A method for assigning optimal rest time slices for agricultural sensors

Abstract

The invention discloses a method for assigning optimal time slices for agricultural sensor rest, comprising: dividing twenty-four hours of a day into time slices according to the power consumption level of a region, and determining the power consumption types corresponding to different time slices in the region; determining the unit price of electricity for each time slice according to the power consumption type of the region; determining the power of each sensor for cold wind rest and the number of cold wind rests of the sensor in a day according to the historical power consumption data of each agricultural sensor in the region; determining the electricity price of each sensor for rest in each time slice according to the unit price of electricity for each time slice and the power of each sensor for cold wind rest and performing normalization processing; determining the latest time for each sensor to start cold wind rest for the first time in a day and the maximum cold wind rest time interval according to the historical heating data of each sensor; establishing an optimization model for allocating rest time slices for agricultural sensors, and obtaining a rest time slice allocation plan for agricultural sensors by solving the optimization model.

Description

Technical Field

[0001] The invention relates to the fields of agriculture, sensor, time slice assignment and optimal planning technology, and in particular to a method for assigning optimal time slices for agricultural sensor rest under dynamic energy consumption price constraints. Background Art

[0002] The century-old solution to agricultural problems is industrialization, and informatization is the soul of industrialization. Realizing the dynamic real-time digital collection, analysis and processing of the entire process of agricultural product growth, including crop cultivation, breeding, fertilization, plant protection, and harvesting, is of great significance for improving the level of agricultural informatization management, guiding agricultural production, and solving agricultural product safety issues.

[0003] Among them, sensors are the key factor in whether they can be popularized in the dynamic and real-time digital collection of the entire growth process of agricultural products. However, the current sensor technology for smart agriculture in my country is still in its infancy. Most agricultural sensors are not only expensive and energy-intensive, but also require long-term uninterrupted work to achieve dynamic and real-time digital collection, which causes severe sensor heat generation, short lifespan, and high maintenance costs. The high cost makes sensors not suitable for ordinary farmers, and popularization has become a difficult task.

[0004] Since the sensor works continuously for a long time, it will cause serious heating of the sensor, which will affect the life of the sensor. Therefore, the sensor with serious heating must stop working and take a rest. The usual way to rest is to use cold air to rest and cool down. This method is easy to control. It only needs to determine the rest and cooling cycle and duration, and allocate fixed rest time slices in sequence. However, this approach has certain disadvantages, that is, it is difficult to arrange rest time slices according to the actual heating degree of the sensor, because the sensor is affected by the model and aging degree, and the heating degree in the same time is different. The number and cycle of rest time slices to be allocated should also change accordingly. Moreover, due to the high electricity consumption, flat peak and valley periods in different regions, the electricity prices in different periods are different. If the sensors with high cold air rest power can be rested as much as possible during the valley period, and the sensors with high cold air rest power can be allowed to work during the peak period, the electricity cost of farmers can be reduced accordingly.

[0005] Therefore, it is of great significance to popularize the use of sensors by reasonably setting flexible rest and cooling cycle intervals to cool down the sensors. Under the premise of satisfying the dynamic real-time digital acquisition of sensors, that is, at most one sensor in a group of sensors can rest in the same time period, different cooling time periods and cooling cycles are arranged according to the actual heating degree of the sensors to realize intelligent rest time management and allocation, so that the scheduling and assignment are reasonable and well-founded. Therefore, how to ensure that the sensors rest and cool down in time while saving electricity bills and reducing the electricity costs of farmers is still an urgent problem to be solved. Summary of the invention

[0006] The purpose of the present invention is to provide a method for assigning the best rest time for agricultural sensors, which can reduce the electricity cost of farmers to a minimum while meeting the requirements of dynamic real-time digital acquisition of sensors and timely rest and cooling of sensors through intelligent rest time management and allocation.

[0007] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0008] A method for assigning optimal rest time slices for agricultural sensors comprises the following steps:

[0009] According to the regional electricity consumption level, the twenty-four hours of a day are divided into time slices to determine the electricity consumption types corresponding to different time slices in the region, including peak period, off-peak period and valley period;

[0010] Determining the unit price of electricity for each of the time slices according to the type of electricity consumption in the area;

[0011] Determine the power of each sensor's cold wind break and the number of cold wind breaks of the sensor in a day through the historical power consumption data of each agricultural sensor in the region;

[0012] According to the unit price of electricity in each time slice and the power of each sensor during cooling and resting, the electricity price of each sensor during resting in each time slice is determined and normalized;

[0013] Determine the latest time of starting the cold wind rest for the first time in a day and the maximum cold wind rest time interval for each sensor through the historical heating data of each sensor;

[0014] An optimization model for the allocation of rest time slices for agricultural sensors is established as follows:

[0015] The objective function is:

[0016]

[0017] The constraints are:

[0018] T[i,j]∈{0,1}(0≤i <m) (2)

[0019]

[0020]

[0021]

[0022]

[0023] Where m is the number of time slices, n is the number of sensors; the matrix is the evaluation matrix of the optimization model, represents the normalized electricity price of sensor j at rest in the i-th time slice;

[0024] The matrix T is the output result of the optimization model, that is, the allocation scheme of the rest time slices of agricultural sensors. T[i,j]=1 means that the i-th time slice is allocated to sensor j, and T[i,j]=0 means that the i-th time slice is not allocated to sensor j;

[0025] L[j] represents the number of cold air breaks of sensor j in a day; Γ[j,0] represents the latest time when sensor j starts the cold air break for the first time in a day; Γ[j,1] represents the maximum cold air break time interval of sensor j;

[0026] By solving the optimization model of rest time allocation for agricultural sensors, the rest time allocation scheme for agricultural sensors is obtained.

[0027] Furthermore, the twenty-four hours of a day are divided into 48 time slices for processing.

[0028] Furthermore, the formula for calculating the electricity price for each time slot is as follows:

[0029] Q[i,j]=Price[C period [i]]*Power[j]*0.5(0≤i <m,0≤j<n)

[0030] Where Q[i,j] represents the electricity price of sensor j in the i-th time slot, Power[j] represents the power of the cooling air of sensor j, m is the number of time slots, and n is the number of sensors; C period [i] represents the type of electricity consumption in the i-th time slice, Price[C period [i]] represents the unit price of electricity in each time slice;

[0031] The calculated electricity price of the sensor at rest in each time slice is normalized:

[0032]

[0033] Right now:

[0034]

[0035] Thus, the electricity price of each sensor in each time slice after processing is obtained.

[0036] A dispatching device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for dispatching the optimal rest time slice of the agricultural sensor are implemented.

[0037] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for assigning optimal rest time slices for agricultural sensors.

[0038] Compared with the prior art, the present invention has the following technical features:

[0039] 1. By establishing an optimization model for the allocation of rest time slices for agricultural sensors, the problem of allocating cold air rest time for sensors is briefly formalized with flexible time intervals.

[0040] 2. The expression of different period interval constraints of the sensor's cold wind rest time is linearized, and a reasonable method for assigning optimal time slices for agricultural sensor rest is given.

[0041] 3. Large-scale simulation experiments have proved that the method of allocating sensor cold air rest time slices with flexible periodic intervals can save more electricity than scheduled cold air rest, which can save electricity costs for farmers and help popularize the use of sensors, thereby further realizing dynamic real-time digital collection of the entire growth process of agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the process of the present invention;

[0043] Figure 2 The figure is a comparison diagram of the application effects of the solution of the present invention and the solution of the prior art. DETAILED DESCRIPTION

[0044] See also Figure 1 The purpose of the present invention is to provide a method for allocating optimal rest time slices for agricultural sensors, comprising the following steps:

[0045] Step 1: divide the twenty-four hours of a day into time slices according to the regional electricity consumption level, and determine the electricity consumption types corresponding to different time slices in the region, including peak period, off-peak period and valley period.

[0046] In this embodiment, the peak, off-peak and off-peak periods of electricity consumption in the region are shown in Table 1 below.

[0047] Table 1 Distribution of electricity consumption

[0048] Time period 0:00-7:00 7:00-9:00 9:00-23:00 23:00-24:00 type Low period Off-peak period Peak Off-peak period

[0049] Using C period[i] represents the type of electricity consumption in the i-th time slice. The twenty-four hours of a day are divided into 48 time slices for processing, and the following formula is obtained:

[0050]

[0051] This means that the electricity consumption type of time slices 0 to 13 is off-peak, the electricity consumption type of time slices 14 to 17 and 46 to 47 is off-peak, and the electricity consumption type of time slices 18 to 45 is peak. The off-peak, off-peak and peak periods are represented by the numbers 0, 1 and 2 respectively.

[0052] Step 2: Determine the unit price of electricity for each time slice according to the type of electricity consumption in the area.

[0053] In this embodiment, the unit price of electricity Price[α]=[0.425, 0.725, 1.025](0≤α<3), α=C period [i], that is This means that the off-peak electricity price for time slices 0 to 13 is 0.425, the off-peak electricity price for time slices 14 to 17 and 46 to 47 is 0.725, and the peak electricity price for time slices 18 to 45 is 1.025.

[0054] Step 3, determining the power of each sensor's cold wind rest and the number of cold wind rests of the sensor in a day through the historical power consumption data of each agricultural sensor in the area.

[0055] As shown in the example given in Table 2, the power of the cold wind rest of sensor 0 is 3500Kw / h, and the number of cold wind rests in a day is 3.

[0056] Table 2 The power of the sensor cold air rest and the number of cold air rests in a day

[0057] sensor 0 1 2 3 4 5 Cooling power (Kw / h) 3500 2500 4000 3800 4200 3200 Cooling breaks 3 2 4 3 4 2

[0058] The agricultural sensors refer to various types of sensors used in agriculture in the region, such as temperature and humidity sensors, concentration sensors, etc.; the specific types are specified by the user according to actual application requirements.

[0059] Step 4: According to the unit price of electricity in each time slice and the power of each sensor in the cold wind rest mode, the electricity price of each sensor in each time slice is determined and normalized, as follows:

[0060] 4.1 Determine the electricity price for each sensor to rest in each time slice.

[0061] The formula for calculating the electricity price for each time slot is as follows:

[0062] Q[i,j]=Price[Cperiod [i]]*Power[j]*0.5(0≤i <m,0≤j<n)

[0063] Where Q[i,j] represents the electricity price of sensor j during the i-th time slot, Power[j] represents the power of the cooling fan of sensor j during the rest period, m is the number of time slots, and n is the number of sensors.

[0064] 4.2 Normalize the calculated electricity price of the sensor at rest in each time slice:

[0065]

[0066] Right now:

[0067]

[0068] Thus, the electricity price of each sensor in each time slice after processing is obtained.

[0069] In an example of the present invention, the electricity price Q[i,j] for each time slot is as follows:

[0070] The electricity price of the matrix Q sensor at rest in different time periods

[0071]

[0072] In order to express it in a matrix, we use ellipsis to indicate the undisplayed part. Among them, Q[0,0] means that the electricity price of sensor 0 at rest in the 0th time slice is 0.13.

[0073] Step 5: Determine the latest time of starting the cold wind rest for the first time in a day and the maximum cold wind rest time interval of each sensor through the historical heating data of each sensor.

[0074] Let Г[j,0] represent the latest time when sensor j starts the cold air rest for the first time in a day, and Г[j,1] represent the maximum cold air rest time interval of sensor j.

[0075] Table 3 The latest time for the sensor to start the cold wind rest for the first time and the maximum cold wind rest time interval

[0076]

[0077] That is, in the example given in the above table, Г[0,0] means that the latest time for sensor 0 to start the cold air rest for the first time in a day is the 8th time slice, and Г[0,1] means that the maximum cold air rest time interval of sensor 0 is 16 time slices.

[0078] Step 6: Establish an optimization model for allocating rest time slices for agricultural sensors, as follows:

[0079] The objective function is:

[0080]

[0081] The constraints are:

[0082] T[i,j]∈{0,1}(0≤i <m) (2)

[0083]

[0084]

[0085]

[0086]

[0087] Where m is the number of time slices and n is the number of sensors.

[0088] matrix is the evaluation matrix of the optimization model, represents the normalized electricity price of sensor j at rest in the i-th time slice.

[0089] The matrix T is the output result of the optimization model, that is, the allocation plan of the rest time slices of agricultural sensors. T[i,j] indicates whether the i-th time slice is allocated to sensor j. T[i,j]=1 indicates that the i-th time slice is allocated to sensor j, and T[i,j]=0 indicates that the i-th time slice is not allocated to sensor j.

[0090] L[j] represents the number of cold air breaks of sensor j in a day.

[0091] Γ[j,0] represents the latest time when sensor j starts the cold wind break for the first time in a day, and Γ[j,1] represents the maximum cold wind break time interval of sensor j.

[0092] Therefore, formula (1), i.e., the objective function, is to find the minimum electricity cost of the rest time slice assignment scheme. Formula (2), represents the value of the matrix T, which has only two options: 0 or 1. That is, T[i,j] indicates whether the i-th time slice is assigned to sensor j, T[i,j]=1 indicates that the i-th time slice is assigned to sensor j, and T[i,j]=0 indicates that the i-th time slice is not assigned to sensor j. Formula (3), indicates that each time slice i can only be assigned to one sensor at most. Formula (4), indicates that the sum of the time slices assigned to sensor j should be equal to the number of cold wind breaks of sensor j in one day. Formula (5), indicates that sensor j should be assigned to at least one time slice before the Γ[j,0]th time slice (including the Γ[j,0]th time slice), that is, it should have taken a cold wind break at least once. Formula (6), indicates that the time interval between each two time slices assigned to sensor j cannot be greater than the maximum cold wind break time interval of sensor j.

[0093] Step 7, by solving the optimization model of the rest time allocation of agricultural sensors, the rest time allocation scheme of agricultural sensors is obtained.

[0094] In this embodiment, taking the data in Table 1, Table 2, Matrix Q, and Table 3 as an example, this solution obtains the rest time slice allocation scheme of the agricultural sensor as Matrix T as follows:

[0095] Rest time slice allocation scheme for matrix T agricultural sensors

[0096]

[0097] For matrix representation, portions not shown are indicated by ellipsis.

[0098] Effect verification

[0099] like Figure 2 As shown, the application effect comparison between the solution of the present invention and the prior art solution is shown; it can be found that the electricity cost of the present solution is lower, and as the number of time slices increases, the difference in electricity costs between the two becomes larger, that is, the present solution has a stable linear growth rate and universal applicability within a controllable range, which is more helpful in reducing electricity costs.

[0100] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for assigning optimal rest time slices for agricultural sensors, characterized in that: The following steps are involved: According to the regional electricity consumption level, the twenty-four hours of a day are divided into time slices to determine the electricity consumption types corresponding to different time slices in the region, including peak period, off-peak period and valley period; Determining the unit price of electricity for each of the time slices according to the type of electricity consumption in the area; Determine the power of each sensor's cold wind break and the number of cold wind breaks of the sensor in a day through the historical power consumption data of each agricultural sensor in the region; According to the unit price of electricity in each time slice and the power of each sensor during cooling and resting, the electricity price of each sensor during resting in each time slice is determined and normalized; Determine the latest time of starting the cold wind rest for the first time in a day and the maximum cold wind rest time interval for each sensor through the historical heating data of each sensor; An optimization model for the allocation of rest time slices for agricultural sensors is established as follows: The objective function is: The constraints are: T[i,j]∈{0,1}(0≤i <m) (2) Where m is the number of time slices, n is the number of sensors; the matrix is the evaluation matrix of the optimization model, represents the normalized electricity price of sensor j at rest in the i-th time slice; The matrix T is the output result of the optimization model, that is, the allocation scheme of the rest time slices of agricultural sensors. T[i,j]=1 means that the i-th time slice is allocated to sensor j, and T[i,j]=0 means that the i-th time slice is not allocated to sensor j; L[j] represents the number of cold air breaks of sensor j in a day; Γ[j,0] represents the latest time when sensor j starts the cold air break for the first time in a day; Γ[j,1] represents the maximum cold air break time interval of sensor j; By solving the optimization model of rest time allocation for agricultural sensors, the rest time allocation scheme for agricultural sensors is obtained.

2. The method for assigning optimal rest time slices for agricultural sensors according to claim 1, characterized in that: The twenty-four hours of a day are divided into 48 time slices for processing.

3. The method for assigning optimal rest time slices for agricultural sensors according to claim 1, characterized in that: The formula for calculating the electricity price for each time slot is as follows: Q[i,j]=Price[C period [i]]*Power[j]*0.5(0≤i<m,0≤j<n) Where Q[i,j] represents the electricity price of sensor j in the i-th time slot, Power[j] represents the power of the cooling air of sensor j, m is the number of time slots, and n is the number of sensors; C period [i] represents the type of electricity consumption in the i-th time slice, Price[C period [i]] represents the unit price of electricity in each time slice; The calculated electricity price of the sensor at rest in each time slice is normalized: Right now: Thus, the electricity price of each sensor in each time slice after processing is obtained.

4. A dispatching device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for assigning optimal rest time slices for agricultural sensors according to any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for assigning optimal rest time slices for agricultural sensors according to any one of claims 1 to 3 are implemented.

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