CN111928491A - Electric water heater intelligent detection system based on big data - Google Patents
Electric water heater intelligent detection system based on big data Download PDFInfo
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- CN111928491A CN111928491A CN202010853085.3A CN202010853085A CN111928491A CN 111928491 A CN111928491 A CN 111928491A CN 202010853085 A CN202010853085 A CN 202010853085A CN 111928491 A CN111928491 A CN 111928491A
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 307
- 238000001514 detection method Methods 0.000 title claims abstract description 54
- 238000010438 heat treatment Methods 0.000 claims abstract description 285
- 238000004458 analytical method Methods 0.000 claims abstract description 48
- 238000010521 absorption reaction Methods 0.000 claims abstract description 23
- 241000894006 Bacteria Species 0.000 claims abstract description 8
- 238000004140 cleaning Methods 0.000 claims description 43
- 230000001580 bacterial effect Effects 0.000 claims description 33
- 238000012545 processing Methods 0.000 claims description 25
- 238000000034 method Methods 0.000 claims description 16
- 241000894007 species Species 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 5
- 241000193738 Bacillus anthracis Species 0.000 claims description 3
- 241000193755 Bacillus cereus Species 0.000 claims description 3
- 241000222122 Candida albicans Species 0.000 claims description 3
- 241000588724 Escherichia coli Species 0.000 claims description 3
- 241000191967 Staphylococcus aureus Species 0.000 claims description 3
- 229940065181 bacillus anthracis Drugs 0.000 claims description 3
- 229940095731 candida albicans Drugs 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000000630 rising effect Effects 0.000 claims description 2
- 230000001186 cumulative effect Effects 0.000 claims 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 5
- 238000003860 storage Methods 0.000 description 2
- 208000003251 Pruritus Diseases 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000017525 heat dissipation Effects 0.000 description 1
- 229910052749 magnesium Inorganic materials 0.000 description 1
- 239000011777 magnesium Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004181 pedogenesis Methods 0.000 description 1
- 230000001012 protector Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 208000017520 skin disease Diseases 0.000 description 1
- 230000005808 skin problem Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24H—FLUID HEATERS, e.g. WATER OR AIR HEATERS, HAVING HEAT-GENERATING MEANS, e.g. HEAT PUMPS, IN GENERAL
- F24H9/00—Details
- F24H9/20—Arrangement or mounting of control or safety devices
- F24H9/2007—Arrangement or mounting of control or safety devices for water heaters
- F24H9/2014—Arrangement or mounting of control or safety devices for water heaters using electrical energy supply
- F24H9/2021—Storage heaters
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- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Heat-Pump Type And Storage Water Heaters (AREA)
Abstract
The invention discloses an intelligent detection system of an electric water heater based on big data, which comprises a temperature detection unit, a timing trigger unit, a heat absorption change analysis unit, a dirt rate estimation unit, a heating device performance analysis unit and a prompt early warning unit, wherein the accumulated heating time required by heating the same temperature value is collected, the dirt quality change rate generated in the heating process of a water tank is counted by combining the dirt rate estimation unit, the dirt quality generated in the water tank and the corresponding danger degree are counted according to the dirt quality change rate, and bacteria and water scale in the water tank are comprehensively analyzed to determine whether the water tank needs to be cleaned. The invention can accurately judge the total mass of the dirt generated in the water tank, measure the danger degree caused by the continuous heating of the dirt on the electric water heater according to the total mass of the dirt, accurately judge whether the water quality in the water tank needs to be cleaned and replaced, realize the early warning and reminding for users in time, and greatly improve the safety and the sanitation degree of water.
Description
Technical Field
The invention belongs to the technical field of electric water heaters, and particularly relates to an intelligent detection system of an electric water heater based on big data.
Background
The electric water heater is a water heater which heats by taking electricity as energy. The household water storage type electric water heater is convenient to install, low in price, long in heating time and capable of being used after reaching a certain temperature.
The magnesium rod is arranged in the water storage type electric water heater, the water in the water heater cannot be softened along with the overlong service time of the magnesium rod, accumulated scale is attached to the heating pipe, the local heat dissipation of the heating pipe is poor, even the heating pipe is burnt, electric leakage can occur when the heating pipe is burnt, once an electric leakage protector of the water heater does not work, casualties can be caused, the heating speed is reduced along with the accumulation of the scale, the power consumption is increased, and the water heat exchange is poor The old man or the patient that has skin disease itself can cause phenomenons such as skin itch, the red spot that rises, and prior art can't detect the incrustation scale content in the water tank, can't judge the dangerous degree that the continuation heating exists and can't judge the water quality safety nature in the water tank according to the incrustation scale content.
Disclosure of Invention
The invention aims to provide an intelligent detection system of an electric water heater based on big data, which solves the problems that the dirt amount in a water tank cannot be detected, the danger degree of continuous heating of the electric water heater cannot be judged according to the dirt amount, the water quality in the water tank of the electric water heater cannot be judged, and the like in the prior art.
The purpose of the invention can be realized by the following technical scheme:
an intelligent detection system of an electric water heater based on big data comprises a temperature detection unit, a timing trigger unit, a heat absorption change analysis unit, a dirt rate estimation unit, a heating device performance analysis unit and a prompt early warning unit;
the heat absorption change analysis unit is respectively connected with the temperature detection unit, the timing trigger unit and the fouling rate estimation unit, and the heating device performance analysis unit is respectively connected with the fouling rate estimation unit, the cleaning correlation processing unit and the prompt early warning unit;
the temperature detection unit is arranged in a water tank of the electric water heater and used for detecting the water temperature in the water tank in real time and sending the detected water temperature in the water tank to the heat absorption change analysis unit;
the heat absorption change analysis unit is used for collecting the temperature in the water tank detected by the temperature detection unit at equal intervals T, triggering the timing trigger unit to time when the temperature in the water tank reaches a first-stage temperature value, triggering the timing trigger unit to stop timing until the temperature in the water tank reaches a second-stage temperature value, counting the heating time length required by the temperature in the water tank from the first-stage temperature to the second-stage temperature value, extracting the heating power of the electric water heater in the process of enabling the water temperature in the water tank to reach the second-stage temperature value from the first-stage temperature, classifying the heating time length required by the water temperature in the electric water heater from the first-stage temperature to the second-stage temperature value under the same heating power, and sending the classified heating accumulated time length set to the fouling rate estimation unit;
the timing trigger unit is connected with the heat absorption change analysis unit and used for receiving a trigger timing starting instruction and a trigger timing stopping instruction sent by the heat absorption change analysis unit and counting the heating time length required in the process of the temperature in the water tank from the first-level temperature value to the second-level temperature value;
the fouling rate estimation unit is connected with the heat absorption change analysis unit and is used for acquiring the heating accumulated time length required by the electric water heater in the process of increasing the water temperature from the first-stage temperature to the second-stage temperature under the same heating power, comparing the last detected heating accumulated time length under the same heating power with the next detected heating accumulated time length, and obtaining the variation delta w of the relative heating time lengthPEk＝wPE(k+1)-wPEk，ΔwPEk is the difference value between the heating accumulated time length detected at the k +1 th time and the heating accumulated time length detected at the k th time under the PE heating power, the standard heating accumulated time length required for raising the water temperature from the first-level temperature to the second-level temperature under the PE heating power is screened out, the variation of the relative heating time length and the heating accumulated time length required for raising the water temperature from the first-level temperature to the second-level temperature are substituted into a fouling evolution rate model to obtain the fouling quality variation rate generated in the current water tank heating process, and the generated fouling quality variation rate is sent to a heating device performance analysis unit;
the heating device performance analysis unit is used for receiving the dirt quality change rate sent by the dirt rate estimation unit, screening out a detection number f when the variation of the relative heating time is larger than a set relative heating time variation threshold, wherein f is smaller than m, sequentially extracting the dirt quality change rate when the variation of the relative heating time is larger than the relative heating time variation threshold, counting the dirt quality generated in the water tank in each interval acquisition time period according to the dirt quality change rate, counting the accumulated dirt quality sum in the water tank according to the dirt quality generated in each interval acquisition time period, judging the heating performance danger coefficient in the continuous heating process of the electric water heater according to the accumulated dirt quality sum in the water tank, and sending the heating performance danger coefficient in the continuous heating process of the electric water heater to the prompt early warning unit;
the prompting early warning unit is used for receiving a heating performance danger coefficient of the electric water heater sent by the heating device performance analysis unit in the continuous heating process and displaying the heating performance danger coefficient, once the heating performance danger coefficient is larger than the upper limit of a heating performance safety coefficient threshold value, a user is prompted to stop heating the electric water heater, the cleaning replacement coefficient of water quality in the electric water heater sent by the cleaning associated processing unit is received and displayed, and if the cleaning replacement coefficient of water quality is larger than the cleaning replacement coefficient threshold value of water quality, the user is prompted to discharge water accumulated in the water tank and clean water scales in the water tank.
Further, the soil evolution rate model is
VkThe mass change rate of dirt generated in the heating process of the water tank during the kth detection, w 'is the average relative heating time length change quantity corresponding to the heating pipe in the water tank after a fixed interval time t', wPE StandardCumulative time of standard heating required for the water temperature to rise from the primary temperature to the secondary temperature under PE heating power, wPEk is the accumulated time corresponding to the value of the kth detected temperature in the water tank from the first-level temperature to the second-level temperature when the electric water heater uses the PE heating power, Gw″The water temperature rises from the first-level temperature to the second temperature after a fixed interval time t ″The dirt quality generated by the average relative heating time variation w' corresponding to the stage temperature is determined, T is an interval acquisition time period, namely the time interval between the (k + 1) th detection and the kth detection, and e is a natural number.
Further, the dirt quality formula generated in each interval acquisition time period is
Mk,k+1The dirt quality of the water tank generated in the k +1 detection process from the kth detection is represented, namely the dirt quality of the water tank generated in the kth interval acquisition time period, and T is the time length corresponding to the interval acquisition time period.
Further, the sum of the accumulated dirt mass in the water tank
And n is the total number of times of collection.
Further, the heating device performance analysis unit analyzes the danger degree of the electric water heater in the heating process, and comprises the following steps:
v1, calculation formula by using fouling mass
Counting the quality of dirt generated in each interval acquisition time period;
v2, counting the total mass of the accumulated dirt in each interval acquisition time period;
v3, comparing the total mass of the scale with the average scale mass in the scale mass range corresponding to the first-level heating danger level to obtain a scale ratio, wherein the average scale mass is equal to half of the sum of the upper limit value and the lower limit value in the scale mass range corresponding to the first-level heating danger level, each-level heating danger level has only one heating performance danger coefficient corresponding to the upper limit value and the lower limit value, the heating performance danger coefficients corresponding to the first-level heating danger level, the second-level heating danger level, the third-level heating danger level and the fourth-level heating danger level are gradually increased, and the higher the heating performance danger coefficient is, the greater the danger of the electric water heater for continuously heating the scale to the safe use of the electric;
v4, judging whether the dirt ratio is smaller than 1, if so, determining that the heating performance risk coefficient corresponding to the continuous heating process of the current electric heater is the heating performance risk coefficient corresponding to the first-level heating risk level, and if so, executing the step V5;
v5, judging whether the dirt ratio is smaller than 2 and larger than 1, if the dirt ratio is smaller than 2 and larger than 1, determining that the heating performance risk coefficient corresponding to the continuous heating process of the current electric heater is the heating performance risk coefficient corresponding to the secondary heating risk level, and if the dirt ratio is larger than 2, executing a step V6;
v6, comparing the total mass of the dirt with the average mass of the dirt in the mass range of the dirt corresponding to the three-level heating danger level to obtain a dirt ratio, judging whether the dirt ratio is smaller than 1, if so, determining that the heating performance danger coefficient corresponding to the current continuous heating process of the electric heater is the heating performance danger coefficient corresponding to the three-level heating danger level, and if so, determining that the heating performance danger coefficient corresponding to the current continuous heating process of the electric heater is the heating performance danger coefficient corresponding to the four-level heating danger level.
The device further comprises a water quality environment detection unit and a cleaning associated processing unit, wherein the cleaning associated processing unit is respectively connected with the water quality environment detection unit and the heating device performance analysis unit;
the water quality environment detection unit is arranged in a water tank of the electric water heater and is used for collecting time periods T at equal intervals to detect the content of each bacterial species in the water tank successively and sending the detected content of each bacterial species to the cleaning correlation processing unit;
the cleaning correlation processing unit is used for receiving the content of each bacterial species sent by the water quality environment detection unit, comparing the content of each bacterial species with a bacterial content threshold value corresponding to the bacterial species, extracting the total mass of dirt in the electric water heater, comprehensively analyzing the water quality cleaning and replacing coefficient in the electric water heater according to the water quality pollution coefficient corresponding to the dirt of unit mass in the electric water heater, wherein the water quality cleaning and replacing coefficient is 0 & lt 1, and the water quality cleaning and replacing coefficient in the electric water heater obtained by the processing of the cleaning correlation processing unit is sent to the prompt early warning unit.
Further, the calculation formula of the water quality cleaning and replacing coefficient in the electric water heater is as follows
D is a water quality cleaning and replacing coefficient of the electric water heater, beta is an influence weight proportion coefficient of water scales on water quality in the water tank, and is a water quality pollution coefficient corresponding to dirt with unit mass m, Q is the total mass of the dirt in the electric water heater, m is the unit mass of the dirt, 1-beta is an influence weight proportion coefficient of bacteria on the water quality in the water tank, j is 1,2,3,4 and 5, and is respectively escherichia coli, staphylococcus aureus, candida albicans, bacillus anthracis and bacillus cereus, eta isjExpressed as the bacterial interference hazard factor, η, corresponding to the jth bacterial species1+η2+η3+η4+η5＝1，X″jExpressed as the threshold value of the bacterial content, X, corresponding to the jth bacterial speciesjExpressed as the bacterial content of the jth bacterial species.
The invention has the beneficial effects that:
according to the invention, the heating accumulated time length of the water temperature in the electric water heater from the first-stage temperature to the second-stage temperature is detected, the heating accumulated time lengths detected at two adjacent times under the same power are compared to obtain the variation of the relative heating time length, the variation of the relative heating time length under each processing power and the dirt evolution rate of the heating accumulated time length are integrated into the dirt evolution rate module to count the dirt quality change rate generated in the heating process of the water tank, the variation rate of the dirt quality in each interval acquisition time period can be obtained by a series of analysis such as judging the heating accumulated time length according to the same temperature increasing value, the accuracy of detecting the dirt quality change rate under different powers is improved, and the dirt quality generated in the water tank is conveniently counted in the later period along with the time.
The invention screens the change rate of the dirt quality through the performance analysis unit of the heating device so as to keep the change rate of the dirt quality which is larger than the set threshold value of the change amount of the relative heating time, reduce the workload of data processing, obtain the dirt quality generated in each interval acquisition time period and the total accumulated dirt mass in the water tank through a dirt quality solving formula, further analyze the heating performance danger coefficient of the heating pipe under the influence of the current water scale according to the total dirt mass, judge the danger degree of continuous heating of the heating pipe and realize the quantification of the danger influence degree of the water scale on the use process of the electric water heater.
The invention collects the content of each bacterial species in the water tank and extracts the water quality pollution coefficient corresponding to the dirt of unit mass, and simultaneously analyzes the water quality replacement coefficient in the electric water heater by combining the total mass sum of the dirt in the water tank of the electric water heater, and the collection prompting early warning unit carries out threshold judgment on the water quality replacement coefficient and the heating performance danger coefficient so as to send out early warning prompt in time, thereby facilitating the user to carry out different degrees of treatment on the magnesium rod, the dirt and the water in the water tank according to the requirements of the user on the safety of the electric water heater and the water quality safety.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An intelligent detection system of an electric water heater based on big data comprises a temperature detection unit, a water quality environment detection unit, a timing trigger unit, a heat absorption change analysis unit, a dirt rate estimation unit, a heating device performance analysis unit, a cleaning correlation processing unit and a prompt early warning unit.
The heat absorption change analysis unit is respectively connected with the temperature detection unit, the timing trigger unit and the dirt rate estimation unit, the heating device performance analysis unit is respectively connected with the dirt rate estimation unit, the cleaning correlation processing unit and the prompt early warning unit, and the cleaning correlation processing unit is respectively connected with the water quality environment detection unit and the prompt early warning unit.
The temperature detection unit is a temperature detector, is arranged in a water tank of the electric water heater, is positioned at the upper part of a middle water tank of the electric water heater, and is used for detecting the water temperature in the water tank in real time and sending the detected water temperature in the water tank to the heat absorption change analysis unit;
the water quality environment detection unit is a bacteria detector, is arranged in a water tank of the electric water heater and is used for collecting time periods T at equal intervals to detect the content of each bacteria type in the water tank successively and sending the detected content of each bacteria type to the cleaning correlation processing unit;
the heat absorption change analysis unit is used for collecting the temperature in the water tank detected by the temperature detection unit at equal intervals T, triggering the timing trigger unit to time when the temperature in the water tank reaches a first-level temperature value, triggering the timing trigger unit to stop timing until the temperature in the water tank reaches a second-level temperature value, counting the heating time required by the temperature in the water tank from the first-level temperature to the second-level temperature value, extracting the heating power of the electric water heater in the process of enabling the water temperature in the water tank to reach the second-level temperature value from the first-level temperature, classifying the heating time required by the water temperature in the electric water heater from the first-level temperature to the second-level temperature value under the same heating power, and forming a heating accumulated time set W according to the detected sequence of temperature variation under the same powerPE＝{wPE1,wPE2,...,wPEk,...,wPEy, PE P1, P2, P3, P1, P2, P3 are 800W, 1200W and 2000W power, WPEk is the accumulated time corresponding to the numerical value of the temperature from the first-stage temperature to the second-stage temperature in the water tank when the electric water heater works with the power PE, and the heat absorption change analysis unit sends the collection of the heating accumulated time required in the process of increasing the water temperature from the first-stage temperature to the second-stage temperature to the fouling rate estimation unit for the electric water heater under the same heating power.
The electric water heater is at the same heating power and according to a specific heat capacity formula
The specific heat capacity C of the water is fixed, and when the heat quantity Q is constant, the unit mass of the waterThe temperature variation delta t is the same, the water in the water tank is heated by the electric water heater with the same heating power, the heating temperature variation is the same, the corresponding heating time duration is the same, namely the electric energy is converted into heat energy, but the heating speed is slow due to the scale attached to the surface of the heating pipe in the electric water heater, the water heat exchange is poor, and the time required for the water in the water tank to rise to the same temperature variation is gradually increased along with the increase of the scale.
The timing trigger unit is connected with the heat absorption change analysis unit and used for receiving a trigger start timing instruction and a trigger stop timing instruction sent by the heat absorption change analysis unit, counting the heating time required in the process of the temperature in the water tank from a first-stage temperature value to a second-stage temperature value, wherein the second-stage temperature value is greater than the first-stage temperature value, and the first-stage temperature value and the second-stage temperature value are both greater than 30 ℃.
The fouling rate estimation unit is connected with the heat absorption change analysis unit and is used for acquiring the heating accumulated time length required by the electric water heater in the process of increasing the water temperature from the first-stage temperature to the second-stage temperature under the same heating power, comparing the last detected heating accumulated time length under the same heating power with the next detected heating accumulated time length, and obtaining the variation delta w of the relative heating time lengthPEk＝wPE(k+1)-wPEk，ΔwPEk is the difference value between the heating accumulated time length detected for the k +1 th time and the heating accumulated time length detected for the k time under the PE heating power, the standard heating accumulated time length required for raising the water temperature from the first-level temperature to the second-level temperature under the PE heating power is screened out, the variation of the relative heating time length and the heating accumulated time length required for raising the water temperature from the first-level temperature to the second-level temperature are substituted into a dirt evolution rate model to obtain the dirt quality variation rate generated in the current water tank heating process, and the generated dirt quality variation rate is sent to a heating device performance analysis unit, wherein the dirt evolution rate model is a dirt evolution rate model
VkThe change rate of the quality of dirt generated in the heating process of the water tank at the k-th detection is w ″After a fixed interval of time t' the average relative heating time variation, w, corresponding to the heating pipe in the water tankPE StandardCumulative time of standard heating required for the water temperature to rise from the primary temperature to the secondary temperature under PE heating power, wPEk is the accumulated time corresponding to the value of the kth detected temperature in the water tank from the first-level temperature to the second-level temperature when the electric water heater uses the PE heating power, Gw″The mass of the dirt generated by the variation w 'of the average relative heating time corresponding to the temperature of the water rising from the first-level temperature to the second-level temperature after the fixed interval time T', T is the interval acquisition time period, namely the time interval between the k +1 th detection and the k-th detection, and e is a natural number.
The heating device performance analysis unit is used for receiving the dirt quality change rate sent by the dirt rate estimation unit, screening out the detection times number f with the relative heating time length variation larger than the set relative heating time length variation threshold, wherein f is smaller than m, for being smaller than the relative heating time length variation threshold, the scale amount on the surface of the heating pipe in the water tank is small, the influence degree on heating of the heating pipe is small, in order to reduce the complexity of experimental data processing, the relative heating time length variation smaller than the relative heating time length variation threshold is not processed, the dirt quality change rate with the relative heating time length variation larger than the relative heating time length variation threshold is sequentially extracted, and the dirt quality change rate generated by the dirt in the water tank in each interval acquisition time period is counted according to the dirt quality change rate
Mk,k+1The method comprises the steps of representing the quality of dirt generated in the process that a water tank is detected from the kth time to the (k + 1) th time, namely the quality of the dirt generated by the water tank in the kth interval acquisition time period, wherein T is the duration corresponding to the interval acquisition time period, and counting the accumulated quality of the dirt in the water tank according to the quality of the dirt generated in each interval acquisition time periodSum of quantities
And n is the total number of times of collection, the heating performance danger coefficient of the electric water heater in the continuous heating process is judged according to the accumulated dirt mass sum in the water tank, and the heating performance danger coefficient of the electric water heater in the continuous heating process is sent to the prompt early warning unit.
Magnesium stick live time overlength in the water heater can lead to the water in the water heater not to get the softening, and the incrustation scale of gathering is attached to on the heating pipe, along with incrustation scale adnexed quality constantly increases on the heating pipe, can lead to the heating pipe part to be heated badly, burns out the heating pipe even, when the heating pipe burns out, can take place the electric leakage, simultaneously, because the gathering of incrustation scale can lead to the rate of heating to obviously reduce, increase power consumption.
Can carry out the analysis to the dirt production volume in each interval collection time quantum through heating device performance analysis unit to judge the total quality of dirt of gathering in the heating tube, and then the heating performance danger coefficient of analysis heating tube under the influence of current incrustation scale can be judged the dangerous degree that the heating tube lasts the heating, realizes that the incrustation scale quantifies the influence degree in the water heater use.
The method comprises the following steps of:
v1, calculation formula by using fouling mass
Counting the quality of dirt generated in each interval acquisition time period;
v2, counting the total mass of the accumulated dirt in each interval acquisition time period;
v3, comparing the total mass of the scale with the average scale mass in the scale mass range corresponding to the first-level heating danger level to obtain a scale ratio, wherein the average scale mass is equal to half of the sum of the upper limit value and the lower limit value in the scale mass range corresponding to the first-level heating danger level, each-level heating danger level has only one heating performance danger coefficient corresponding to the upper limit value and the lower limit value, the heating performance danger coefficients corresponding to the first-level heating danger level, the second-level heating danger level, the third-level heating danger level and the fourth-level heating danger level are gradually increased, and the higher the heating performance danger coefficient is, the greater the danger of the electric water heater for continuously heating the scale to the safe use of the electric;
v4, judging whether the dirt ratio is smaller than 1, if so, determining that the heating performance risk coefficient corresponding to the continuous heating process of the current electric heater is the heating performance risk coefficient corresponding to the first-level heating risk level, and if so, executing the step V5;
v5, judging whether the dirt ratio is smaller than 2 and larger than 1, if the dirt ratio is smaller than 2 and larger than 1, determining that the heating performance risk coefficient corresponding to the continuous heating process of the current electric heater is the heating performance risk coefficient corresponding to the secondary heating risk level, and if the dirt ratio is larger than 2, executing a step V6;
v6, comparing the total mass of the dirt with the average mass of the dirt in the mass range of the dirt corresponding to the three-level heating danger level to obtain a dirt ratio, judging whether the dirt ratio is smaller than 1, if so, determining that the heating performance danger coefficient corresponding to the current continuous heating process of the electric heater is the heating performance danger coefficient corresponding to the three-level heating danger level, and if so, determining that the heating performance danger coefficient corresponding to the current continuous heating process of the electric heater is the heating performance danger coefficient corresponding to the four-level heating danger level.
The total mass of the dirt in the electric water heater is compared with the average mass of the scale corresponding to each heating danger level respectively to determine the heating danger level corresponding to the electric water heater step by step, so that the danger degree of the electric water heater for continuous heating can be accurately screened out, the danger degree can be quantized, and a user can conveniently and visually know the damage degree of the scale in the electric water heater to a heating pipe.
The cleaning correlation processing unit is used for receiving the content of each bacterial species sent by the water quality environment detection unit, comparing the content of each bacterial species with a bacterial content threshold value corresponding to the bacterial species, extracting the total mass of dirt in the electric water heater, and obtaining the unit mass of the electric water heaterThe water quality pollution coefficient corresponding to the dirt is less than 0 and less than 1, and the water quality cleaning and replacing coefficient in the electric water heater is comprehensively analyzed through the content of various bacteria types
D is a water quality cleaning and replacing coefficient of the electric water heater, beta is an influence weight proportion coefficient of water scales on water quality in the water tank, and is a water quality pollution coefficient corresponding to dirt with unit mass m, Q is the total mass of the dirt in the electric water heater, m is the unit mass of the dirt, 1-beta is an influence weight proportion coefficient of bacteria on the water quality in the water tank, j is 1,2,3,4 and 5, and is respectively escherichia coli, staphylococcus aureus, candida albicans, bacillus anthracis and bacillus cereus, eta isjExpressed as the bacterial interference hazard factor, η, corresponding to the jth bacterial species1+η2+η3+η4+η5＝1，X″jExpressed as the threshold value of the bacterial content, X, corresponding to the jth bacterial speciesjThe water quality cleaning and replacing coefficient is sent to the prompting and early warning unit, is in direct proportion to the emergency degree of water replacement in the water tank, and the larger the water quality cleaning and replacing coefficient is, the larger the requirement that the water in the water tank needs to be replaced is.
The prompting and early warning unit is used for receiving and displaying the heating performance danger coefficient of the electric water heater in the continuous heating process sent by the heating device performance analysis unit, prompting a user to stop heating the electric water heater once the heating performance danger coefficient is larger than the upper limit of the heating performance safety coefficient threshold, so as to facilitate users to replace the magnesium rod and clean scale in the water tank in time, effectively avoid the loss of the performance of the electric water heater caused by the scale attached on the heating pipe and even harm the health and the life of the users, and receives and displays the water quality cleaning and replacing coefficient sent by the cleaning and associating processing unit in the electric water heater, if the water quality cleaning and replacing coefficient is larger than the water quality cleaning and replacing coefficient threshold value, the user is prompted to discharge the water accumulated in the water tank and clean the scale in the water tank, so that the skin problem and the like caused by the polluted water in the water tank can be avoided.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (7)
1. The utility model provides an electric water heater intelligent detection system based on big data which characterized in that: the device comprises a temperature detection unit, a timing trigger unit, a heat absorption change analysis unit, a dirt rate estimation unit, a heating device performance analysis unit and a prompt early warning unit;
the heat absorption change analysis unit is respectively connected with the temperature detection unit, the timing trigger unit and the fouling rate estimation unit, and the heating device performance analysis unit is respectively connected with the fouling rate estimation unit, the cleaning correlation processing unit and the prompt early warning unit;
the temperature detection unit is arranged in a water tank of the electric water heater and used for detecting the water temperature in the water tank in real time and sending the detected water temperature in the water tank to the heat absorption change analysis unit;
the heat absorption change analysis unit is used for collecting the temperature in the water tank detected by the temperature detection unit at equal intervals T, triggering the timing trigger unit to time when the temperature in the water tank reaches a first-stage temperature value, triggering the timing trigger unit to stop timing until the temperature in the water tank reaches a second-stage temperature value, counting the heating time length required by the temperature in the water tank from the first-stage temperature to the second-stage temperature value, extracting the heating power of the electric water heater in the process of enabling the water temperature in the water tank to reach the second-stage temperature value from the first-stage temperature, classifying the heating time length required by the water temperature in the electric water heater from the first-stage temperature to the second-stage temperature value under the same heating power, and sending the classified heating accumulated time length set to the fouling rate estimation unit;
the timing trigger unit is connected with the heat absorption change analysis unit and used for receiving a trigger timing starting instruction and a trigger timing stopping instruction sent by the heat absorption change analysis unit and counting the heating time length required in the process of the temperature in the water tank from the first-level temperature value to the second-level temperature value;
the fouling rate estimation unit is connected with the heat absorption change analysis unit and is used for acquiring the heating accumulated time length required by the electric water heater in the process of increasing the water temperature from the first-stage temperature to the second-stage temperature under the same heating power, comparing the last detected heating accumulated time length under the same heating power with the next detected heating accumulated time length, and obtaining the variation delta w of the relative heating time lengthPEk＝wPE(k+1)-wPEk，ΔwPEk is the difference value between the heating accumulated time length detected at the k +1 th time and the heating accumulated time length detected at the k th time under the PE heating power, the standard heating accumulated time length required for raising the water temperature from the first-level temperature to the second-level temperature under the PE heating power is screened out, the variation of the relative heating time length and the heating accumulated time length required for raising the water temperature from the first-level temperature to the second-level temperature are substituted into a fouling evolution rate model to obtain the fouling quality variation rate generated in the current water tank heating process, and the generated fouling quality variation rate is sent to a heating device performance analysis unit;
the heating device performance analysis unit is used for receiving the dirt quality change rate sent by the dirt rate estimation unit, screening out a detection number f when the variation of the relative heating time is larger than a set relative heating time variation threshold, wherein f is smaller than m, sequentially extracting the dirt quality change rate when the variation of the relative heating time is larger than the relative heating time variation threshold, counting the dirt quality generated in the water tank in each interval acquisition time period according to the dirt quality change rate, counting the accumulated dirt quality sum in the water tank according to the dirt quality generated in each interval acquisition time period, judging the heating performance danger coefficient in the continuous heating process of the electric water heater according to the accumulated dirt quality sum in the water tank, and sending the heating performance danger coefficient in the continuous heating process of the electric water heater to the prompt early warning unit;
the prompting early warning unit is used for receiving a heating performance danger coefficient of the electric water heater sent by the heating device performance analysis unit in the continuous heating process and displaying the heating performance danger coefficient, once the heating performance danger coefficient is larger than the upper limit of a heating performance safety coefficient threshold value, a user is prompted to stop heating the electric water heater, the cleaning replacement coefficient of water quality in the electric water heater sent by the cleaning associated processing unit is received and displayed, and if the cleaning replacement coefficient of water quality is larger than the cleaning replacement coefficient threshold value of water quality, the user is prompted to discharge water accumulated in the water tank and clean water scales in the water tank.
2. The intelligent detection system for the big data-based electric water heater according to claim 1, characterized in that: the fouling evolution rate model is
VkThe mass change rate of dirt generated in the heating process of the water tank during the kth detection, w 'is the average relative heating time length change quantity corresponding to the heating pipe in the water tank after a fixed interval time t', wPE StandardCumulative time of standard heating required for the water temperature to rise from the primary temperature to the secondary temperature under PE heating power, wPEk is the accumulated time corresponding to the value of the kth detected temperature in the water tank from the first-level temperature to the second-level temperature when the electric water heater uses the PE heating power, Gw″The dirt quality is generated by the average relative heating time length variation w 'corresponding to the temperature of the water rising from the first-stage temperature to the second-stage temperature after a fixed interval time T', T is an interval acquisition time period, namely the time interval between the (k + 1) th detection and the kth detection, and e is a natural number.
3. The intelligent detection system for the big data-based electric water heater according to claim 1, characterized in that: the dirt quality formula generated in each interval acquisition time period is
Mk,k+1Indicating the quality of the dirt generated in the process of the water tank from the k +1 detection,namely the quality of dirt generated by the water tank in the kth interval acquisition time period, and T is the time length corresponding to the interval acquisition time period.
4. The intelligent detection system for the big data-based electric water heater according to claim 3, wherein: cumulative total mass of dirt in water tank
And n is the total number of times of collection.
5. The intelligent detection system for the big data-based electric water heater according to claim 4, wherein: the heating device performance analysis unit analyzes the danger degree of the electric water heater in the heating process, and comprises the following steps:
v1, calculation formula by using fouling mass
Counting the quality of dirt generated in each interval acquisition time period;
v2, counting the total mass of the accumulated dirt in each interval acquisition time period;
v3, comparing the total mass of the scale with the average scale mass in the scale mass range corresponding to the first-level heating danger level to obtain a scale ratio, wherein the average scale mass is equal to half of the sum of the upper limit value and the lower limit value in the scale mass range corresponding to the first-level heating danger level, each-level heating danger level has only one heating performance danger coefficient corresponding to the upper limit value and the lower limit value, the heating performance danger coefficients corresponding to the first-level heating danger level, the second-level heating danger level, the third-level heating danger level and the fourth-level heating danger level are gradually increased, and the higher the heating performance danger coefficient is, the greater the danger of the electric water heater for continuously heating the scale to the safe use of the electric;
v4, judging whether the dirt ratio is smaller than 1, if so, determining that the heating performance risk coefficient corresponding to the continuous heating process of the current electric heater is the heating performance risk coefficient corresponding to the first-level heating risk level, and if so, executing the step V5;
v5, judging whether the dirt ratio is smaller than 2 and larger than 1, if the dirt ratio is smaller than 2 and larger than 1, determining that the heating performance risk coefficient corresponding to the continuous heating process of the current electric heater is the heating performance risk coefficient corresponding to the secondary heating risk level, and if the dirt ratio is larger than 2, executing a step V6;
v6, comparing the total mass of the dirt with the average mass of the dirt in the mass range of the dirt corresponding to the three-level heating danger level to obtain a dirt ratio, judging whether the dirt ratio is smaller than 1, if so, determining that the heating performance danger coefficient corresponding to the current continuous heating process of the electric heater is the heating performance danger coefficient corresponding to the three-level heating danger level, and if so, determining that the heating performance danger coefficient corresponding to the current continuous heating process of the electric heater is the heating performance danger coefficient corresponding to the four-level heating danger level.
6. The intelligent detection system for the big data-based electric water heater according to claim 5, wherein: the device also comprises a water quality environment detection unit and a cleaning associated processing unit, wherein the cleaning associated processing unit is respectively connected with the water quality environment detection unit and the heating device performance analysis unit;
the water quality environment detection unit is arranged in a water tank of the electric water heater and is used for collecting time periods T at equal intervals to detect the content of each bacterial species in the water tank successively and sending the detected content of each bacterial species to the cleaning correlation processing unit;
the cleaning correlation processing unit is used for receiving the content of each bacterial species sent by the water quality environment detection unit, comparing the content of each bacterial species with a bacterial content threshold value corresponding to the bacterial species, extracting the total mass of dirt in the electric water heater, comprehensively analyzing the water quality cleaning and replacing coefficient in the electric water heater according to the water quality pollution coefficient corresponding to the dirt of unit mass in the electric water heater, wherein the water quality cleaning and replacing coefficient is 0 & lt 1, and the water quality cleaning and replacing coefficient in the electric water heater obtained by the processing of the cleaning correlation processing unit is sent to the prompt early warning unit.
7. The intelligent detection system for the big data-based electric water heater according to claim 6, wherein: the calculation formula of the water quality cleaning and replacing coefficient in the electric water heater is
D is a water quality cleaning and replacing coefficient of the electric water heater, beta is an influence weight proportion coefficient of water scales on water quality in the water tank, and is a water quality pollution coefficient corresponding to dirt with unit mass m, Q is the total mass of the dirt in the electric water heater, m is the unit mass of the dirt, 1-beta is an influence weight proportion coefficient of bacteria on the water quality in the water tank, j is 1,2,3,4 and 5, and is respectively escherichia coli, staphylococcus aureus, candida albicans, bacillus anthracis and bacillus cereus, eta isjExpressed as the bacterial interference hazard factor, η, corresponding to the jth bacterial species1+η2+η3+η4+η5＝1，X″jExpressed as the threshold value of the bacterial content, X, corresponding to the jth bacterial speciesjExpressed as the bacterial content of the jth bacterial species.
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