than the first temperature regulating device; for example, the second temperature regulating device may include at least one of an air conditioner, a computer room fan, and an air duct switch. In this case, correspondingly, the second operating parameter may include at least one of the following: air conditioner setting value, computer room fan speed, and air duct switch opening degree.

[0043] It can be understood that, for each device included in the second temperature regulating device, at least one parameter may correspond to the second operating parameters.

[0044] It should be noted that the power consumption value of the first temperature regulating device is related to the temperature in the communication room, and the first range is determined based on the temperature range required for the normal operation of the communication equipment in the communication room. In this way, when the power consumption value of the first temperature regulating device is in the first range, it means that the temperature in the communication room is within the temperature range required for the normal operation of the communication equipment.

[0045] In addition, in an embodiment of the present application, the input content of the above-mentioned power consumption prediction model includes: the first operating parameter of the first temperature regulating device, the second operating parameter of the second temperature regulating device, and the output content includes: the power consumption prediction value of the first temperature regulating device. Then the power consumption prediction model can be obtained by training in advance through training samples, and the training samples may include: the first operating parameter of the first temperature regulating device, the second operating parameter of the second temperature regulating device, and the actual power consumption value of the first temperature regulating device. It can be understood that the process of training the power consumption prediction model based on the training samples can adopt the training method in the existing related technology, which will not be repeated here.

[0046] Step 103: According to the solved power consumption prediction value, adjust the second operating parameter so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold.

[0047] Among them, if the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to the first threshold, it means that the power consumption value of the first temperature adjustment device at the second time is close to the power consumption prediction value, or can be approximately equal to the power consumption prediction value.

[0048] It can be seen from the above steps 101 to 103 that in the embodiment of the present application, the first operating parameters of the communication equipment in the communication room at the first time can be obtained, so that according to the first operating parameters and the first constraints and the second constraints that have been determined, the constructed first objective function is solved to obtain the power consumption prediction value of the first temperature control device in the communication room, and then according to the solved power consumption prediction value, the second operating parameters are adjusted so that the difference between the power consumption value of the first temperature control device at the second time and the solved power consumption prediction value is less than or equal to the first threshold value.

[0049] Among them, the first objective function aims to minimize the predicted power consumption value of the first temperature control device, and the first objective function is used to indicate: there is a functional relationship between the input parameters and the predicted power consumption value of the first temperature control device through the power consumption prediction model, and the input parameters include the first operating parameters and the second operating parameters of the second temperature control device in the communication room; the first constraint condition is used to indicate that the power consumption value of the first temperature control device is within the first range, and the second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature control device is within the corresponding second range.

[0050] It can be seen that in the embodiment of the present application, the power consumption prediction value of the first temperature regulating device can be solved to be the minimum, and the power consumption value of the first temperature regulating device is within the first range, and at least one of the second operating parameters of the second temperature regulating device is within the corresponding second range; wherein, the power consumption value of the first temperature regulating device is related to the temperature in the communication room, and if the power consumption value of the first temperature regulating device is within the first range, then the temperature in the communication room is within the temperature range required for the normal operation of the communication equipment. In this way, according to the solved power consumption prediction value, the second operating parameter of the second temperature regulating device is automatically adjusted, so that the power consumption value of the first temperature regulating device can be minimized on the basis of ensuring that the temperature in the communication room is within the temperature range required for the normal operation of the communication equipment and ensuring that the second operating parameter is within a reasonable range. The whole process does not involve additional equipment such as air cooling and water cooling. Therefore, the embodiment of the present application can solve the problem that the prior art uses air cooling and water cooling to reduce the power consumption of the temperature regulating equipment in the communication room, which is costly and thus not suitable for small and medium-sized rooms.

[0051] Optionally, the power consumption prediction model is a convolutional neural network (CNN) model. It is understandable that the power consumption prediction model may also adopt other types of deep learning models, which will not be described in detail here.

[0052] Among them, CNN is a deep learning model, which is widely used in the fields of image and video recognition, recommendation system and natural language processing. In the embodiment of the present application, the CNN model is used to predict the power consumption value of the first temperature adjustment device.

[0053] In order to understand the specific calculation process involved in the CNN model, the following example is given:

[0054] First, the input content of the CNN model includes the first operating parameter and the second operating parameter. For example, the first operating parameter includes the current value x of 8 communication devices, the indoor temperature t of the communication room, and the outdoor temperature d of the communication room. The second operating parameter includes the air conditioning setting value r1, the fan speed r1 of the room, and the air duct switch r1. Figure 2 As shown, the values ​​of the above parameters at the current time k can be collected; then the input data of the CNN model can be expressed as follows:

[0055]

[0056] Secondly, the CNN model includes convolutional layer, pooling layer, fully connected layer, and integrated output layer. The following is a detailed introduction to these layers:

[0057] 1. Convolutional layer:

[0058] The convolution layer extracts features by perceiving the local features of the data through the convolution kernel. In this embodiment of the application, a convolution kernel with a dimension of h×1 and a moving step of 1 may be used, indicating that each time the convolution kernel rolls in a one-step cycle. The specific formula is: z =σ relu (ω z *A+b z ); where z = 1, 2, 3 ... n, n represents the number of convolution kernels; A is the input data of the CNN model. It can be seen that the input data and the convolution kernel ω z Perform convolution calculation and combine with bias b z Finally, using the activation function σ relu (.) Activate the calculated data, and the activated u z The data will enter the pooling layer for the next calculation.

[0059] in,

[0060] ω z It means that the z-th dimension is h×1 convolution kernel vector, and the value range of z is 1 to n, indicating that there are n different convolution kernels to extract different features.

[0061] In addition, as mentioned above, the dimension of A is 13×1, ω z The dimension of is h×1, then ω z *The dimension of A is (13-h+1)×(1-1+1), that is, (12-h)×1; then b z is the bias vector corresponding to the zth convolution kernel, which also has n different biases, b z The dimension is (12-h)×1, which is the same as ω z *A dimensions are the same.

[0062] In addition, the above activation function can be relu (x) = max(0, x).

[0063] 2. About the pooling layer:

[0064] The pooling layer is used for feature dimensionality reduction, which has the functions of compressing data volume, reducing parameters, reducing overfitting, and improving model fault tolerance. The specific structure is as follows Figure 3 shown.

[0065] Among them, the pooling layer adopts the average pooling method, and the specific formula is as follows:

[0066]

[0067] x τ Represents the τth pooled data (i.e., the output of the convolutional layer u z part of), τ takes a value of 1 to k, k represents the number of pooled data (ie, k = n); it should be noted that the embodiment of the present application can adopt a one-dimensional pooling calculation method, so k can also be expressed as the size of the pooling window, which is k×1.

[0068] 3. About the fully connected layer:

[0069] The fully connected layer plays the role of a "classifier" in the entire CNN network. The output data of the pooling layer is feature-weighted. The specific structure can be as follows Figure 4 shown.

[0070] Among them, the relevant formula of the fully connected layer is as follows:

[0071] Y f =σ relu (w f *F+b f )

[0072] F represents the input data of the fully connected layer (i.e., the output data of the pooling layer), w f represents the weight of the neuron in the fth fully connected layer, b f Represents the bias of the f-th fully connected layer neuron, and the value of f ranges from 1 to n.

[0073] 4. About the integrated output layer:

[0074] Integrate the data output by the fully connected layer to obtain the power consumption prediction value. The specific formula is as follows:

[0075]

[0076] Among them, Y f Represents the data output by the fully connected layer, y represents the power consumption prediction value output by the CNN model, and the degree of closeness between y and the true value can reflect the accuracy of the prediction model.

[0077] From the above, it can be seen that the power consumption prediction model in this embodiment can be expressed as:

[0078] y=f(x1, x2, x3, x4, x5, x6, x7, x8, r1, r2, r3, t, d).

[0079] In addition, as mentioned above, the first constraint condition is used to indicate that the power consumption value of the first temperature adjustment device is within a first range. In the above example, the first constraint condition can be expressed as: Indicates the lower and upper limits of the first range;

[0080] The second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature adjustment device is within a corresponding second range. In the above example, the second constraint condition can be expressed as:

[0081]

[0082] Indicates the lower limit and upper limit of the second range corresponding to r1 (for example It can be 16℃, can be 28°C);

[0083] Indicates the lower limit and upper limit of the second range corresponding to r2;

[0084] Indicates the lower limit and upper limit of the second range corresponding to r3.

[0085] Correspondingly, based on the above example, the first objective function and its constraints in the embodiment of the present application can be expressed as follows:

[0086]

[0087] Optionally, in the above step 102, solving the constructed first objective function according to the first operating parameter and the determined first constraint condition and second constraint condition to obtain the power consumption prediction value of the first temperature adjustment device in the communication room includes the following step A-1:

[0088] Step A-1: ​​Using a multi-strategy constrained differential evolution algorithm based on population partitioning, according to the first operating parameters and the first constraint condition and the second constraint condition, the first objective function is solved to obtain a predicted power consumption value of the first temperature control device in the communication room.

[0089] Therefore, in the embodiment of the present application, when solving the first objective function, a multi-strategy constrained differential evolution algorithm based on population partitioning can be used. Among them, the multi-strategy constrained differential evolution algorithm based on population partitioning is an efficient optimization algorithm that combines multiple mutation and crossover strategies to solve single-objective optimization problems. This algorithm is based on the random optimization technology of population optimization and has the advantages of simple structure, easy implementation, and strong robustness.

[0090] It is understandable that, for solving single-objective optimization problems based on constraint conditions, other algorithms in existing related technologies may also be used, which are not listed here one by one.

[0091] Optionally, in step A-1, the multi-strategy constrained differential evolution algorithm based on population partition is used to solve the first objective function according to the first operating parameter and the first constraint condition and the second constraint condition to obtain the power consumption prediction value of the first temperature adjustment device in the communication room, including the following steps A-1.1 to A-1.13:

[0092] Step A-1.1: Initializing the second operating parameter to obtain an initial population (optionally, the second operating parameter is initialized according to the second constraint condition, that is, the second operating parameter within the second range indicated by the second constraint condition can be randomly generated);

[0093] Step A-1.2: when v is an integer from 1 to V, the first operating parameter and the second operating parameter corresponding to the vth individual in the initial population are input into the power consumption prediction model to obtain the fitness of the vth individual, where V represents the number of individuals in the initial population;

[0094] Step A-1.3: Divide the initial population into a dominant population and a disadvantaged population according to the obtained fitness of the 1st to Vth individuals;

[0095] Step A-1.4: using a superior mutation strategy to perform a mutation operation on the superior population to obtain a first population to be processed, and using a inferior mutation strategy to perform a mutation operation on the inferior population to obtain a second population to be processed;

[0096] Step A-1.5: performing a crossover operation on the dominant population and the first population to be processed to obtain a third population to be processed, and performing a crossover operation on the inferior population and the second population to be processed to obtain a fourth population to be processed;

[0097] Step A-1.6: According to the first constraint condition and the second constraint condition, adjusting the second operating parameter and the fitness corresponding to each individual in the third population to be processed to obtain a fifth population to be processed;

[0098] Step A-1.7: According to the first constraint condition and the second constraint condition, adjusting the second operating parameter and the fitness corresponding to each individual in the fourth population to be processed to obtain a sixth population to be processed;

[0099] Step A-1.8: Determine the offspring population corresponding to the dominant population according to the fitness of each individual in the dominant population and the fifth population to be processed;

[0100] Step A-1.9: Determine the offspring population corresponding to the inferior population according to the fitness of each individual in the inferior population and the sixth population to be processed;

[0101] Step A-1.10: Let x = 2, and determine whether x is less than the maximum number of iterations;

[0102] Step A-1.11: When x is less than the maximum number of iterations, the offspring population corresponding to the dominant population and the offspring population corresponding to the inferior population are merged, and the merged population is re-divided into a dominant population and an inferior population, and the process returns to step A-1.4 (i.e., the step of using a dominant mutation strategy to mutate the dominant population to obtain a first population to be processed, and using an inferior mutation strategy to mutate the inferior population to obtain a second population to be processed);

[0103] Step A-1.12: Let x=x+1, and repeat the step of determining whether x is less than the maximum number of iterations;

[0104] Step A-1.13: When x is equal to the maximum number of iterations, obtain the minimum fitness among the fitness of each individual of the child population corresponding to the dominant population, and determine the minimum fitness as the power consumption prediction value of the first temperature control device in the communication room.

[0105] To facilitate understanding of the above steps A-1.1 to A-1.13, the following examples are given:

[0106] For example, the first operating parameters include x1, x2, x3, x4, x5, x6, x7, x8, t, d mentioned above; the second operating parameters include r1, r2, r3 mentioned above, and the number of the initialized population is 10 (it should be noted that the number of the initialized population here is only an example, and can also be other values, which can be determined according to actual conditions);

[0107] Then, the execution process is as follows Figure 5 As shown, the process includes the following:

[0108] Perform the first iteration:

[0109] Initialize the population, that is, initialize the second operating parameters (for example, randomly generate 10 values ​​for each second operating parameter, which are used as the second operating parameters corresponding to each individual in the initial population), so that each individual in the initial population has a corresponding value of r1, r2, and r3;

[0110] Among them, the values ​​of x1, x2, x3, x4, x5, x6, x7, x8, t, d collected at the current time, and the values ​​of r1, r2, r3 corresponding to each individual in the initialized population can be input into the power consumption prediction model described above, and the power consumption prediction value corresponding to each individual can be output, so that the power consumption prediction value can be used as the fitness of the individual.

[0111] Then, according to the fitness of the 10 individuals in the initialization population, the 10 individuals can be divided into a dominant population and a disadvantaged population. For example, the dominant population includes 7 individuals and the disadvantaged population includes 3 individuals. After that, the 7 individuals in the dominant population are sequentially subjected to mutation operation, crossover operation, and constraint processing (i.e., processed according to the first constraint condition and the second constraint condition), and the 3 individuals in the disadvantaged population are sequentially subjected to mutation operation, crossover operation, and constraint processing.

[0112] Then, a selection operation is performed, that is, based on the dominant population and the population obtained by performing mutation, crossover and constraint processing on the dominant population, the offspring population of the dominant population is further obtained; based on the inferior population and the population obtained by performing mutation, crossover and constraint processing on the inferior population, the offspring population of the inferior population is further obtained;

[0113] At this point, the first iteration is completed.

[0114] After that, the second iteration is performed:

[0115] The two offspring populations obtained in the first iteration are merged, and the merged population is re-divided into a dominant population and an inferior population. Thereafter, the processing process of the newly obtained dominant population and inferior population is the same as that of the first iteration, which will not be repeated here.

[0116] After the last iteration is completed, the minimum fitness is selected from the fitness of each individual of the offspring population corresponding to the dominant population obtained in the last iteration, and the minimum fitness is the power consumption prediction value obtained by solving.

[0117] It should be noted that the purpose of mutation operation is to expand the search range of the population in the solution space, thereby increasing the probability of the algorithm converging to the global optimal solution; the purpose of crossover operation is to retain the good genes in the parent individuals and further ensure the overall convergence of the algorithm.

[0118] It can be seen that the population partition multi-strategy constrained differential evolution algorithm (PD-MSCDE) used in the embodiment of the present application is based on classical differential evolution, which initializes the population, adaptively generates two sub-populations, and the two sub-populations enter the next generation through mutation, crossover and selection operations, and finally reallocates the two sub-populations until the maximum number of iterations is reached or other termination conditions are met.

[0119] In the iterative process, the population is initially divided into two sub-populations, each co-evolving. The dominant population uses dominant individuals to find better individuals, and the dominant population will gradually increase with the number of iterations; the inferior population is used to explore unknown areas in the solution space to enhance population diversity, and the number of inferior populations is large at the beginning of the iteration, which can increase the algorithm solution selection space, thereby avoiding falling into the local optimum.

[0120] In addition, in the embodiments of the present application, when performing mutation operations, different mutation strategies are used for the dominant population and the inferior population, respectively, wherein the dominant mutation strategy can accelerate algorithm convergence, and the inferior mutation strategy can expand the solution space.

[0121] Optionally, in step A-1.3, the individuals in the initial population may be sorted in order of fitness from small to large, and then according to the first number of individuals in the dominant population, the first number of individuals in the sorting may be selected to form a dominant population, and the remaining individuals may form a disadvantaged population; wherein, in a certain iteration process, the ratio of the number of individuals in the dominant population to the number of individuals in the disadvantaged population in this iteration process may be calculated according to the following formula, and then the number of each individual included in the dominant population may be calculated: Among them, ITE represents the current iteration number, ITE MAX Indicates the maximum number of iterations.

[0122] Optionally, in the above step A-1.4, the second operating parameter includes N parameters; and the step of using the dominant mutation strategy to perform a mutation operation on the dominant population to obtain the first population to be processed includes:

[0123] Selecting the first individual with the smallest fitness in the dominant population;

[0124] When v is any integer from 1 to V, the following process is performed to obtain the first to-be-processed population including V individuals:

[0125] Randomly selecting a second individual and a third individual from the individuals other than the first individual in the dominant population;

[0126] When i is an integer from 1 to N, the i-th parameter r of the first individual 1i , the i-th parameter r of the second individual 2i, the i-th parameter r of the third individual 3i , substitute into the first formula, and obtain the i-th parameter r of an individual in the first population to be processed 待1i ;

[0127] Among them, the first formula is: 待1i =r 1i +F1(r 2i -r 3i ), F1 represents the scaling factor.

[0128] In addition, the selection of F1 is generally between [0, 2], usually F1 = 0.5. If the population converges too early, you can increase the value of F1 or increase the number of individuals in the population.

[0129] In order to facilitate understanding of the specific mutation operation process of the dominant population in step A-1.4, the following example is given with the dominant population consisting of 7 individuals:

[0130] Each individual in the dominant population has its corresponding fitness, so the individual with the smallest fitness can be selected; then, two individuals are randomly selected from the individuals in the dominant population except the individual with the smallest fitness, and then the same second operating parameters of these three individuals are substituted into the above first formula, then a new second operating parameter can be obtained, and the new second operating parameter is a mutated individual; this process is repeated 7 times, and 7 mutated individuals can be obtained, thereby completing the mutation operation on the dominant population.

[0131] It can be understood that if there are multiple individuals with the smallest fitness in the dominant population, one of the multiple individuals with the smallest fitness can be randomly selected as the first individual.

[0132] Optionally, in the above step A-1.4, the second operating parameter includes N parameters; and the step of using the inferior mutation strategy to perform a mutation operation on the inferior population to obtain a second population to be processed includes:

[0133] When v is an integer from 1 to V, the following process is performed to obtain the second to-be-processed population including V individuals:

[0134] Randomly selecting a fourth individual, a fifth individual, and a sixth individual from the inferior population;

[0135] When i is an integer from 1 to N, the i-th parameter r of the fourth individual 4i , the i-th parameter r of the fifth individual 5i , the i-th parameter r of the sixth individual 6i , substitute into the second formula, and obtain the i-th parameter r of an individual in the second population to be processed 待2i ;

[0136] Wherein, the second formula is: 待2i =r 4i +F2(r 5i -r 6i ), F2 represents the scaling factor.

[0137] In addition, the selection of F2 is generally between [0, 2], usually F2 = 0.5. If the population converges too early, you can increase the value of F2 or increase the number of individuals in the population.

[0138] In order to facilitate understanding of the specific mutation operation process of the inferior population in step A-1.4, an example is given below using the inferior population consisting of 3 individuals:

[0139] Each individual in the disadvantaged population has its corresponding fitness. Three individuals are randomly selected from the disadvantaged population, and then the same second operating parameter of these three individuals is substituted into the above second formula to obtain a new second operating parameter, which is a mutated individual. This process is repeated three times to obtain three mutated individuals, thereby completing the mutation operation on the disadvantaged population.

[0140] Optionally, in the above step A-1.5, the step of performing a crossover operation on the dominant population and the first population to be processed to obtain a third population to be processed comprises:

[0141] According to the second operating parameters of each individual in the dominant population, a first matrix is ​​obtained (that is, the second operating parameters of each individual in the dominant population are used as the values ​​in the first matrix, so that the element r in the vth row and the ith column of the first matrix v,i , then it represents the i-th parameter in the second operating parameter corresponding to the v-th individual in the dominant population);

[0142] According to the second operating parameters of each individual in the first to-be-processed population, a second matrix is ​​obtained (that is, the second operating parameters of each individual in the first to-be-processed population are used as the values ​​in the second matrix, so that the element r′ in the vth row and the ith column of the second matrix v,i , then it represents the i-th parameter in the second operating parameter corresponding to the v-th individual in the first population to be processed);

[0143] According to the fifth formula and the first matrix and the second matrix, a third matrix is ​​obtained, wherein the fifth formula is: r″ v,i represents the element in the vth row and ith column of the third matrix, rand v,i represents the random number corresponding to the position of the vth row and the ith column of the first matrix or the second matrix, C r represents the crossover probability;

[0144] Elements in the same row of the third matrix are used as different parameters in the second operating parameters corresponding to the same individual, and these parameters constitute the third population to be processed.

[0145] Among them, rand v,i Can be a random number between 0 and 1, C r It can be a probability between 0 and 1.

[0146] In order to facilitate understanding of the specific process of the crossover operation here, an example is given below in which the dominant population includes 7 individuals and the second operating parameters include r1, r2, and r3:

[0147] The values ​​of the three second operating parameters of the seven individuals of the dominant population can obtain a 7×3 first matrix; similarly, the values ​​of the three second operating parameters of the seven individuals of the first to-be-processed population can obtain a 7×3 second matrix; 21 random numbers are generated. For the first random number, if it is less than the predetermined crossover probability C r , then select the first element in the second matrix as the first element in the third matrix, otherwise select the first element in the first matrix as the first element in the third matrix; and so on, the process is repeated to obtain a third matrix, and the three values ​​in the same row of the third matrix belong to the values ​​of r1, r2, and r3 of the same individual. In this way, the third population to be processed can be obtained based on the third matrix.

[0148] It should be noted that the serial numbers of the elements in the first matrix, the second matrix and the third matrix are determined in the order of increasing row numbers and column numbers.

[0149] It can be understood that the specific process of performing a crossover operation on the inferior population and the second population to be processed to obtain the fourth population to be processed is similar to the specific process of performing a crossover operation on the superior population and the first population to be processed to obtain the third population to be processed, and will not be repeated here.

[0150] Optionally, in the above step A-1.6, adjusting the second operating parameter and the fitness corresponding to each individual in the third population to be processed according to the first constraint condition and the second constraint condition to obtain a fifth population to be processed includes at least one of the following:

[0151] In the case where there is a seventh individual in the third population to be processed, setting the fitness of the seventh individual to be a first preset value, wherein the fitness of the seventh individual is outside the first range and is less than a second threshold, and the difference between the first preset value and the upper limit of the first range is greater than a third threshold;

[0152] When there is an eighth individual in the third population to be processed, the parameters of the eighth individual that are outside the second range are adjusted to within the second range, wherein at least one of the second operating parameters of the eighth individual is outside the corresponding second range.

[0153] It should be noted that if the fitness of the seventh individual is outside the first range and less than the second threshold, it means that the fitness of the seventh individual is too small and unreasonable. In this way, the fitness of the seventh individual can be set larger. In this way, in the subsequent process of determining the offspring population, the probability of the seventh individual being selected will be greatly reduced, thereby avoiding wrong solutions.

[0154] It can be seen from this that by constraining the third and fourth populations to be processed through the first and second constraints, the fitness of the individuals and the second operating parameters therein can be adjusted to a reasonable range, so that the power consumption value of the first temperature control device is within a reasonable range (so that the temperature in the communication room can be within the range required for the normal operation of the communication equipment), and the second temperature control device can be guaranteed to work normally.

[0155] Optionally, in the above step A-1.8, determining the offspring population corresponding to the dominant population according to the fitness of each individual in the dominant population and the fifth population to be processed includes:

[0156] When v is an integer from 1 to V, the following process is performed to obtain the offspring population corresponding to the dominant population including V individuals:

[0157] Comparing the first fitness of the vth individual in the dominant population with the second fitness of the vth individual in the fifth population to be processed;

[0158] When the first fitness is less than the second fitness, taking the vth individual in the dominant population as the vth individual in the offspring population corresponding to the dominant population;

[0159] When the first fitness is greater than the second fitness, taking the vth individual in the fifth population to be processed as the vth individual in the offspring population corresponding to the dominant population;

[0160] When the first fitness is equal to the second fitness, one of the vth individual in the dominant population and the vth individual in the fifth population to be processed is randomly selected as the vth individual in the offspring population corresponding to the dominant population.

[0161] From this, we can see that when obtaining the offspring population of the dominant population based on the dominant population and the fifth population to be processed (i.e., the population obtained after mutation, crossover, and constraint processing of the dominant population), the fitness of two individuals with the same serial number in the two populations can be compared, and the individual with smaller fitness can be retained. If the two are the same, one of them is randomly selected to be retained. In this way, the retained individuals constitute the offspring individuals of the dominant population.

[0162] It can be understood that the specific process of determining the offspring population corresponding to the inferior population based on the fitness of each individual in the inferior population and the sixth population to be processed is similar to the process of obtaining the offspring individuals corresponding to the dominant population, and will not be repeated here.

[0163] Optionally, in the above step 103, adjusting the second operating parameter according to the solved power consumption prediction value so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold value includes:

[0164] According to the target type of the network usage time period of the communication room to which the current time belongs, a predictive control method is adopted to adjust the second operating parameter according to the power consumption prediction value obtained by solving, so that the difference between the power consumption value of the first temperature control device at the second time and the power consumption prediction value obtained by solving is less than or equal to the first threshold.

[0165] Among them, due to the particularity of the communication room environment, there are many types of main equipment, different working conditions of each device in different time periods, and different distribution of ordinary users and dedicated line users carried by the room. Therefore, the types of network time periods in the communication room can be divided, so that based on different types, the predictive control method can be used to control the second operating parameters of the second temperature adjustment device.

[0166] For example, the period from 24:00 to 8:00 the next day is usually the low point of network usage for ordinary users, and other time periods are peak time periods. Based on this, the types of network usage time periods of the communication room can be divided.

[0167] Optionally, the step of using a predictive control method according to the target type of the network usage time period of the communication room to which the current time belongs, and adjusting the second operating parameter according to the solved power consumption prediction value, so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold, includes:

[0168] When the target type is the first predetermined type, solving the second target function to obtain the value to which the second operating parameter needs to be adjusted in step P, and adjusting the second operating parameter to the value corresponding to step j when the time corresponding to step j arrives;

[0169] Where j is an integer from 1 to P, and the time interval between two adjacent steps is a fixed value;

[0170] The second objective function aims to minimize the first objective value O1, and the first objective value is determined by a third formula;

[0171] The third formula is:

[0172] Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained;

[0173] h j represents the error compensation coefficient corresponding to the jth step;

[0174] e(k)=Y(k)-Y M (k), Y(k) represents the actual power consumption value of the first temperature adjustment device at the first time k, and Y M (k) represents the first operating parameter collected at the first time k and the second operating parameter collected are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the first time k is obtained;

[0175] Y R (k+j)=α j Y(k)+(1-α j )Y * , Y * represents the power consumption prediction value obtained by solving the first objective function, α j Represents the smoothness coefficient of the jth step.

[0176] Optionally, the first predetermined type is network usage low point.

[0177] The third formula is introduced as follows:

[0178] First, about Y R (k+j):

[0179] In the embodiment of the present application, the first objective function is solved to obtain the power consumption prediction value Y * Afterwards, a path from the actual power consumption value Y(k) at the first time k to Y * The generation rules are as follows:

[0180] The reference trajectory curve at time k can be obtained from the ideal optimization value Y in the next j steps. R(k+j), j=

[0181] 1,2,...P. The first-order exponential change can be used to express the future trend, and we can get: Y R (k+j)=α j Y(k)+(1-α j )Y * ; then [Y R (k+1),Y R (k+2),……,Y R (k+P)] is the reference trajectory.

[0182] α j is the smoothness coefficient, ranging from 0 to 1. j The smaller it is, the faster the reference trajectory can reach Y * α j The smaller the value, the greater the system adjustment burden and the more likely it is to cause production instability. j The larger the value, the lower the system adjustment burden, but it will increase the adjustment time and easily cause resource waste. j The selection of the range is crucial.

[0183] Second, regarding Y M (k+j)+h j e(k):

[0184] Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained; e(k) represents the difference between the actual power consumption value at the first time k and the power consumption prediction value at the first time k. It can be seen that in the embodiment of the present application, the error between the actual power consumption value and the power consumption prediction value can be compensated or corrected based on the power consumption prediction value corresponding to each step. In this way, Y M (k+j)+h j e(k) represents the corrected power consumption value corresponding to the jth step;

[0185] From the first and second aspects, we can see that Y M (k+j)+h j e(k)-Y R (k+j) represents the difference between the corrected power consumption value corresponding to step j and the ideal optimization value of step j. It can be seen that O1 represents the difference between the corrected power consumption value from step 1 to step P and the ideal optimization value. MThe calculation process of (k+j) involves the second operating parameters corresponding to each step. By solving the above second objective function with O1 minimum as the goal, the specific values ​​of the second operating parameters corresponding to each step when O1 is minimum can be obtained.

[0186] It is understandable that the multi-strategy constrained differential evolution algorithm based on population division described above can also be used to solve the second objective function. The specific solution process is similar to the solution process of the first objective function, which will not be repeated here.

[0187] Optionally, the step of using a predictive control method according to the target type of the network usage time period of the communication room to which the current time belongs, and adjusting the second operating parameter according to the solved power consumption prediction value, so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold, includes:

[0188] When the target type is the second predetermined type, solving the third target function to obtain the value to which the second operating parameter needs to be adjusted in step P, and adjusting the second operating parameter to the value corresponding to step j when the time corresponding to step j arrives;

[0189] Where j is an integer from 1 to P, and the time interval between two adjacent steps is a fixed value;

[0190] The third objective function aims to minimize the second objective value O2, and the second objective value is determined by a fourth formula;

[0191] The fourth formula is:

[0192] Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained;

[0193] h j represents the error compensation coefficient corresponding to the jth step;

[0194] e(k)=Y(k)-Y M (k), Y(k) represents the actual power consumption value of the first temperature adjustment device at the first time k, and Y M (k) represents the first operating parameter collected at the first time k and the second operating parameter collected are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the first time k is obtained;

[0195] YT (k+j)=α j Y(k)+(1-α j )Y * , Y * represents the power consumption prediction value obtained by solving the first objective function, α j represents the smoothness coefficient of the jth step;

[0196] N represents the number of parameters included in the second operating parameters, C i represents the weight coefficient of the i-th parameter in the second operating parameter, r i (k+j+1) represents the value of the i-th parameter in the second operating parameter corresponding to the j+1-th step, r i (k+j) represents the value of the i-th parameter in the second operating parameters corresponding to the j-th step.

[0197] Among them, q j With C i And the ratio of dedicated line users in the computer room to total users, q j With C i The choice needs to be considered comprehensively.

[0198] Optionally, the second scheduled type is a network usage peak.

[0199] The fourth formula is introduced as follows:

[0200] Third, regarding Y M (k+j)+h j e(k)-Y R (k+j), please refer to the first and second aspects mentioned above for details, which will not be repeated here.

[0201] Fourthly, regarding r i (k+j+1)-r i (k+j):

[0202] r i (k+j+1) represents the value of the i-th parameter in the second operating parameter corresponding to the j+1-th step, r i (k+j) represents the value of the i-th parameter in the second operating parameter corresponding to the j-th step. It can be seen that r i (k+j+1)-r i (k+j) represents the difference between the i-th parameters corresponding to two adjacent steps, then It represents the fluctuation degree of all the second operating parameters between every two adjacent steps from step 1 to step P.

[0203] Among them, Y MThe calculation process of (k+j) involves the second operating parameter corresponding to each step, and This content also includes the second operating parameters corresponding to each step. By solving the above-mentioned third objective function with the minimum O2 as the goal, the specific values ​​of the second operating parameters corresponding to each step when O2 is minimum can be obtained.

[0204] It is understandable that the multi-strategy constrained differential evolution algorithm based on population division described above can also be used to solve the third objective function. The specific solution process is similar to the solution process of the first objective function, which will not be repeated here.

[0205] It should also be noted that during the time period corresponding to the peak network usage, the computer room environment should not change too much, so it is necessary to ensure that the second operating parameter is as small as possible in each step, while during the time period corresponding to the trough network usage, there is no need to consider the stability of the computer room, so the second operating parameter can be adjusted to a larger value. Therefore, when the target type is the trough network usage, O1 of the second objective function can only include This item; when the target type is the peak network usage, O2 of the third objective function needs to include as well as These two items.

[0206] In summary, the specific implementation of the operation control method of the temperature adjustment device of the embodiment of the present application can be described as follows:

[0207] Preparation stage:

[0208] 1. Construct a power consumption prediction model. The process of training the power consumption prediction model can be as described above and will not be repeated here.

[0209] 2. Constructing constraint conditions (i.e., the first constraint condition and the second constraint condition mentioned above), wherein the specific contents of the constraint conditions are as mentioned above and will not be repeated here;

[0210] 3. Construct a first objective function, wherein the specific introduction of the first objective function is as described above and will not be repeated here.

[0211] Operation control phase, such as Figure 6 As shown, the process includes the following:

[0212] Collect the first operating parameter of the first temperature adjustment device at the current time, and solve the first objective function based on the first operating parameter and the first constraint condition and the second constraint condition to obtain the power consumption prediction value Y of the second temperature adjustment device * ;

[0213] Based on Y * , determine the reference trajectory [YR (k+1), Y R (k+2), ..., Y R (k+P)], wherein the specific method for determining the reference trajectory can be found in the above description and will not be repeated here;

[0214] Determine the calculation expression for feedback correction: Y M (k+j)+h j e(k);

[0215] Determine the calculation expression for the difference between two adjacent steps of the same parameter in the second operation parameter: r i (k+j+1)-r i (k+j);

[0216] Based on the reference trajectory, the calculation expression of feedback correction, and the calculation expression of the difference between two adjacent steps of the same parameter in the second operating parameter, the calculation formula of the first target value O1 and the second target value O2 is obtained;

[0217] Classification control, that is, when the current time is the off-peak time of network usage, solve the second objective function with the minimum first objective value as the goal, obtain the value to which the second operating parameter needs to be adjusted at each step, and then control the second operating parameter of the second temperature adjustment device to be gradually adjusted to the corresponding value; when the current time is the peak time of network usage, solve the third objective function with the minimum second objective value as the goal, obtain the value to which the second operating parameter needs to be adjusted at each step, and then control the second operating parameter of the second temperature adjustment device to be gradually adjusted to the corresponding value.

[0218] In summary, in the embodiment of the present application, by establishing a power consumption prediction model, the first operating parameter of the first temperature regulating device and the second operating parameter of the second temperature regulating device are determined to construct an input layer (that is, by establishing a power consumption prediction model, complex physical deductions are avoided, and variables related to the power consumption of the first temperature regulating device are determined). According to the established power consumption model, the first objective function is constructed with the goal of minimizing the power consumption of the first temperature regulating device, and the power consumption range of the first temperature regulating device and the range of the second operating parameter of the second temperature regulating device are constrained. The PD-MSCDE algorithm is used to solve the first objective function to obtain a Pareto front surface that meets the conditions. Furthermore, a time-sensitive steady-state operation optimization algorithm based on a communication room is established, which distinguishes different types of network usage time periods in the communication room, and designs optimization objective functions respectively to improve system stability, and can enable the room to quickly and stably approach the ideal value while reducing the power consumption of the first temperature regulating device, thereby increasing robustness.

[0219] The above introduces the operation control method of the temperature regulating device provided in the embodiment of the present application. The following will introduce the operation control device of the temperature regulating device provided in the embodiment of the present application in conjunction with the accompanying drawings.

[0220] See also Figure 7 The embodiment of the present application further provides an operation control device for a temperature adjustment device, and the operation control device 700 for the temperature adjustment device includes:

[0221] A first acquisition module 701 is used to acquire a first operating parameter of a communication device in a communication room at a first time;

[0222] A solution module 702 is used to solve the constructed first objective function according to the first operating parameter and the determined first constraint condition and second constraint condition, so as to obtain the power consumption prediction value of the first temperature adjustment device in the communication room; wherein the first objective function takes the minimum power consumption prediction value of the first temperature adjustment device as the target, and the first objective function is used to indicate: there is a functional relationship between the input parameter and the power consumption prediction value of the first temperature adjustment device through the power consumption prediction model, and the input parameter includes the first operating parameter and the second operating parameter of the second temperature adjustment device in the communication room; the first constraint condition is used to indicate that the power consumption value of the first temperature adjustment device is within a first range, and the second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature adjustment device is within a corresponding second range;

[0223] The adjustment control module 703 is used to adjust the second operating parameter according to the solved power consumption prediction value so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold.

[0224] Optionally, the solution module 702 includes:

[0225] The solution submodule is used to adopt a multi-strategy constrained differential evolution algorithm based on population partitioning to solve the first objective function according to the first operating parameters and the first constraint condition and the second constraint condition to obtain the power consumption prediction value of the first temperature control device in the communication room.

[0226] Optionally, the solution submodule is specifically used for:

[0227] Initializing the second operating parameter to obtain an initial population;

[0228] When v is an integer from 1 to V, the first operating parameter and the second operating parameter corresponding to the vth individual in the initial population are input into the power consumption prediction model to obtain the fitness of the vth individual, where V represents the number of individuals in the initial population;

[0229] According to the obtained fitness of the 1st to Vth individuals, the initial population is divided into a dominant population and a disadvantaged population;

[0230] A superior mutation strategy is used to perform a mutation operation on the superior population to obtain a first population to be processed, and a inferior mutation strategy is used to perform a mutation operation on the inferior population to obtain a second population to be processed;

[0231] Performing a crossover operation on the dominant population and the first population to be processed to obtain a third population to be processed, and performing a crossover operation on the inferior population and the second population to be processed to obtain a fourth population to be processed;

[0232] According to the first constraint condition and the second constraint condition, the second operating parameter and the fitness corresponding to each individual in the third population to be processed are adjusted to obtain a fifth population to be processed;

[0233] According to the first constraint condition and the second constraint condition, the second operating parameter and the fitness corresponding to each individual in the fourth population to be processed are adjusted to obtain a sixth population to be processed;

[0234] Determining a progeny population corresponding to the dominant population according to the fitness of each individual in the dominant population and the fifth population to be processed;

[0235] Determining a progeny population corresponding to the inferior population according to the fitness of each individual in the inferior population and the sixth population to be processed;

[0236] Let x = 2, determine whether x is less than the maximum number of iterations;

[0237] When x is less than the maximum number of iterations, the offspring population corresponding to the dominant population and the offspring population corresponding to the inferior population are merged, the merged population is re-divided into a dominant population and an inferior population, and the step of using the dominant mutation strategy to mutate the dominant population to obtain the first population to be processed and using the inferior mutation strategy to mutate the inferior population to obtain the second population to be processed is returned to;

[0238] Let x=x+1, and repeat the step of determining whether x is less than the maximum number of iterations;

[0239] When x is equal to the maximum number of iterations, the minimum fitness is obtained from the fitness of each individual of the child population corresponding to the dominant population, and the minimum fitness is determined as the power consumption prediction value of the first temperature adjustment device in the communication room.

[0240] Optionally, the second operating parameter includes N parameters; the solving submodule adopts a dominant mutation strategy to perform a mutation operation on the dominant population to obtain a first population to be processed, including:

[0241] Selecting the first individual with the smallest fitness in the dominant population;

[0242] When v is any integer from 1 to V, the following process is performed to obtain the first to-be-processed population including V individuals:

[0243] Randomly selecting a second individual and a third individual from the individuals other than the first individual in the dominant population;

[0244] When i is an integer from 1 to N, the i-th parameter r of the first individual 1i , the i-th parameter r of the second individual 2i , the i-th parameter r of the third individual 3i , substitute into the first formula, and obtain the i-th parameter r of an individual in the first population to be processed 待1i ;

[0245] Among them, the first formula is: 待1i =r 1i +F1(r 2i -r 3i ), F1 represents the scaling factor.

[0246] Optionally, the second operating parameter includes N parameters; the solving submodule adopts an inferior mutation strategy to perform a mutation operation on the inferior population to obtain a second population to be processed, including:

[0247] When v is an integer from 1 to V, the following process is performed to obtain the second to-be-processed population including V individuals:

[0248] Randomly selecting a fourth individual, a fifth individual, and a sixth individual from the inferior population;

[0249] When i is an integer from 1 to N, the i-th parameter r of the fourth individual 4i , the i-th parameter r of the fifth individual 5i , the i-th parameter r of the sixth individual 6i , substitute into the second formula, and obtain the i-th parameter r of an individual in the second population to be processed 待2i ;

[0250] Wherein, the second formula is: 待2i =r 4i +F2(r 5i -r 6i ), F2 represents the scaling factor.

[0251] Optionally, the solving submodule adjusts the second operating parameter and the fitness corresponding to each individual in the third population to be processed according to the first constraint condition and the second constraint condition to obtain a fifth population to be processed, including at least one of the following:

[0252] In the case where there is a seventh individual in the third population to be processed, setting the fitness of the seventh individual to be a first preset value, wherein the fitness of the seventh individual is outside the first range and is less than a second threshold, and the difference between the first preset value and the upper limit of the first range is greater than a third threshold;

[0253] When there is an eighth individual in the third population to be processed, the parameters of the eighth individual that are outside the second range are adjusted to within the second range, wherein at least one of the second operating parameters of the eighth individual is outside the corresponding second range.

[0254] Optionally, the solving submodule determines the offspring population corresponding to the dominant population according to the fitness of each individual in the dominant population and the fifth population to be processed, including:

[0255] When v is an integer from 1 to V, the following process is performed to obtain the offspring population corresponding to the dominant population including V individuals:

[0256] Comparing the first fitness of the vth individual in the dominant population with the second fitness of the vth individual in the fifth population to be processed;

[0257] When the first fitness is less than the second fitness, taking the vth individual in the dominant population as the vth individual in the offspring population corresponding to the dominant population;

[0258] When the first fitness is greater than the second fitness, taking the vth individual in the fifth population to be processed as the vth individual in the offspring population corresponding to the dominant population;

[0259] When the first fitness is equal to the second fitness, one of the vth individual in the dominant population and the vth individual in the fifth population to be processed is randomly selected as the vth individual in the offspring population corresponding to the dominant population.

[0260] Optionally, the adjustment control module 703 includes:

[0261] The control submodule is used to adjust the second operating parameter according to the target type of the network usage time period of the communication room to which the current time belongs, using a predictive control method and according to the power consumption prediction value obtained by solving it, so that the difference between the power consumption value of the first temperature control device at the second time and the power consumption prediction value obtained by solving it is less than or equal to a first threshold value.

[0262] Optionally, the control submodule is specifically used for:

[0263] When the target type is the first predetermined type, solving the second target function to obtain the value to which the second operating parameter needs to be adjusted in step P, and adjusting the second operating parameter to the value corresponding to step j when the time corresponding to step j arrives;

[0264] Where j is an integer from 1 to P, and the time interval between two adjacent steps is a fixed value;

[0265] The second objective function aims to minimize the first objective value O1, and the first objective value is determined by a third formula;

[0266] The third formula is:

[0267] Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained;

[0268] h j represents the error compensation coefficient corresponding to the jth step;

[0269] e(k)=Y(k)-Y M (k), Y(k) represents the actual power consumption value of the first temperature adjustment device at the first time k, and Y M (k) represents the first operating parameter collected at the first time k and the second operating parameter collected are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the first time k is obtained;

[0270] Y R (k+j)=α j Y(k)+(1-α j )Y * , Y * represents the power consumption prediction value obtained by solving the first objective function, α j Represents the smoothness coefficient of the jth step.

[0271] Optionally, the control submodule is specifically used for:

[0272] When the target type is the second predetermined type, solving the third target function to obtain the value to which the second operating parameter needs to be adjusted in step P, and adjusting the second operating parameter to the value corresponding to step j when the time corresponding to step j arrives;

[0273] Where j is an integer from 1 to P, and the time interval between two adjacent steps is a fixed value;

[0274] Wherein, the third objective function aims to minimize the second objective value O2, and the second objective value is determined by the fourth formula;

[0275] The fourth formula is:

[0276] Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained;

[0277] h j represents the error compensation coefficient corresponding to the jth step;

[0278] e(k)=Y(k)-Y M (k), Y(k) represents the actual power consumption value of the first temperature adjustment device at the first time k, and Y M (k) represents the first operating parameter collected at the first time k and the second operating parameter collected are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the first time k is obtained;

[0279] Y R (k+j)=α j Y(k)+(1-α j )Y * , Y * represents the power consumption prediction value obtained by solving the first objective function, α j represents the smoothness coefficient of the jth step;

[0280] N represents the number of parameters included in the second operating parameters, C i represents the weight coefficient of the i-th parameter in the second operating parameter, r i (k+j+1) represents the value of the i-th parameter in the second operating parameter corresponding to the j+1-th step, r i(k+j) represents the value of the i-th parameter in the second operating parameters corresponding to the j-th step.

[0281] It should be noted that the division of units in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0282] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0283] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0284] The embodiment of the present application also provides an electronic device, such as Figure 8 As shown, the electronic device includes a memory 820, a transceiver 810, and a processor 800;

[0285] A memory 820, for storing computer programs;

[0286] The transceiver 810 is used to receive and send data under the control of the processor 800;

[0287] The processor 800 is used to read the computer program in the memory 820 and execute the operation control method of the temperature adjustment device described in the first aspect.

[0288] Among them, Figure 8In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 800 and memory represented by memory 820. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 810 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, which transmission medium may include a wireless channel, a wired channel, an optical cable, and other transmission media. The processor 800 is responsible for managing the bus architecture and general processing, and the memory 820 may store data used by the processor 800 when performing operations.

[0289] The processor 800 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD). The processor 800 may also adopt a multi-core architecture.

[0290] It should be noted here that the above-mentioned electronic device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0291] An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the operation control method of the temperature regulating device described in the first aspect above is implemented.

[0292] The computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (Magnet-Optica, MO)), etc.), optical storage (such as Compact Disk (CD), Digital Versatile Disc (DVD), Blu-ray Disc (BD), Holographic Versatile Disc (HVD), etc.), and semiconductor memory (such as Read-Only Memory (ROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), non-volatile memory (NAND FLASH), solid-state drive (Solid State Disk or Solid State Drive, SSD)), etc.

[0293] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0294] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0295] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0296] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0297] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for controlling the operation of a temperature regulating device, characterized in that: The method comprises: Obtain the first operating parameters of the communication equipment in the communication room at the first time; According to the first operating parameter and the determined first constraint and second constraint, the constructed first objective function is solved to obtain the power consumption prediction value of the first temperature adjustment device in the communication room; wherein the first objective function takes the minimum power consumption prediction value of the first temperature adjustment device as the target, and the first objective function is used to indicate: there is a functional relationship between the input parameter and the power consumption prediction value of the first temperature adjustment device through the power consumption prediction model, and the input parameter includes the first operating parameter and the second operating parameter of the second temperature adjustment device in the communication room; the first constraint is used to indicate that the power consumption value of the first temperature adjustment device is within a first range, and the second constraint is used to indicate that at least one of the second operating parameters of the second temperature adjustment device is within a corresponding second range; According to the solved power consumption prediction value, the second operating parameter is adjusted so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold.

2. The method according to claim 1, characterized in that The step of solving the constructed first objective function according to the first operating parameter and the determined first constraint condition and second constraint condition to obtain the power consumption prediction value of the first temperature adjustment device in the communication room includes: A multi-strategy constrained differential evolution algorithm based on population partition is adopted to solve the first objective function according to the first operating parameter and the first constraint condition and the second constraint condition to obtain the power consumption prediction value of the first temperature adjustment device in the communication room.

3. The method according to claim 2, characterized in that The multi-strategy constrained differential evolution algorithm based on population partition is used to solve the first objective function according to the first operating parameter and the first constraint condition and the second constraint condition to obtain the power consumption prediction value of the first temperature adjustment device in the communication room, including: Initializing the second operating parameter to obtain an initial population; When v is an integer from 1 to V, the first operating parameter and the second operating parameter corresponding to the vth individual in the initial population are input into the power consumption prediction model to obtain the fitness of the vth individual, where V represents the number of individuals in the initial population; According to the obtained fitness of the 1st to Vth individuals, the initial population is divided into a dominant population and a disadvantaged population; A superior mutation strategy is used to perform a mutation operation on the superior population to obtain a first population to be processed, and a inferior mutation strategy is used to perform a mutation operation on the inferior population to obtain a second population to be processed; Performing a crossover operation on the dominant population and the first population to be processed to obtain a third population to be processed, and performing a crossover operation on the inferior population and the second population to be processed to obtain a fourth population to be processed; According to the first constraint condition and the second constraint condition, the second operating parameter and the fitness corresponding to each individual in the third population to be processed are adjusted to obtain a fifth population to be processed; According to the first constraint condition and the second constraint condition, the second operating parameter and the fitness corresponding to each individual in the fourth population to be processed are adjusted to obtain a sixth population to be processed; Determining a progeny population corresponding to the dominant population according to the fitness of each individual in the dominant population and the fifth population to be processed; Determining a progeny population corresponding to the inferior population according to the fitness of each individual in the inferior population and the sixth population to be processed; Let x = 2, determine whether x is less than the maximum number of iterations; When x is less than the maximum number of iterations, the offspring population corresponding to the dominant population and the offspring population corresponding to the inferior population are merged, the merged population is re-divided into a dominant population and an inferior population, and the step of using the dominant mutation strategy to mutate the dominant population to obtain the first population to be processed and using the inferior mutation strategy to mutate the inferior population to obtain the second population to be processed is returned to; Let x=x+1, and repeat the step of determining whether x is less than the maximum number of iterations; When x is equal to the maximum number of iterations, the minimum fitness is obtained from the fitness of each individual of the child population corresponding to the dominant population, and the minimum fitness is determined as the power consumption prediction value of the first temperature adjustment device in the communication room.

4. The method according to claim 3, characterized in that The second operating parameter includes N parameters; the step of using the dominant mutation strategy to perform a mutation operation on the dominant population to obtain a first population to be processed includes: Selecting the first individual with the smallest fitness in the dominant population; When v is any integer from 1 to V, the following process is performed to obtain the first to-be-processed population including V individuals: Randomly selecting a second individual and a third individual from the individuals other than the first individual in the dominant population; When i is an integer from 1 to N, the i-th parameter r of the first individual 1i , the i-th parameter r of the second individual 2i , the i-th parameter r of the third individual 3i , substitute into the first formula, and obtain the i-th parameter r of an individual in the first population to be processed 待1i ; Among them, the first formula is: 待1i =r 1i +F1(r 2i -r 3i ), F1 represents the scaling factor.

5. The method according to claim 3, characterized in that: The second operating parameters include N parameters; the step of using the inferior mutation strategy to perform a mutation operation on the inferior population to obtain a second population to be processed includes: When v is an integer from 1 to V, the following process is performed to obtain the second to-be-processed population including V individuals: Randomly selecting a fourth individual, a fifth individual, and a sixth individual from the inferior population; When i is an integer from 1 to N, the i-th parameter r of the fourth individual 4i , the i-th parameter r of the fifth individual 5i , the i-th parameter r of the sixth individual 6i , substitute into the second formula, and obtain the i-th parameter r of an individual in the second population to be processed 待2i ; Wherein, the second formula is: 待2i =r 4i +F2(r 5i -r 6i ), F2 represents the scaling factor.

6. The method according to claim 3, characterized in that The step of adjusting the second operating parameter and the fitness corresponding to each individual in the third population to be processed according to the first constraint condition and the second constraint condition to obtain a fifth population to be processed includes at least one of the following: In the case where there is a seventh individual in the third population to be processed, setting the fitness of the seventh individual to be a first preset value, wherein the fitness of the seventh individual is outside the first range and is less than a second threshold, and the difference between the first preset value and the upper limit of the first range is greater than a third threshold; When there is an eighth individual in the third population to be processed, the parameters of the eighth individual that are outside the second range are adjusted to within the second range, wherein at least one of the second operating parameters of the eighth individual is outside the corresponding second range.

7. The method according to claim 3, characterized in that The step of determining the offspring population corresponding to the dominant population according to the fitness of each individual in the dominant population and the fifth population to be processed comprises: When v is an integer from 1 to V, the following process is performed to obtain the offspring population corresponding to the dominant population including V individuals: Comparing the first fitness of the vth individual in the dominant population with the second fitness of the vth individual in the fifth population to be processed; When the first fitness is less than the second fitness, taking the vth individual in the dominant population as the vth individual in the offspring population corresponding to the dominant population; When the first fitness is greater than the second fitness, taking the vth individual in the fifth population to be processed as the vth individual in the offspring population corresponding to the dominant population; When the first fitness is equal to the second fitness, one of the vth individual in the dominant population and the vth individual in the fifth population to be processed is randomly selected as the vth individual in the offspring population corresponding to the dominant population.

8. The method according to any one of claims 1 to 7, characterized in that: The adjusting the second operating parameter according to the solved power consumption prediction value so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold value includes: According to the target type of the network usage time period of the communication room to which the current time belongs, a predictive control method is adopted to adjust the second operating parameter according to the power consumption prediction value obtained by solving, so that the difference between the power consumption value of the first temperature control device at the second time and the power consumption prediction value obtained by solving is less than or equal to the first threshold.

9. The method according to claim 8, characterized in that The method of using a predictive control method according to the target type of the network usage time period of the communication room to which the current time belongs, and adjusting the second operating parameter according to the power consumption prediction value obtained by solving, so that the difference between the power consumption value of the first temperature adjustment device at the second time and the power consumption prediction value obtained by solving is less than or equal to a first threshold, includes: When the target type is the first predetermined type, solving the second target function to obtain the value to which the second operating parameter needs to be adjusted in step P, and adjusting the second operating parameter to the value corresponding to step j when the time corresponding to step j arrives; Where j is an integer from 1 to P, and the time interval between two adjacent steps is a fixed value; The second objective function aims to minimize the first objective value O1, and the first objective value is determined by a third formula; The third formula is: Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained; h j represents the error compensation coefficient corresponding to the jth step; e(k)=Y(k)-Y M (k), Y(k) represents the actual power consumption value of the first temperature adjustment device at the first time k, and Y M (k) represents the first operating parameter collected at the first time k and the second operating parameter collected are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the first time k is obtained; Y R (k+j)=α j Y(k)+(1-α j )Y * , Y * represents the power consumption prediction value obtained by solving the first objective function, α j Represents the smoothness coefficient of the jth step.

10. The method according to claim 8, characterized in that The method of using a predictive control method according to the target type of the network usage time period of the communication room to which the current time belongs, and adjusting the second operating parameter according to the power consumption prediction value obtained by solving, so that the difference between the power consumption value of the first temperature adjustment device at the second time and the power consumption prediction value obtained by solving is less than or equal to a first threshold, includes: When the target type is the second predetermined type, solving the third target function to obtain the value to which the second operating parameter needs to be adjusted in step P, and adjusting the second operating parameter to the value corresponding to step j when the time corresponding to step j arrives; Where j is an integer from 1 to P, and the time interval between two adjacent steps is a fixed value; Wherein, the third objective function aims to minimize the second objective value O2, and the second objective value is determined by the fourth formula; The fourth formula is: Among them, Y M (k+j) represents: the first operating parameter collected at the first time k and the second operating parameter corresponding to the jth step are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the time corresponding to the jth step is obtained; h j represents the error compensation coefficient corresponding to the jth step; e(k)=Y(k)-Y M (k), Y(k) represents the actual power consumption value of the first temperature adjustment device at the first time k, and Y M (k) represents the first operating parameter collected at the first time k and the second operating parameter collected are input into the power consumption prediction model, and the power consumption prediction value of the first temperature adjustment device at the first time k is obtained; Y R (k+j)=α j Y(k)+(1-α j )Y * , Y * represents the power consumption prediction value obtained by solving the first objective function, α j represents the smoothness coefficient of the jth step; N represents the number of parameters included in the second operating parameters, C i represents the weight coefficient of the i-th parameter in the second operating parameter, r i (k+j+1) represents the value of the i-th parameter in the second operating parameter corresponding to the j+1-th step, r i (k+j) represents the value of the i-th parameter in the second operating parameters corresponding to the j-th step.

11. An operation control device for a temperature regulating device, characterized in that: The device comprises: A first acquisition module, used to acquire a first operating parameter of a communication device in a communication room at a first time; A solution module, used for solving the constructed first objective function according to the first operating parameter and the determined first constraint condition and second constraint condition, to obtain the power consumption prediction value of the first temperature adjustment device in the communication room; wherein the first objective function takes the minimum power consumption prediction value of the first temperature adjustment device as the target, and the first objective function is used to indicate: there is a functional relationship between the input parameter and the power consumption prediction value of the first temperature adjustment device through the power consumption prediction model, and the input parameter includes the first operating parameter and the second operating parameter of the second temperature adjustment device in the communication room; the first constraint condition is used to indicate that the power consumption value of the first temperature adjustment device is within a first range, and the second constraint condition is used to indicate that at least one of the second operating parameters of the second temperature adjustment device is within a corresponding second range; The adjustment control module is used to adjust the second operating parameter according to the solved power consumption prediction value so that the difference between the power consumption value of the first temperature adjustment device at the second time and the solved power consumption prediction value is less than or equal to a first threshold.

12. An electronic device, characterized in that: Including memory, transceiver, processor: Memory for storing computer programs; a transceiver, for transmitting and receiving data under the control of the processor; A processor is used to read the computer program in the memory and execute the operation control method of the temperature adjustment device according to any one of claims 1 to 10.

13. A readable storage medium, characterized in that: The readable storage medium stores a program or an instruction, and when the program or the instruction is executed by the processor, the operation control method of the temperature adjustment device according to any one of claims 1 to 10 is implemented.

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