Workshop environment state type judgment method and device, electronic equipment and storage medium

Abstract

The invention is suitable for the technical field of workshop environment state monitoring and analysis, and provides a workshop environment state type judgment method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the data of at least one environment factor of a plurality of position measurement points of a workshop within a preset time; for each environment factor, according to the data of the environment factor, determining a dimensionless normalized statistical extreme value of the environment factor of different position measuring points; clustering the dimensionless normalized statistical extreme values of the environmental factors of different position measuring points, and dividing a workshop into a plurality of areas according to a clustering result; determining a representative value of the environmental factor of each area, and determining a state description index of the environmental factor according to the representative value; and determining the environment state type of the workshop based on the state description index of each environment factor. Accurate description and characteristic type judgment of the environment state of the large-span large-space workshop can be achieved, and support is provided for layout design and regulation and control work of an air conditioning system.

Description

Technical Field

[0001] The present invention belongs to the technical field of workshop environment status monitoring and analysis, and in particular relates to a workshop environment status type determination method, device, electronic equipment and storage medium. Background Art

[0002] With the development of the tobacco industry, large-span and large-space industrial plants are widely used in tobacco production. A large amount of raw materials such as tobacco shreds and cigarette paper are stored inside. At the same time, many instruments and equipment for performing different work links are installed. The storage of these raw materials, the processing technology of tobacco by instruments and equipment, and the comfort of production staff all put forward higher precision control requirements for the workshop environment.

[0003] At present, the satisfaction of reasonable environmental conditions such as wind temperature and humidity in the workshop is usually achieved with the help of air-conditioning systems. Reasonable description of the environmental conditions such as wind temperature and humidity in the tobacco factory workshop and accurate determination of their characteristic types are the basis and prerequisite for effectively starting and adjusting the working state of the air-conditioning system and optimizing its layout. It is also critical to improving the quality of finished cigarettes, reducing the energy consumption of the air-conditioning system on the basis of improving the working environment of the tobacco factory workshop, and reducing production and operation costs.

[0004] However, due to the diverse structural forms of large-span and large-space tobacco factories, and the significant coupling effects of workshop wind field, temperature field and humidity field, there is still no systematic method to establish the description of the workshop environmental status of large-span and large-space tobacco factories and the determination of their characteristic types. This brings many inconveniences to the layout design and regulation of air-conditioning systems, and often results in low regulation efficiency or insufficient regulation accuracy of the air-conditioning system. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method, device, electronic device and storage medium for determining the type of workshop environmental status, so as to achieve accurate description and characteristic type determination of the environmental status of large-span and large-space workshops, and provide support for the layout design and regulation of air-conditioning systems.

[0006] A first aspect of an embodiment of the present invention provides a method for determining a type of a workshop environment state, comprising:

[0007] Obtaining environmental data of multiple measurement points in a workshop within a preset time; wherein the environmental data includes data of at least one environmental factor;

[0008] For each environmental factor, determining the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations according to the data of the environmental factor;

[0009] Clustering the dimensionless normalized statistical extreme values ​​of the environmental factor at the different measuring points, and dividing the workshop into a plurality of areas according to the clustering results;

[0010] Determine a representative value of the environmental factor for each area, and determine a state description index of the environmental factor according to the representative value of the environmental factor for each area;

[0011] Based on the status description index of each environmental factor, the environmental status type of the workshop is determined.

[0012] In combination with the first aspect, in a possible implementation manner of the first aspect, determining, according to the data of the environmental factor, the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations includes:

[0013] The data of the environmental factor are dimensionlessly normalized;

[0014] According to the data of the environmental factor, the average value and the pulsation value of the environmental factor at the measuring points at different positions are calculated;

[0015] according to Determining the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations;

[0016] In the formula, x is the environmental factor, i is the location of the measurement point, is the dimensionless normalized statistical extreme value, is the average value, is the preset peak factor, is the pulsation value, and sign is the sign function.

[0017] In combination with the first aspect, in a possible implementation manner of the first aspect, determining the representative value of the environmental factor of each area includes:

[0018] Obtain the volume of each area and the volume of the subordinate area of ​​each preset position measuring point;

[0019] The representative value of the environmental factor in each area is determined according to the dimensionless normalized statistical extreme value of the environmental factor, the volume of each area and the volume of the subordinate area of ​​each preset position measuring point.

[0020] In conjunction with the first aspect, in a possible implementation of the first aspect, a formula for determining a representative value of the environmental factor of each area includes:

[0021]

[0022] In the formula, x is the environmental factor, i is the location measurement point, l is the cluster area, is a representative value, V i is the volume of the subordinate area of ​​the position measuring point i, is the volume of the lth cluster region, l n is the number of location measurement points in the lth cluster area, is the dimensionless normalized statistical extreme value.

[0023] In combination with the first aspect, in a possible implementation manner of the first aspect, determining the state description indicator of the environmental factor according to the representative value of the environmental factor of each area includes:

[0024] Calculate the average value of the representative value of the environmental factor in each area;

[0025] according to Determine the state description index of the environmental factor;

[0026] In the formula, x is the environmental factor, l is the area, C x is the state description indicator, M is the number of regions corresponding to the environmental factor, is the representative value of the environmental factor in the lth area, It is the average value of the representative value of the environmental factor in each area.

[0027] In combination with the first aspect, in a possible implementation manner of the first aspect, the environmental factor includes at least one of the following: wind speed, temperature, and humidity.

[0028] In combination with the first aspect, in a possible implementation manner of the first aspect, determining the environmental state of the workshop based on the state description indicators of each environmental factor includes:

[0029] Determine the state level of each environmental factor according to the state description indicator of each environmental factor and a preset grading table; wherein the grading table includes a reference range of the state description indicator corresponding to different state levels of the at least one environmental factor;

[0030] Based on the status level of each environmental factor, the environmental status of the workshop is determined.

[0031] A second aspect of an embodiment of the present invention provides a device for determining a type of a workshop environment state, comprising:

[0032] An acquisition module, used to acquire environmental data of multiple measurement points in a workshop within a preset time; wherein the environmental data includes data of at least one environmental factor;

[0033] A statistical module, for determining, for each environmental factor, the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations according to the data of the environmental factor;

[0034] A clustering module, used for clustering the dimensionless normalized statistical extreme values ​​of the environmental factor at the different measuring points, and dividing the workshop into a plurality of areas according to the clustering results;

[0035] A calculation module, used to determine a representative value of the environmental factor in each area, and determine a state description index of the environmental factor according to the representative value of the environmental factor in each area;

[0036] The determination module is used to determine the environmental state type of the workshop based on the state description indicators of various environmental factors.

[0037] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the statistical module is used to:

[0038] The data of the environmental factor are dimensionlessly normalized;

[0039] According to the data of the environmental factor, the average value and the pulsation value of the environmental factor at the measuring points at different positions are calculated;

[0040] according to Determining the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations;

[0041] In the formula, x is the environmental factor, i is the location of the measurement point, is the dimensionless normalized statistical extreme value, is the average value, is the preset peak factor, is the pulsation value, and sign is the sign function.

[0042] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the calculation module is used to:

[0043] Obtain the volume of each area and the volume of the subordinate area of ​​each preset position measuring point;

[0044] The representative value of the environmental factor in each area is determined according to the dimensionless normalized statistical extreme value of the environmental factor, the volume of each area and the volume of the subordinate area of ​​each preset position measuring point.

[0045] In conjunction with the second aspect, in a possible implementation of the second aspect, a formula for determining a representative value of the environmental factor of each area includes:

[0046]

[0047] In the formula, x is the environmental factor, i is the location measurement point, l is the cluster area, is a representative value, V i is the volume of the subordinate area of ​​the position measuring point i, is the volume of the lth cluster region, l n is the number of location measurement points in the lth cluster area, is the dimensionless normalized statistical extreme value.

[0048] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the calculation module is used to:

[0049] Calculate the average value of the representative value of the environmental factor in each area;

[0050] according to Determine the state description index of the environmental factor;

[0051] In the formula, x is the environmental factor, l is the area, C x is the state description indicator, M is the number of regions corresponding to the environmental factor, is the representative value of the environmental factor in the lth area, It is the average value of the representative value of the environmental factor in each area.

[0052] In combination with the second aspect, in a possible implementation manner of the second aspect, the environmental factor includes at least one of the following: wind speed, temperature, and humidity.

[0053] In conjunction with the second aspect, in a possible implementation manner of the second aspect, the determining module is used to:

[0054] Determine the state level of each environmental factor according to the state description indicator of each environmental factor and a preset grading table; wherein the grading table includes a reference range of the state description indicator corresponding to different state levels of the at least one environmental factor;

[0055] Based on the status level of each environmental factor, the environmental status type of the workshop is determined.

[0056] A third aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements the steps of the method in the first aspect or any one of the implementations of the first aspect.

[0057] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method in the above-mentioned first aspect or any one of the implementation methods of the first aspect.

[0058] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0059] The embodiment of the present invention obtains data of at least one environmental factor at multiple measuring points in a workshop within a certain period of time, and obtains dimensionless normalized statistical extreme values ​​with a certain guarantee rate through processing. On this basis, the dimensionless normalized statistical extreme values ​​of the environmental factor at different measuring points are clustered, and the workshop is divided into multiple areas according to the clustering results. The state description index of the environmental factor is determined by calculating the representative value of each area, and the description of the state of the environmental factor and the division of the characteristic type are realized, which provides a basis for accurately and efficiently adjusting the working state of the air-conditioning system and optimizing its layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art 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.

[0061] Figure 1 The following is a flow chart of a method for determining the type of workshop environment status provided by an embodiment of the present invention. Figure 1 ;

[0062] Figure 2 The following is a flow chart of a method for determining the type of workshop environment status provided by an embodiment of the present invention. Figure 2 ;

[0063] Figure 3 It is a structural schematic diagram of a workshop environment status type determination device provided by an embodiment of the present invention;

[0064] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0066] In order to illustrate the technical solution of the present invention, a specific embodiment is provided below for illustration.

[0067] Due to the diverse structural forms of large-span and large-space tobacco factories, the complex layout of air-conditioning systems, and the significant coupling effects of wind field, temperature field and humidity field in the workshop, it is difficult to accurately describe the workshop environment state and determine the characteristic type, which brings many inconveniences to the layout design and regulation of the air-conditioning system in tobacco factories, and often causes the air-conditioning system to have low regulation efficiency or insufficient regulation accuracy. With the increasing requirements of tobacco production and processing technology for the precision control of wind, temperature and humidity environment, and the current background of energy conservation and emission reduction, it is urgent to accurately grasp the coupling effect law of wind speed field, temperature field and humidity field in tobacco factory workshops, so as to establish a practical and feasible comprehensive description of tobacco factory workshop environment state and its characteristic type determination method, so as to efficiently guide the adjustment of air-conditioning system working state and optimize its layout. Previous studies on this issue have mostly used the proportion of wind, temperature and humidity space as the criterion for judging the quality, or used a single wind speed standard, temperature standard or humidity standard, without considering the coupling effect of the three.

[0068] In this regard, an embodiment of the present invention provides a method for determining the type of workshop environmental status, which aims to achieve the description of the environmental status of a tobacco factory workshop and the classification of characteristic types, creating conditions for accurately and efficiently adjusting the working status of the air-conditioning system and optimizing its layout.

[0069] Figure 1 1 is a schematic diagram of the implementation flow of the workshop environment status type determination method provided by the embodiment of the present invention. Figure 1 , the method of the embodiment of the present invention is described:

[0070] Step S101, obtaining environmental data of multiple measurement points in a workshop within a preset time; wherein the environmental data includes data of at least one environmental factor.

[0071] In this embodiment, multiple position measurement points are pre-set in the workshop, and these position measurement points are distributed as evenly as possible in the workshop. The preset time can be 12 hours, 24 hours, etc. The acquisition method can be based on a numerical simulation method or on-site monitoring, which is not limited in this embodiment.

[0072] Through this step, at each location measurement point, time-course data of at least one environmental factor within a preset time is obtained.

[0073] Among them, environmental factors include but are not limited to wind speed, temperature, humidity, dust, etc. Based on the different monitoring requirements of different workshops, technical personnel in this field can select appropriate environmental factors on their own.

[0074] Step S102: for each environmental factor, determine the dimensionless normalized statistical extreme value of the environmental factor at different measuring points according to the data of the environmental factor.

[0075] Dimensionless normalization is a data processing method that aims to convert data of different dimensions to the same scale for easy comparison and analysis. The main purpose of dimensionless normalization is to eliminate the dimensional differences between data so that data of different units or magnitudes can be compared and weighted. Normalization is a dimensionless processing method that usually scales data to a range, usually between 0 and 1.

[0076] By processing the data of the environmental factor into dimensionless normalized data results, the statistical extreme value with a certain guarantee rate can be statistically obtained.

[0077] As a possible implementation method, the calculation method of statistical extreme values ​​includes:

[0078] First, according to the data of the environmental factor, the average value and the pulsation value of the environmental factor at different measuring points are calculated, wherein the pulsation value refers to the difference between a random variable and its expected value (or average value).

[0079] Then, according to Determine the dimensionless normalized statistical extreme value of the environmental factor at different measuring points; where x is the environmental factor, i is the measuring point, is the dimensionless normalized statistical extreme value, is the average value, is the preset peak factor, is the pulsation value, and sign is the sign function.

[0080] Step S103 , clustering the dimensionless normalized statistical extreme values ​​of the environmental factor at different measuring points, and dividing the workshop into multiple areas according to the clustering results.

[0081] Here, the k-means clustering algorithm can be used to cluster the dimensionless normalized statistical extreme values ​​of the environmental factor at different location measurement points. It can be understood that since each statistical extreme value corresponds to a location measurement point, clustering can divide the location measurement points with similar statistical extreme values ​​into one area, thereby dividing the workshop into multiple areas.

[0082] Step S104: determine the representative value of the environmental factor of each area, and determine the state description indicator type of the environmental factor according to the representative value of the environmental factor of each area.

[0083] As a possible implementation method, determining the representative value of the environmental factor in each area may include:

[0084] Obtain the volume of each area and the volume of the subordinate area of ​​each preset position measuring point;

[0085] The representative value of the environmental factor in each area is determined according to the dimensionless normalized statistical extreme value of the environmental factor, the volume of each area and the volume of the subordinate area of ​​each preset position measuring point.

[0086] Here, the formula for determining the representative value of the environmental factor for each region includes:

[0087]

[0088] In the formula, x is the environmental factor, i is the location measurement point, l is the cluster area, is a representative value, V i is the volume of the subordinate area of ​​the position measuring point i, is the volume of the lth cluster region, l n is the number of location measurement points in the lth cluster area, is the dimensionless normalized statistical extreme value.

[0089] Furthermore, the state description index of the environmental factor can be determined by combining the representative value of the environmental factor in each area through the following formula:

[0090]

[0091] In the formula, x is the environmental factor, l is the area, C x is the state description indicator, M is the number of regions corresponding to the environmental factor, is the representative value of the environmental factor in the lth area, It is the average value of the representative value of the environmental factor in each area.

[0092] Step S105: determining the environmental status type of the workshop based on the status description index of each environmental factor.

[0093] In this embodiment, a grading table can be pre-established, which includes the reference range of the state description index corresponding to the different state levels of each environmental factor. According to the state description index of each environmental factor, the grading table is searched to determine the reference range of the state description index to which each environmental factor belongs, and the state level of each environmental factor is obtained.

[0094] Furthermore, by considering the coupling effect of various environmental factors, the environmental status type of the workshop can be comprehensively determined in combination with the status levels of various environmental factors.

[0095] It can be seen that the embodiment of the present invention obtains data of at least one environmental factor at multiple measurement points in a workshop within a certain period of time, and obtains dimensionless normalized statistical extreme values ​​with a certain guarantee rate through processing. On this basis, the dimensionless normalized statistical extreme values ​​of the environmental factor at different measurement points are clustered, and the workshop is divided into multiple areas according to the clustering results. The state description index of the environmental factor is determined by calculating the representative value of each area, and the description of the state of the environmental factor and the division of the characteristic type are realized, which provides a basis for accurately and efficiently adjusting the working state of the air-conditioning system and optimizing its layout.

[0096] In the following example, for a cigarette factory, the wind speed, temperature and humidity are used to analyze the wind temperature and humidity environment of the cigarette factory workshop to further explain the method. Figure 2 , the content of this embodiment is described in detail:

[0097] (1) Space measurement points are arranged at different positions in the tobacco factory workshop. Horizontal positions include: corners of the room, the middle of each wall, and 1 / 4 and 3 / 4 length positions, and the horizontal distance between the measurement point and the wall is guaranteed to be greater than 0.5m; vertical positions include: corresponding to the horizontal measurement point position, starting from 0.5m from the ground, and adding a measurement point for every 1.0m increase in height. During normal working hours, the wind speed test is tested and data is collected once every 1min, and the temperature and humidity tests are tested and data is collected once every 10min. The total test time is not less than 24h. The wind speed time history, temperature time history, and humidity time history of the measurement points at different positions are obtained.

[0098] (2) Based on numerical simulation or on-site monitoring, the wind speed time series, temperature time series, and humidity time series at different measurement points in the tobacco factory workshop are calculated to obtain their dimensionless normalized data results. and

[0099]

[0100] In the formula, v i (t m ), w i (t m ) and s i (t m ) are t m The instantaneous values ​​of wind speed, temperature and humidity obtained from numerical simulation or field test at measuring point i at time position, N is the number of time-series sampling at the measuring point.

[0101] (3) Calculate the dimensionless normalized wind speed statistical extreme values ​​at different measuring points in the tobacco factory workshop Temperature extremes and humidity statistical extremes

[0102]

[0103]

[0104]

[0105] In the formula, and are the dimensionless normalized wind speed, temperature and humidity averages at the measuring point i, respectively, where i = 1…n; n is the total number of measuring points; sign is the sign function; and are the peak factors corresponding to the dimensionless normalized wind speed, temperature and humidity time history data at the i measuring point, and are uniformly taken as 3.5; and are the dimensionless normalized wind speed, temperature and humidity pulsation values ​​at measuring point i respectively.

[0106] Among them, the dimensionless normalized wind speed average value at different measuring points in the tobacco factory workshop is Temperature average and humidity average Calculated by the following formula:

[0107]

[0108] Among them, the dimensionless normalized wind speed pulsation value at different measuring points in the tobacco factory workshop is Temperature pulsation value and humidity pulsation value Calculated by the following formula:

[0109]

[0110] (4) Using the k-means clustering algorithm, the dimensionless normalized wind speed statistics at different measurement points in the tobacco factory workshop were used to obtain the extreme values. Temperature extremes and humidity statistical extremes Realize the numerical size clustering area division of wind speed field, temperature field and humidity field in tobacco factory workshop vl , D wl and D sl , l is the calculated cluster area division number, corresponding to the wind speed field l=1...P, temperature field l=1...Q and humidity field l=1...R, where P, Q and R are the cluster partition numbers of the wind speed field, temperature field and humidity field respectively.

[0111] (5) Calculate the dimensionless normalized wind speed representative value in each cluster area of ​​the wind speed field, temperature field and humidity field of the tobacco factory workshop respectively Temperature representative value and humidity representative values

[0112]

[0113]

[0114] Where V i is the volume of the subordinate area of ​​measuring point i; and are the total volumes of the lth clustering area of ​​the wind speed field, temperature field and humidity field respectively; l n is the total number of measurement points in the lth cluster area.

[0115] (6) According to the wind speed field, temperature field and humidity field of the tobacco factory workshop, the dimensionless normalized wind speed representative values ​​of P, Q and R cluster areas are obtained. Temperature representative value and humidity representative values The wind speed field state description index C is calculated respectively v , Temperature field state description index C w and humidity field state description index C s :

[0116]

[0117] Among them, the dimensionless normalized average representative values ​​of wind speed in the P, Q and R cluster areas corresponding to the wind speed field, temperature field and humidity field of the tobacco factory workshop are Average temperature representative value and humidity average representative value Calculated by the following formula:

[0118]

[0119] (7) Based on wind speed field state description index C v , Temperature field state description index C w and humidity field state description index C s , combined with Table 1 to determine the environmental status of the workshop:

[0120] Table 1 Environmental status determination table

[0121]

[0122] According to Table 1:

[0123] When C v ≤0.1, C w ≤0.1 and C s When ≤0.1, the wind temperature and humidity environment of the tobacco factory workshop is level I (good);

[0124] When 0.1 <Cv ≤0.3, 0.1 <C w ≤0.3 and 0.1 <C s When ≤0.3, the wind temperature and humidity environment of the tobacco factory workshop is level II (general);

[0125] When 0.3 <C v ≤0.5, 0.3 <C w ≤0.5 and 0.3 <C s When ≤0.5, the wind temperature and humidity environment of the tobacco factory workshop is level III (poor);

[0126] When C v >0.5, C w >0.5 and C s When >0.5, the wind temperature and humidity environment in the tobacco factory workshop is level IV (extremely bad).

[0127] In view of the problem that there is no systematic method to establish the description of the wind temperature and humidity environment state of a large-span tobacco factory workshop and its characteristic type determination method, the present embodiment proposes to synchronize the wind speed time series, temperature time series and humidity time series based on the measurement points at different positions in the tobacco factory workshop, and process them into dimensionless normalized data results to obtain the wind speed statistical extreme value, temperature statistical extreme value and humidity statistical extreme value with a certain guarantee rate. On this basis, the k-means clustering algorithm is introduced to realize the clustering area division of the wind field, temperature field and humidity field of the tobacco factory workshop, and the dimensionless normalized wind speed representative value, temperature representative value and humidity representative value of each clustering area are calculated, and the wind field state description index, temperature field state description index and humidity field state description index of different clustering areas are defined and obtained. According to the value range of the above indexes, the description of the wind temperature and humidity environment state of the tobacco factory workshop and the division of characteristic types are realized, which lays a foundation for accurately and efficiently adjusting the working state of the air conditioning system and optimizing its layout.

[0128] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0129] Figure 3 : is a schematic diagram of the structure of a workshop environment state type determination device 30 provided in an embodiment of the present invention, the device comprising:

[0130] The acquisition module 31 is used to acquire environmental data of multiple measurement points in the workshop within a preset time; wherein the environmental data includes data of at least one environmental factor;

[0131] A statistical module 32 is used to determine, for each environmental factor, the dimensionless normalized statistical extreme value of the environmental factor at different measuring points according to the data of the environmental factor;

[0132] A clustering module 33 is used to cluster the dimensionless normalized statistical extreme values ​​of the environmental factor at different measuring points, and divide the workshop into multiple areas according to the clustering results;

[0133] A calculation module 34 is used to determine a representative value of the environmental factor in each area, and determine a state description index of the environmental factor according to the representative value of the environmental factor in each area;

[0134] The determination module 35 is used to determine the environmental status type of the workshop based on the status description indicators of various environmental factors.

[0135] As a possible implementation, the statistics module 32 is used to:

[0136] The data of the environmental factor are dimensionlessly normalized;

[0137] According to the data of the environmental factor, the average value and pulsation value of the environmental factor at different measuring points are calculated;

[0138] according to Determine the dimensionless normalized statistical extreme value of the environmental factor at different measuring points;

[0139] In the formula, x is the environmental factor, i is the location of the measurement point, is the dimensionless normalized statistical extreme value, is the average value, is the preset peak factor, is the pulsation value, and sign is the sign function.

[0140] As a possible implementation manner, the calculation module 34 is used to:

[0141] Obtain the volume of each area and the volume of the subordinate area of ​​each preset position measuring point;

[0142] The representative value of the environmental factor in each area is determined according to the dimensionless normalized statistical extreme value of the environmental factor, the volume of each area and the volume of the subordinate area of ​​each preset position measuring point.

[0143] As a possible implementation method, the formula for determining the representative value of the environmental factor for each area includes:

[0144]

[0145] In the formula, x is the environmental factor, i is the location measurement point, l is the cluster area, is a representative value, V i is the volume of the subordinate area of ​​the position measuring point i, is the volume of the lth cluster region, ln is the number of location measurement points in the lth cluster area, is the dimensionless normalized statistical extreme value.

[0146] As a possible implementation manner, the calculation module 34 is used to:

[0147] Calculate the average value of the representative value of the environmental factor in each area;

[0148] according to Determine the state description index of the environmental factor;

[0149] In the formula, x is the environmental factor, l is the area, C x is the state description indicator, M is the number of regions corresponding to the environmental factor, is the representative value of the environmental factor in the lth area, It is the average value of the representative value of the environmental factor in each area.

[0150] As a possible implementation manner, the environmental factor includes at least one of the following: wind speed, temperature and humidity.

[0151] As a possible implementation manner, the determination module 35 is used to:

[0152] Determine the state level of each environmental factor according to the state description indicator of each environmental factor and a preset grading table; wherein the grading table includes a reference range of the state description indicator corresponding to different state levels of at least one environmental factor;

[0153] Based on the status level of each environmental factor, the environmental status type of the workshop is determined.

[0154] The embodiment of the present invention obtains data of at least one environmental factor at multiple measuring points in a workshop within a certain period of time, and obtains dimensionless normalized statistical extreme values ​​with a certain guarantee rate through processing. On this basis, the dimensionless normalized statistical extreme values ​​of the environmental factor at different measuring points are clustered, and the workshop is divided into multiple areas according to the clustering results. The state description index of the environmental factor is determined by calculating the representative value of each area, and the description of the state of the environmental factor and the division of the characteristic type are realized, which provides a basis for accurately and efficiently adjusting the working state of the air-conditioning system and optimizing its layout.

[0155] Figure 4 FIG. 4 is a schematic diagram of an electronic device 40 provided by an embodiment of the present invention. Figure 4As shown, the electronic device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41, such as a workshop environment state type determination program. When the processor 41 executes the computer program 43, the steps in the above-mentioned workshop environment state type determination method embodiments are implemented, such as Figure 1 Alternatively, when the processor 41 executes the computer program 43, the functions of each module in the above-mentioned device embodiments are realized, for example, Figure 3 The functions of the modules 31 to 35 are shown.

[0156] Exemplarily, the computer program 43 may be divided into one or more modules / units, which are stored in the memory 42 and executed by the processor 41 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 43 in the electronic device 40.

[0157] The electronic device 40 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 40 and does not constitute a limitation of the electronic device 40. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 40 may also include input and output devices, network access devices, buses, etc.

[0158] The processor 41 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0159] The memory 42 may be an internal storage unit of the electronic device 40, such as a hard disk or memory of the electronic device 40. The memory 42 may also be an external storage device of the electronic device 40, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 40. Further, the memory 42 may also include both an internal storage unit of the electronic device 40 and an external storage device. The memory 42 is used to store the computer program and other programs and data required by the electronic device 40. The memory 42 may also be used to temporarily store data that has been output or is to be output.

[0160] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0161] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0163] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional unit in each embodiment of the present invention may be integrated into one 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.

[0166] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0167] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for determining the type of workshop environment status, characterized in that: include: Obtaining environmental data of multiple measurement points in a workshop within a preset time; wherein the environmental data includes data of at least one environmental factor; For each environmental factor, determining the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations according to the data of the environmental factor; Clustering the dimensionless normalized statistical extreme values ​​of the environmental factor at the different measuring points, and dividing the workshop into a plurality of areas according to the clustering results; Determine a representative value of the environmental factor for each area, and determine a state description index of the environmental factor according to the representative value of the environmental factor for each area; Based on the status description index of each environmental factor, the environmental status type of the workshop is determined.

2. The method for determining the type of workshop environment status according to claim 1, characterized in that: Determining the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations according to the data of the environmental factor includes: The data of the environmental factor are dimensionlessly normalized; According to the data of the environmental factor, the average value and the pulsation value of the environmental factor at the measuring points at different positions are calculated; according to Determining the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations; In the formula, x is the environmental factor, i is the location of the measurement point, is the dimensionless normalized statistical extreme value, is the average value, is the preset peak factor, is the pulsation value, and sign is the sign function.

3. The method for determining the type of workshop environment status according to claim 1, characterized in that: Determining the representative value of the environmental factor in each area includes: Obtain the volume of each area and the volume of the subordinate area of ​​each preset position measuring point; The representative value of the environmental factor in each area is determined according to the dimensionless normalized statistical extreme value of the environmental factor, the volume of each area and the volume of the subordinate area of ​​each preset position measuring point.

4. The method for determining the type of workshop environment state according to claim 3, characterized in that: The formula for determining the representative value of this environmental factor for each region includes: In the formula, x is the environmental factor, i is the location of the measurement point, l is the area, is a representative value, V i is the volume of the subordinate area of ​​the position measuring point i, is the volume of the lth region, l n is the number of position measurement points in the lth region, is the dimensionless normalized statistical extreme value.

5. The method for determining the type of workshop environment status according to claim 1, characterized in that: Determining the state description index of the environmental factor according to the representative value of the environmental factor in each area includes: Calculate the average value of the representative value of the environmental factor in each area; according to Determine the state description index of the environmental factor; In the formula, x is the environmental factor, l is the cluster area, C x is the state description indicator, M is the number of cluster regions corresponding to the environmental factor, is the representative value of the environmental factor in the lth cluster area, It is the average value of the representative value of the environmental factor in each area.

6. The method for determining the type of workshop environment status according to any one of claims 1 to 5, characterized in that: The environmental factors include at least one of the following: wind speed, temperature and humidity.

7. The method for determining the type of workshop environment status according to any one of claims 1 to 5, characterized in that: The determining of the environmental status type of the workshop based on the status description index of each environmental factor includes: Determine the state level of each environmental factor according to the state description indicator of each environmental factor and a preset grading table; wherein the grading table includes a reference range of the state description indicator corresponding to different state levels of the at least one environmental factor; Based on the status level of each environmental factor, the environmental status type of the workshop is determined.

8. A workshop environment status type determination device, characterized in that: include: An acquisition module, used to acquire environmental data of multiple measurement points in a workshop within a preset time; wherein the environmental data includes data of at least one environmental factor; A statistical module, for determining, for each environmental factor, the dimensionless normalized statistical extreme value of the environmental factor at the measuring points at different locations according to the data of the environmental factor; A clustering module, used for clustering the dimensionless normalized statistical extreme values ​​of the environmental factor at the different measuring points, and dividing the workshop into a plurality of areas according to the clustering results; A calculation module, used to determine a representative value of the environmental factor in each area, and determine a state description index of the environmental factor according to the representative value of the environmental factor in each area; The determination module is used to determine the environmental state type of the workshop based on the state description indicators of various environmental factors.

9. An electronic 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 according to any one of claims 1 to 7 are implemented.

10. 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 according to any one of claims 1 to 7 are implemented.

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